1 00:00:04,630 --> 00:00:02,470 hello everyone it's a great pleasure to 2 00:00:06,789 --> 00:00:04,640 have you here all we had a very exciting 3 00:00:08,710 --> 00:00:06,799 session i think uh online on auto 4 00:00:11,430 --> 00:00:08,720 catalysis from motocades to evolution 5 00:00:13,350 --> 00:00:11,440 and today is the hybrid session we will 6 00:00:19,269 --> 00:00:13,360 have three talks 7 00:00:19,279 --> 00:00:25,910 yeah and so 8 00:00:32,150 --> 00:00:28,790 so it's a pleasure to to welcome dieter 9 00:00:34,150 --> 00:00:32,160 brown from ludwig maximilian universitat 10 00:00:35,590 --> 00:00:34,160 in munich 11 00:00:37,910 --> 00:00:35,600 and he's going to tell us about 12 00:00:40,790 --> 00:00:37,920 recreating the first steps of life using 13 00:00:42,869 --> 00:00:40,800 non-equilibrium settings 14 00:00:45,350 --> 00:00:42,879 thanks for having me 15 00:00:46,950 --> 00:00:45,360 and thanks for allowing the optimistic 16 00:00:49,190 --> 00:00:46,960 title let's see 17 00:00:51,270 --> 00:00:49,200 uh how we see life and the origins of 18 00:00:53,830 --> 00:00:51,280 life trying to figure out the emergence 19 00:00:55,910 --> 00:00:53,840 of life is i think a twofold into mixed 20 00:00:58,229 --> 00:00:55,920 axis chemicals 21 00:00:59,590 --> 00:00:58,239 physics has to come together that's what 22 00:01:02,069 --> 00:00:59,600 we try to do 23 00:01:04,390 --> 00:01:02,079 and it's like a maze we try to you know 24 00:01:06,310 --> 00:01:04,400 hop around do experiments figure out 25 00:01:08,469 --> 00:01:06,320 what are good conditions for this and we 26 00:01:11,270 --> 00:01:08,479 started the whole story by saying okay 27 00:01:12,789 --> 00:01:11,280 let's put the psi chemistry for a second 28 00:01:14,789 --> 00:01:12,799 and we use a protein to do the 29 00:01:15,990 --> 00:01:14,799 replication for our first evolutionary 30 00:01:17,990 --> 00:01:16,000 cycles 31 00:01:19,749 --> 00:01:18,000 we looked for convection traps where you 32 00:01:21,910 --> 00:01:19,759 have thermocycling 33 00:01:24,230 --> 00:01:21,920 but recently figured out that air water 34 00:01:26,310 --> 00:01:24,240 interfaces can help them if you combine 35 00:01:28,149 --> 00:01:26,320 them and you'll see an example of that 36 00:01:31,429 --> 00:01:28,159 but towards the end of the talk what i 37 00:01:34,469 --> 00:01:31,439 try to show you is that as we go more 38 00:01:36,630 --> 00:01:34,479 more towards chemistry rna ribozymes 39 00:01:37,510 --> 00:01:36,640 two three prime cyclic nucleotides we 40 00:01:39,910 --> 00:01:37,520 might 41 00:01:41,590 --> 00:01:39,920 lead to a real balance between chemistry 42 00:01:44,310 --> 00:01:41,600 and physics 43 00:01:46,550 --> 00:01:44,320 and examples of what we did is you know 44 00:01:48,469 --> 00:01:46,560 thermal gradients some phrases in a 45 00:01:51,109 --> 00:01:48,479 flow-through system running for a 46 00:01:52,870 --> 00:01:51,119 selection for the longer sequences but 47 00:01:55,030 --> 00:01:52,880 you can also have a look on sequence 48 00:01:57,109 --> 00:01:55,040 space evolution focusing yourself in 49 00:02:00,310 --> 00:01:57,119 sequence base or configure 50 00:02:02,870 --> 00:02:00,320 a convection cell which is basically 51 00:02:04,310 --> 00:02:02,880 able to run delicate rna molecules and 52 00:02:05,510 --> 00:02:04,320 still thermally cycle them without 53 00:02:08,469 --> 00:02:05,520 destroying and actually also 54 00:02:11,990 --> 00:02:08,479 accumulating or have a replicator made 55 00:02:14,470 --> 00:02:12,000 out of trna like molecules what i'll 56 00:02:17,430 --> 00:02:14,480 talk today about is is a system here 57 00:02:20,550 --> 00:02:17,440 it's it's air water interface basically 58 00:02:22,710 --> 00:02:20,560 a piece of of a of a cleft in a rock 59 00:02:25,510 --> 00:02:22,720 where you have some water at the bottom 60 00:02:27,270 --> 00:02:25,520 a larger volume on top as you heat and 61 00:02:29,750 --> 00:02:27,280 cool them at the same 62 00:02:32,949 --> 00:02:29,760 continuously you'll you'll see at the 63 00:02:34,869 --> 00:02:32,959 interface this dynamics so bubbles are 64 00:02:36,949 --> 00:02:34,879 forming on the cold side because they 65 00:02:38,550 --> 00:02:36,959 evaporate on the warm side 66 00:02:40,949 --> 00:02:38,560 it grows so large that they touch the 67 00:02:43,270 --> 00:02:40,959 warm side jump onto the warm side 68 00:02:45,190 --> 00:02:43,280 evaporate and you see these coffee ring 69 00:02:47,270 --> 00:02:45,200 effects here 70 00:02:49,190 --> 00:02:47,280 while at the interface molecules are 71 00:02:51,750 --> 00:02:49,200 also accumulating what you see here is a 72 00:02:53,990 --> 00:02:51,760 fluorescence of of dna molecule and you 73 00:02:56,470 --> 00:02:54,000 can actually simulate these conditions 74 00:02:58,229 --> 00:02:56,480 on a computer finite element methods and 75 00:03:00,390 --> 00:02:58,239 figure out that these 76 00:03:03,910 --> 00:03:00,400 settings are actually accumulating 77 00:03:06,229 --> 00:03:03,920 larger molecules always better so it has 78 00:03:08,550 --> 00:03:06,239 a selection physical selection pressure 79 00:03:10,149 --> 00:03:08,560 for length which we think is important 80 00:03:12,309 --> 00:03:10,159 for any replicating system because 81 00:03:14,149 --> 00:03:12,319 otherwise you get for the fast short 82 00:03:15,190 --> 00:03:14,159 replicating molecules and the test 83 00:03:17,350 --> 00:03:15,200 system 84 00:03:20,229 --> 00:03:17,360 was here to use a polymerase chain 85 00:03:21,830 --> 00:03:20,239 reaction a pcr but the interesting point 86 00:03:24,309 --> 00:03:21,840 here was that we used it at a 87 00:03:27,030 --> 00:03:24,319 temperature but actually the template 88 00:03:30,070 --> 00:03:27,040 which we wanted to replicate by you know 89 00:03:31,509 --> 00:03:30,080 those cycles of strand separation 90 00:03:33,670 --> 00:03:31,519 and have then 91 00:03:36,710 --> 00:03:33,680 the polymerase do the job 92 00:03:38,390 --> 00:03:36,720 was lower than the 51 93 00:03:39,350 --> 00:03:38,400 base target 94 00:03:42,149 --> 00:03:39,360 still 95 00:03:44,630 --> 00:03:42,159 we got an amplification and we'll show 96 00:03:46,789 --> 00:03:44,640 you later this amplification actually 97 00:03:48,789 --> 00:03:46,799 created molecules up a thousand bases 98 00:03:50,949 --> 00:03:48,799 long and we understand how this is 99 00:03:52,869 --> 00:03:50,959 possible and i'll go through the story 100 00:03:55,750 --> 00:03:52,879 here you see how at the interface the 101 00:03:57,190 --> 00:03:55,760 replication is triggered and this is 102 00:04:01,030 --> 00:03:57,200 really running 103 00:04:03,750 --> 00:04:01,040 uh and being created at the interface 104 00:04:06,229 --> 00:04:03,760 so what happens here is that the 105 00:04:08,550 --> 00:04:06,239 molecules as they are replicated in this 106 00:04:11,750 --> 00:04:08,560 interplay between the dry side on top 107 00:04:15,509 --> 00:04:11,760 and the wet side at the bottom 108 00:04:17,110 --> 00:04:15,519 is is doing the job and you see actually 109 00:04:19,590 --> 00:04:17,120 if you have a thermocycler where you do 110 00:04:21,189 --> 00:04:19,600 the pcr you see the usual that if you 111 00:04:23,350 --> 00:04:21,199 have competition between the long and 112 00:04:25,670 --> 00:04:23,360 the short strength the long strain is 113 00:04:28,150 --> 00:04:25,680 losing the short frame is making the 114 00:04:29,590 --> 00:04:28,160 show but the funny part is that if you 115 00:04:31,430 --> 00:04:29,600 put this on 116 00:04:32,950 --> 00:04:31,440 such a 117 00:04:35,350 --> 00:04:32,960 temperature gradient with these air 118 00:04:37,430 --> 00:04:35,360 water interface you see that both are 119 00:04:39,430 --> 00:04:37,440 you know equally well 120 00:04:41,189 --> 00:04:39,440 and you see these very large strands on 121 00:04:42,629 --> 00:04:41,199 top and you might wonder oh that's some 122 00:04:44,870 --> 00:04:42,639 dirt effect and we don't fully 123 00:04:45,749 --> 00:04:44,880 understand so we went for sequencing 124 00:04:48,870 --> 00:04:45,759 these 125 00:04:51,270 --> 00:04:48,880 and figure out what might be happening 126 00:04:53,670 --> 00:04:51,280 and the story is now the following we 127 00:04:57,110 --> 00:04:53,680 only saw those long strands if we added 128 00:04:59,030 --> 00:04:57,120 co2 into the mixture now we add the co2 129 00:05:01,189 --> 00:04:59,040 and this fluorescence signal 130 00:05:04,070 --> 00:05:01,199 you see here in the fret you see that if 131 00:05:06,870 --> 00:05:04,080 you add the co2 the chambers that the uh 132 00:05:09,110 --> 00:05:06,880 is that in these bubbles the strands are 133 00:05:11,590 --> 00:05:09,120 separating you get single-stranded rna 134 00:05:14,310 --> 00:05:11,600 or dna and you also see at the same time 135 00:05:16,230 --> 00:05:14,320 that the ph drops so what's to be 136 00:05:18,870 --> 00:05:16,240 expected here is that 137 00:05:21,029 --> 00:05:18,880 as the water evaporates from the bottom 138 00:05:23,909 --> 00:05:21,039 even if this is very salty you'll get 139 00:05:26,230 --> 00:05:23,919 pure water on the side this bubble grows 140 00:05:28,870 --> 00:05:26,240 then jumps on the other side where you 141 00:05:30,230 --> 00:05:28,880 have previously dried the dna then you 142 00:05:33,830 --> 00:05:30,240 have a very low 143 00:05:34,870 --> 00:05:33,840 salt environment for this dna 144 00:05:37,189 --> 00:05:34,880 and 145 00:05:39,430 --> 00:05:37,199 if you add the co2 on top of it because 146 00:05:41,430 --> 00:05:39,440 this is low salt it's not buffering 147 00:05:42,550 --> 00:05:41,440 anymore you also get a low ph because of 148 00:05:44,790 --> 00:05:42,560 co2 149 00:05:47,189 --> 00:05:44,800 and it turns out we can now 150 00:05:49,189 --> 00:05:47,199 calculate and figure out what is the 151 00:05:51,510 --> 00:05:49,199 salt concentration by these optical 152 00:05:53,670 --> 00:05:51,520 measurements you find it's a very low 153 00:05:56,550 --> 00:05:53,680 magnesium chloride concentration about 154 00:05:57,830 --> 00:05:56,560 40 volts lower than the bulk 155 00:06:02,790 --> 00:05:57,840 and 156 00:06:05,430 --> 00:06:02,800 the situation in this setting is now 157 00:06:06,870 --> 00:06:05,440 that as you change the ph with these two 158 00:06:08,870 --> 00:06:06,880 droplets 159 00:06:11,670 --> 00:06:08,880 and you change the salt within these two 160 00:06:15,430 --> 00:06:11,680 droplets you enable a 161 00:06:17,430 --> 00:06:15,440 cycling at this setting for your bulk 162 00:06:19,590 --> 00:06:17,440 and in the dual down here 163 00:06:22,629 --> 00:06:19,600 and you see already in this 164 00:06:25,110 --> 00:06:22,639 fret calibration here that in the blue 165 00:06:27,510 --> 00:06:25,120 you separate the strands 166 00:06:29,870 --> 00:06:27,520 by not only going for low salt that 167 00:06:32,710 --> 00:06:29,880 would not have been enough but by 168 00:06:34,629 --> 00:06:32,720 acidification by the co2 please note 169 00:06:37,590 --> 00:06:34,639 that co2 on early earth would be a 170 00:06:40,309 --> 00:06:37,600 common gas a common atmosphere so you 171 00:06:42,629 --> 00:06:40,319 would actually expect to have co2 around 172 00:06:44,550 --> 00:06:42,639 and the control experiments clearly show 173 00:06:47,110 --> 00:06:44,560 you only get this effect if you have co2 174 00:06:48,950 --> 00:06:47,120 if you just use normal atmosphere you're 175 00:06:51,670 --> 00:06:48,960 not getting it 176 00:06:53,830 --> 00:06:51,680 now back to the sequencing i told you we 177 00:06:55,830 --> 00:06:53,840 got these very long strands and the gel 178 00:06:59,670 --> 00:06:55,840 it was hard to quantify by sequencing 179 00:07:01,510 --> 00:06:59,680 you found up to 1300 bases 180 00:07:02,390 --> 00:07:01,520 and we looked at the sequences and we 181 00:07:05,350 --> 00:07:02,400 looked 182 00:07:07,510 --> 00:07:05,360 on which part of that phase space which 183 00:07:10,150 --> 00:07:07,520 we explored the shorter strands thread 184 00:07:11,350 --> 00:07:10,160 probes to figure out when these strands 185 00:07:14,070 --> 00:07:11,360 are melting 186 00:07:16,870 --> 00:07:14,080 we find that these sequences all pile up 187 00:07:20,070 --> 00:07:16,880 along this melting line 188 00:07:22,390 --> 00:07:20,080 so here are our starting strands 189 00:07:24,309 --> 00:07:22,400 remember that we have about 50 50 gc 190 00:07:26,870 --> 00:07:24,319 content at a 50 mirror 191 00:07:29,510 --> 00:07:26,880 then we also see some aggregated parts 192 00:07:31,189 --> 00:07:29,520 where you make those templates next to 193 00:07:34,150 --> 00:07:31,199 each other and you make longer strands 194 00:07:37,510 --> 00:07:34,160 of it but then you see these 195 00:07:39,589 --> 00:07:37,520 strands here which in this case got a 196 00:07:42,230 --> 00:07:39,599 higher a t than gc 197 00:07:45,029 --> 00:07:42,240 content indicating that you had a 198 00:07:47,990 --> 00:07:45,039 selection pressure which was kind of 199 00:07:49,830 --> 00:07:48,000 driven by two forces and i just give you 200 00:07:51,110 --> 00:07:49,840 the full picture here this is only the 201 00:07:53,029 --> 00:07:51,120 small picture 202 00:07:57,110 --> 00:07:53,039 down here so 203 00:07:59,189 --> 00:07:57,120 these white sequences up here apparently 204 00:08:01,510 --> 00:07:59,199 really were selected for being at that 205 00:08:03,270 --> 00:08:01,520 melting transition now it makes sense to 206 00:08:05,270 --> 00:08:03,280 push it for a system where it can 207 00:08:07,430 --> 00:08:05,280 actually separate the strands 208 00:08:11,270 --> 00:08:07,440 and in this case it actually had to 209 00:08:14,230 --> 00:08:11,280 change the at gc content to do that 210 00:08:16,309 --> 00:08:14,240 please keep in mind that still in this 211 00:08:18,469 --> 00:08:16,319 setting the system seems to be pushed 212 00:08:20,390 --> 00:08:18,479 for longer strands so this 213 00:08:22,309 --> 00:08:20,400 it was not enough to keep those short 214 00:08:24,230 --> 00:08:22,319 sequences those were not the sequences 215 00:08:26,950 --> 00:08:24,240 we are seeing in the majority 216 00:08:29,350 --> 00:08:26,960 but those accumulation effects at these 217 00:08:31,589 --> 00:08:29,360 interfaces most probably were driving 218 00:08:33,509 --> 00:08:31,599 the system also for long australians so 219 00:08:35,190 --> 00:08:33,519 what you can see here is that the simple 220 00:08:38,550 --> 00:08:35,200 air water interface 221 00:08:40,230 --> 00:08:38,560 is enabling with co2 to give you low 222 00:08:43,589 --> 00:08:40,240 salt conditions while you have a high 223 00:08:46,949 --> 00:08:44,470 and 224 00:08:49,190 --> 00:08:46,959 the co2 the acidification of that allows 225 00:08:50,230 --> 00:08:49,200 you to really fully separate really long 226 00:08:51,990 --> 00:08:50,240 strands 227 00:08:54,310 --> 00:08:52,000 now the same thing should actually be 228 00:08:58,870 --> 00:08:54,320 possible also for rna and we'll get to 229 00:09:03,430 --> 00:09:01,110 there's some modeling you can do 230 00:09:05,430 --> 00:09:03,440 and the modeling confirms the 231 00:09:07,910 --> 00:09:05,440 replication simple replication model 232 00:09:09,910 --> 00:09:07,920 confirms the dynamics and the sequence 233 00:09:11,670 --> 00:09:09,920 selection you have in there 234 00:09:14,070 --> 00:09:11,680 so as we want to go for 235 00:09:18,150 --> 00:09:14,080 origin of life figuring out processes 236 00:09:20,630 --> 00:09:18,160 how to start replication how to start 237 00:09:22,389 --> 00:09:20,640 the first cycles of darwin evolution we 238 00:09:23,829 --> 00:09:22,399 went through a number of molecules and 239 00:09:26,230 --> 00:09:23,839 we could go through the advantages 240 00:09:28,070 --> 00:09:26,240 disadvantages of these systems 241 00:09:30,389 --> 00:09:28,080 as they are more aggressive making more 242 00:09:32,949 --> 00:09:30,399 side products more mild but then often 243 00:09:35,509 --> 00:09:32,959 not really covering all the bases or 244 00:09:37,430 --> 00:09:35,519 difficult to achieve with simple 245 00:09:39,750 --> 00:09:37,440 probiotic chemistry and we just looked 246 00:09:41,910 --> 00:09:39,760 back again into chemistries from the 247 00:09:44,150 --> 00:09:41,920 1970s to the two three cyclic 248 00:09:46,070 --> 00:09:44,160 nucleotides and we figured out that at 249 00:09:48,470 --> 00:09:46,080 high ph 250 00:09:51,750 --> 00:09:48,480 with including the g base 251 00:09:54,310 --> 00:09:51,760 these are uh giving us a nice setting 252 00:09:56,389 --> 00:09:54,320 and the 70s analytical techniques were 253 00:09:57,430 --> 00:09:56,399 not yet ready and most experiments were 254 00:09:59,269 --> 00:09:57,440 tried 255 00:10:02,069 --> 00:09:59,279 or published experimentally try this 256 00:10:04,470 --> 00:10:02,079 with a and with g people 257 00:10:06,230 --> 00:10:04,480 had a hard time to separate molecules so 258 00:10:07,829 --> 00:10:06,240 also these systems actually behave quite 259 00:10:11,350 --> 00:10:07,839 nicely in these 260 00:10:13,750 --> 00:10:11,360 water interfaces where we've seen before 261 00:10:16,470 --> 00:10:13,760 phosphorylation encapsulation and 262 00:10:18,470 --> 00:10:16,480 vesicle for tricycling crystallization 263 00:10:20,150 --> 00:10:18,480 and and that enrichment of that coffee 264 00:10:22,230 --> 00:10:20,160 ring effect 265 00:10:25,509 --> 00:10:22,240 so let's see what the system can do for 266 00:10:27,509 --> 00:10:25,519 us and we go for about ph 9 to 11 267 00:10:30,310 --> 00:10:27,519 with quite right a 268 00:10:31,990 --> 00:10:30,320 large range of temperatures and can for 269 00:10:34,350 --> 00:10:32,000 example in such a system run the 270 00:10:36,630 --> 00:10:34,360 phosphorylation to make these with 271 00:10:38,230 --> 00:10:36,640 trimetaphosphate quite specifically we 272 00:10:41,110 --> 00:10:38,240 don't get 273 00:10:43,590 --> 00:10:41,120 much if all detectable 5-prime 274 00:10:46,949 --> 00:10:43,600 phosphorylation but then also in the dry 275 00:10:49,670 --> 00:10:46,959 state with the initial ph you find a 276 00:10:53,190 --> 00:10:49,680 quite efficient polymerization 277 00:10:55,829 --> 00:10:53,200 so between ph 8 and 12 you get these 278 00:10:58,870 --> 00:10:55,839 oligomer lengths it goes up to 15 279 00:11:00,790 --> 00:10:58,880 roughly we're still improving here 280 00:11:02,389 --> 00:11:00,800 and this is a polymerization 281 00:11:03,590 --> 00:11:02,399 which is driven 282 00:11:05,110 --> 00:11:03,600 which is shown 283 00:11:07,110 --> 00:11:05,120 for g 284 00:11:09,590 --> 00:11:07,120 it is a bit more difficult to get all 285 00:11:11,670 --> 00:11:09,600 the bases involved again work in 286 00:11:15,269 --> 00:11:11,680 progress so this is the best we could 287 00:11:17,110 --> 00:11:15,279 achieve so far where we have mixed 288 00:11:18,470 --> 00:11:17,120 four bases at five millimolar 289 00:11:20,949 --> 00:11:18,480 concentration 290 00:11:23,030 --> 00:11:20,959 and this experiment in blue was now 291 00:11:23,910 --> 00:11:23,040 again running these water interfaces so 292 00:11:26,150 --> 00:11:23,920 they 293 00:11:28,150 --> 00:11:26,160 a water cycling 294 00:11:29,509 --> 00:11:28,160 dry wet cycling you are implementing 295 00:11:32,790 --> 00:11:29,519 here 296 00:11:34,389 --> 00:11:32,800 is also effective and gives you 297 00:11:37,590 --> 00:11:34,399 quite an efficient 298 00:11:39,750 --> 00:11:37,600 polymerization dynamics 299 00:11:42,150 --> 00:11:39,760 so how do we detect this that's a longer 300 00:11:44,389 --> 00:11:42,160 story and i think it's also an important 301 00:11:45,670 --> 00:11:44,399 story to discuss that we are sure what 302 00:11:49,110 --> 00:11:45,680 we are seeing 303 00:11:51,269 --> 00:11:49,120 it has a safety precipitation step first 304 00:11:52,550 --> 00:11:51,279 we could omit it but it also reduced a 305 00:11:54,710 --> 00:11:52,560 little bit the construction of the 306 00:11:56,389 --> 00:11:54,720 monomers in the mix and we are really 307 00:11:59,110 --> 00:11:56,399 sure that we get 308 00:12:01,670 --> 00:11:59,120 a clean ionization this is then run 309 00:12:04,790 --> 00:12:01,680 through hplc at 60 degrees high 310 00:12:08,310 --> 00:12:04,800 temperature special oligonucleotide 311 00:12:11,110 --> 00:12:08,320 column which even poly g can separate 312 00:12:13,670 --> 00:12:11,120 nicely without binding effects we can 313 00:12:14,870 --> 00:12:13,680 detect over three orders of magnitude 314 00:12:17,110 --> 00:12:14,880 have a 315 00:12:18,310 --> 00:12:17,120 please note the logarithmic scale here a 316 00:12:20,389 --> 00:12:18,320 raw 317 00:12:22,949 --> 00:12:20,399 mass spectrum which we then fit with the 318 00:12:24,190 --> 00:12:22,959 isotope pattern for all the molecules we 319 00:12:27,190 --> 00:12:24,200 want to check out 320 00:12:28,870 --> 00:12:27,200 [Applause] 321 00:12:29,750 --> 00:12:28,880 questions i'm happy to address these 322 00:12:31,910 --> 00:12:29,760 here 323 00:12:33,829 --> 00:12:31,920 so by that we could see for the 324 00:12:36,550 --> 00:12:33,839 polymerization what is interesting i 325 00:12:40,230 --> 00:12:36,560 think is that under very similar 326 00:12:42,629 --> 00:12:40,240 actually best running at ph 10 is also a 327 00:12:43,350 --> 00:12:42,639 templated ligation 328 00:12:44,710 --> 00:12:43,360 of 329 00:12:52,629 --> 00:12:44,720 a 330 00:12:54,790 --> 00:12:52,639 prime cyclic 331 00:12:56,069 --> 00:12:54,800 ending we get efficient 332 00:12:58,790 --> 00:12:56,079 um 333 00:13:01,110 --> 00:12:58,800 uh ligation that's ph nine actually at 334 00:13:03,190 --> 00:13:01,120 ph 10 or 11 this is boosted almost by 335 00:13:05,509 --> 00:13:03,200 factor of 10 so it seems to confirm that 336 00:13:07,190 --> 00:13:05,519 these are interesting conditions 337 00:13:09,030 --> 00:13:07,200 and of course it would be interesting to 338 00:13:11,030 --> 00:13:09,040 see that these polymerizations would be 339 00:13:13,829 --> 00:13:11,040 good enough to make that next step 340 00:13:15,590 --> 00:13:13,839 that's the major goal for the next years 341 00:13:18,389 --> 00:13:15,600 what we're doing 342 00:13:20,550 --> 00:13:18,399 if you then either actually try to do 343 00:13:22,470 --> 00:13:20,560 this in a dry state 344 00:13:25,590 --> 00:13:22,480 and you see the template ligation in the 345 00:13:28,069 --> 00:13:25,600 dry step which is still humid 346 00:13:30,310 --> 00:13:28,079 and then you could go just by wet 347 00:13:31,829 --> 00:13:30,320 low salt to separate the strains please 348 00:13:33,829 --> 00:13:31,839 note that all that chemistry doesn't 349 00:13:35,990 --> 00:13:33,839 need any magnesium doesn't need any salt 350 00:13:37,670 --> 00:13:36,000 to go so you could really make a fresh 351 00:13:39,590 --> 00:13:37,680 water cycle here 352 00:13:42,470 --> 00:13:39,600 but you could also think about that air 353 00:13:44,150 --> 00:13:42,480 water interface we saw before where you 354 00:13:47,189 --> 00:13:44,160 have a little bit magnesium in the mix 355 00:13:49,509 --> 00:13:47,199 but the wet in the hue with the co2 356 00:13:50,470 --> 00:13:49,519 gives you also efficient separation and 357 00:13:51,509 --> 00:13:50,480 that's 358 00:13:53,590 --> 00:13:51,519 something 359 00:13:54,629 --> 00:13:53,600 we are trying right now that we have a 360 00:13:58,550 --> 00:13:54,639 template 361 00:14:00,629 --> 00:13:58,560 have a ligating ribozyme have three 362 00:14:03,189 --> 00:14:00,639 parts which make a hammerhead 363 00:14:05,430 --> 00:14:03,199 ribozyme now if you normally do that in 364 00:14:06,949 --> 00:14:05,440 a in a normal vial you need high enough 365 00:14:07,990 --> 00:14:06,959 magnesium concentration that you'll 366 00:14:10,550 --> 00:14:08,000 never give 367 00:14:12,870 --> 00:14:10,560 uh get these ligated templates 368 00:14:15,350 --> 00:14:12,880 ligated strands off the template 369 00:14:18,069 --> 00:14:15,360 so you never have this configuration and 370 00:14:20,069 --> 00:14:18,079 the hammerhead folded replicated 371 00:14:21,990 --> 00:14:20,079 configuration at the same time 372 00:14:24,629 --> 00:14:22,000 interestingly enough in this chamber you 373 00:14:27,509 --> 00:14:24,639 can go down to five millimolar magnesium 374 00:14:29,189 --> 00:14:27,519 which is uh then cycling in here and we 375 00:14:31,990 --> 00:14:29,199 see at the same time this ligation 376 00:14:34,230 --> 00:14:32,000 affinity activity and actually the 377 00:14:37,509 --> 00:14:34,240 activity of the hammerhead ribozyme so 378 00:14:39,350 --> 00:14:37,519 it seems that we can go for rna-based 379 00:14:41,590 --> 00:14:39,360 ligating systems which 380 00:14:44,389 --> 00:14:41,600 have both at the same time templating 381 00:14:46,949 --> 00:14:44,399 ligation and actually unfolding from the 382 00:14:50,230 --> 00:14:46,959 template and being functional 383 00:14:54,069 --> 00:14:50,240 okay so with that i want to leave it we 384 00:14:56,870 --> 00:14:54,079 try to balance out this physical side of 385 00:14:59,110 --> 00:14:56,880 of making non-equilibrium condition to 386 00:15:01,110 --> 00:14:59,120 to drive the replication system 387 00:15:04,069 --> 00:15:01,120 and the same time also get more and more 388 00:15:05,910 --> 00:15:04,079 to a setting of chemistries which is 389 00:15:07,750 --> 00:15:05,920 simple that uh you know the 390 00:15:10,550 --> 00:15:07,760 phosphorylation is a 391 00:15:13,030 --> 00:15:10,560 simple mode which you get reasonably 392 00:15:14,710 --> 00:15:13,040 reliable without much more molecules so 393 00:15:15,990 --> 00:15:14,720 the idea you would start this 394 00:15:18,949 --> 00:15:16,000 evolutionary process with three 395 00:15:20,069 --> 00:15:18,959 molecules a jeep a c and a trimethyl 396 00:15:20,949 --> 00:15:20,079 phosphate 397 00:15:27,990 --> 00:15:20,959 and 398 00:15:29,749 --> 00:15:28,000 make a non-experienced setting where you 399 00:15:32,310 --> 00:15:29,759 can run it here through 400 00:15:34,389 --> 00:15:32,320 these evolutionary cycles 401 00:15:36,389 --> 00:15:34,399 if you break and hydrolyze the strands 402 00:15:39,030 --> 00:15:36,399 it's very likely that you end up with 403 00:15:41,430 --> 00:15:39,040 these cyclic two three prime again it's 404 00:15:43,189 --> 00:15:41,440 a major product of the hydrolysis here 405 00:15:44,710 --> 00:15:43,199 even if you lose one phosphate or you 406 00:15:47,430 --> 00:15:44,720 lose both 407 00:15:49,030 --> 00:15:47,440 the idea is that very similar conditions 408 00:15:51,749 --> 00:15:49,040 same condition bring you back to the 409 00:15:55,350 --> 00:15:51,759 trinity phosphate and that should be 410 00:15:59,110 --> 00:15:55,360 then able to explore 411 00:16:01,189 --> 00:15:59,120 sequences which are able to boost this 412 00:16:03,030 --> 00:16:01,199 ligation reaction here so that would be 413 00:16:04,230 --> 00:16:03,040 then the onset of a darwinian evolution 414 00:16:07,189 --> 00:16:04,240 where you would 415 00:16:09,350 --> 00:16:07,199 enhance the perhaps not so strong 416 00:16:10,389 --> 00:16:09,360 ligation activity in the beginning 417 00:16:12,710 --> 00:16:10,399 with 418 00:16:15,670 --> 00:16:12,720 strands and i think running that even in 419 00:16:17,670 --> 00:16:15,680 a dry state would be very interesting 420 00:16:20,310 --> 00:16:17,680 so that's where we are funding is below 421 00:16:22,310 --> 00:16:20,320 we look out for postdoc as short 422 00:16:24,550 --> 00:16:22,320 advertisement for hours of life meeting 423 00:16:27,110 --> 00:16:24,560 munich in about three weeks 424 00:16:29,090 --> 00:16:27,120 and i'm happy to take questions thanks 425 00:16:33,990 --> 00:16:29,100 very much 426 00:16:36,150 --> 00:16:34,000 [Applause] 427 00:16:38,069 --> 00:16:36,160 thank you very much for this impressive 428 00:16:39,749 --> 00:16:38,079 work so questions 429 00:16:42,069 --> 00:16:39,759 very nice 430 00:16:43,670 --> 00:16:42,079 uh bryce clifton from georgia tech um 431 00:16:46,150 --> 00:16:43,680 can you speak more about the 432 00:16:48,150 --> 00:16:46,160 environments that this sort of um 433 00:16:50,949 --> 00:16:48,160 like dew cycling and capillaries might 434 00:16:53,430 --> 00:16:50,959 be found 435 00:16:56,870 --> 00:16:53,440 uh the idea where you would get these 436 00:16:57,910 --> 00:16:56,880 low salt conditions uh would be rather 437 00:16:59,990 --> 00:16:57,920 you know 438 00:17:01,350 --> 00:17:00,000 not deep in the ocean 439 00:17:05,510 --> 00:17:01,360 you need some 440 00:17:07,829 --> 00:17:05,520 so you have to high pressures will 441 00:17:09,990 --> 00:17:07,839 prevent any bubbles 442 00:17:12,710 --> 00:17:10,000 in the rocks so you would say i would 443 00:17:15,110 --> 00:17:12,720 say you know anything 444 00:17:17,990 --> 00:17:15,120 20 30 degrees uh 445 00:17:20,309 --> 00:17:18,000 30 meters in the water you know or above 446 00:17:22,470 --> 00:17:20,319 so the idea would be rather go above for 447 00:17:23,510 --> 00:17:22,480 volcanic island where you have fresh 448 00:17:26,110 --> 00:17:23,520 water 449 00:17:28,630 --> 00:17:26,120 where the volcano might provide you 450 00:17:30,070 --> 00:17:28,640 trimetaphosphate source and 451 00:17:31,990 --> 00:17:30,080 and 452 00:17:34,549 --> 00:17:32,000 also we will kind of providing you 453 00:17:36,150 --> 00:17:34,559 naturally very polished rock samples 454 00:17:37,909 --> 00:17:36,160 you'd rather want to hide a little bit 455 00:17:38,870 --> 00:17:37,919 in a pore that you're not burned by the 456 00:17:39,750 --> 00:17:38,880 uv 457 00:17:42,390 --> 00:17:39,760 um 458 00:17:44,310 --> 00:17:42,400 but that you know some centimeters of 459 00:17:45,669 --> 00:17:44,320 rock could give you that 460 00:17:50,310 --> 00:17:45,679 um 461 00:17:51,909 --> 00:17:50,320 we 462 00:17:53,510 --> 00:17:51,919 there's 463 00:17:57,110 --> 00:17:53,520 more more evidence that you can get for 464 00:17:58,070 --> 00:17:57,120 high ph conditions uh in 465 00:18:01,029 --> 00:17:58,080 in 466 00:18:03,990 --> 00:18:01,039 and 467 00:18:06,310 --> 00:18:04,000 geoscience 468 00:18:08,150 --> 00:18:06,320 can be provided which is matching the 469 00:18:09,510 --> 00:18:08,160 condition 470 00:18:12,230 --> 00:18:09,520 thank you um 471 00:18:14,390 --> 00:18:12,240 one more question also please um 472 00:18:17,909 --> 00:18:14,400 with the cyclic phosphate 473 00:18:20,710 --> 00:18:17,919 ligation uh it tends to form at least 474 00:18:23,190 --> 00:18:20,720 when templated uh vast majority of the 475 00:18:25,110 --> 00:18:23,200 two prime five prime linkage are you 476 00:18:27,590 --> 00:18:25,120 seeing that or are you seeing different 477 00:18:29,590 --> 00:18:27,600 results with no magnesium 478 00:18:32,390 --> 00:18:29,600 and um 479 00:18:33,830 --> 00:18:32,400 you know it's degrades pretty fast um 480 00:18:36,150 --> 00:18:33,840 compared to the 481 00:18:37,669 --> 00:18:36,160 uh you know canonical linkage can you 482 00:18:39,029 --> 00:18:37,679 say anything about that 483 00:18:41,110 --> 00:18:39,039 yes i mean that's also in the 484 00:18:42,549 --> 00:18:41,120 polarization while for the three five 485 00:18:45,029 --> 00:18:42,559 prime it's quite established that it 486 00:18:48,230 --> 00:18:45,039 makes the right one we're not sure here 487 00:18:50,470 --> 00:18:48,240 actually also already uh it's it's quite 488 00:18:52,230 --> 00:18:50,480 tricky to get the digest and the mass 489 00:18:54,150 --> 00:18:52,240 spec all together we 490 00:18:57,510 --> 00:18:54,160 we kind of would estimate right now it's 491 00:19:00,310 --> 00:18:57,520 50 50 hard to say you know still you get 492 00:19:02,150 --> 00:19:00,320 base pairing but it's more fragile now 493 00:19:05,510 --> 00:19:02,160 since you have that feedback loop down 494 00:19:07,350 --> 00:19:05,520 here i wouldn't be too concerned because 495 00:19:08,789 --> 00:19:07,360 you have a fast way to recycle your 496 00:19:10,870 --> 00:19:08,799 molecules if you go through that 497 00:19:12,710 --> 00:19:10,880 hydrolysis 498 00:19:15,430 --> 00:19:12,720 and then you would you know slowly 499 00:19:17,830 --> 00:19:15,440 select out the more stable ones 500 00:19:22,150 --> 00:19:20,150 we also hope that you know some rock 501 00:19:24,789 --> 00:19:22,160 interactions might have helped there 502 00:19:26,950 --> 00:19:24,799 that we might find you know 503 00:19:29,190 --> 00:19:26,960 peptides helping there we might find you 504 00:19:31,510 --> 00:19:29,200 know ribozymes helping there you know 505 00:19:34,070 --> 00:19:31,520 the point i want to make is that the the 506 00:19:36,549 --> 00:19:34,080 core process seems to be here under one 507 00:19:38,310 --> 00:19:36,559 pot condition so we have the steps here 508 00:19:40,230 --> 00:19:38,320 they are inefficient they have problems 509 00:19:41,590 --> 00:19:40,240 but that's nice because 510 00:19:44,710 --> 00:19:41,600 upon that 511 00:19:48,390 --> 00:19:44,720 evolutionary dynamics could kick in and 512 00:19:50,150 --> 00:19:48,400 help it by evolutionary process 513 00:19:52,230 --> 00:19:50,160 so if you know if you would not get 514 00:19:55,270 --> 00:19:52,240 anything then you could say okay it's a 515 00:19:56,870 --> 00:19:55,280 hard game right because you there's no 516 00:20:00,470 --> 00:19:56,880 there's no base 517 00:20:02,710 --> 00:20:00,480 reactivity but you have that 518 00:20:04,830 --> 00:20:02,720 thank you 519 00:20:07,909 --> 00:20:04,840 john ian of university of 520 00:20:09,990 --> 00:20:07,919 wisconsin-madison thanks for a very uh 521 00:20:13,750 --> 00:20:10,000 interesting talk i had a question about 522 00:20:17,029 --> 00:20:13,760 the pcr study where if i understand you 523 00:20:18,149 --> 00:20:17,039 had uh initial populations of templates 524 00:20:21,510 --> 00:20:18,159 that got 525 00:20:23,909 --> 00:20:21,520 longer over time is it necessary to have 526 00:20:25,190 --> 00:20:23,919 a seed template or is it possible that 527 00:20:27,270 --> 00:20:25,200 you could be 528 00:20:28,789 --> 00:20:27,280 um could you start a could you have an 529 00:20:32,950 --> 00:20:28,799 initial condition with just the 530 00:20:34,470 --> 00:20:32,960 nucleotides triphosphates and the enzyme 531 00:20:36,710 --> 00:20:34,480 i mean this polarization you saw here 532 00:20:39,029 --> 00:20:36,720 had no template it just starts from 533 00:20:40,710 --> 00:20:39,039 itself in the dry state so you basically 534 00:20:42,789 --> 00:20:40,720 all you have for that reaction is the g 535 00:20:45,430 --> 00:20:42,799 molecule nothing else no salt no 536 00:20:47,430 --> 00:20:45,440 catalyzer catalyzer you need to shift 537 00:20:50,390 --> 00:20:47,440 the ph wait for a day 538 00:20:53,029 --> 00:20:50,400 and you get those ten words 539 00:20:55,029 --> 00:20:53,039 you know having said that if 540 00:20:58,390 --> 00:20:55,039 by evolutionary dynamics you would have 541 00:20:59,990 --> 00:20:58,400 already some old rna hanging around it's 542 00:21:01,909 --> 00:21:00,000 of course the hope that already the 543 00:21:04,390 --> 00:21:01,919 polymerization is a bit biased and it's 544 00:21:06,630 --> 00:21:04,400 a little bit faster and better but we've 545 00:21:08,310 --> 00:21:06,640 not yet explored polymerization of that 546 00:21:10,230 --> 00:21:08,320 all we know is that the 547 00:21:12,789 --> 00:21:10,240 templated ligation of course would then 548 00:21:15,270 --> 00:21:12,799 pick it up you know it's it's 549 00:21:18,310 --> 00:21:15,280 the history of the sequence could either 550 00:21:20,470 --> 00:21:18,320 come in here perhaps already here but 551 00:21:22,230 --> 00:21:20,480 those would be 552 00:21:24,630 --> 00:21:22,240 the ways how the evolutionary cycles 553 00:21:26,789 --> 00:21:24,640 could pick up sequence information out 554 00:21:28,310 --> 00:21:26,799 there i guess i was referring to the 555 00:21:30,870 --> 00:21:28,320 first part of your talk where you were 556 00:21:32,789 --> 00:21:30,880 doing the pcr cycling and i was 557 00:21:33,909 --> 00:21:32,799 initiating with templates and getting 558 00:21:36,149 --> 00:21:33,919 much 559 00:21:39,750 --> 00:21:36,159 very long uh templates is it necessary 560 00:21:42,950 --> 00:21:39,760 to have a seed template in this system 561 00:21:45,110 --> 00:21:42,960 um i mean the the controls we run were 562 00:21:47,830 --> 00:21:45,120 you know without seed we got nothing 563 00:21:49,830 --> 00:21:47,840 it's a bit important point here because 564 00:21:52,470 --> 00:21:49,840 you want to be sure that your primers of 565 00:21:55,510 --> 00:21:52,480 the pcr is not you know priming the 566 00:21:58,070 --> 00:21:55,520 system and doing it but my guess gut 567 00:22:00,149 --> 00:21:58,080 feeling but we have not done it is if 568 00:22:02,070 --> 00:22:00,159 you would be a bit sloppy in your in 569 00:22:04,230 --> 00:22:02,080 your primer design you know you make 570 00:22:06,070 --> 00:22:04,240 primer dimers and then i would say in 571 00:22:07,750 --> 00:22:06,080 such a setting it would be very likely 572 00:22:09,909 --> 00:22:07,760 that if you have a primer diameter down 573 00:22:13,990 --> 00:22:09,919 there it will also start the evolution 574 00:22:18,630 --> 00:22:16,149 also if you would start with a longer 575 00:22:20,390 --> 00:22:18,640 template here because the pcr is quite 576 00:22:22,630 --> 00:22:20,400 precise 577 00:22:24,870 --> 00:22:22,640 i would also give a good chance that 578 00:22:26,390 --> 00:22:24,880 some of these sequences are still you 579 00:22:28,789 --> 00:22:26,400 know then retained 580 00:22:30,870 --> 00:22:28,799 here we push the system for 581 00:22:32,870 --> 00:22:30,880 you know starting here and apparently it 582 00:22:33,750 --> 00:22:32,880 wants to replicate up here so there was 583 00:22:36,950 --> 00:22:33,760 no 584 00:22:39,430 --> 00:22:36,960 way to to keep on the sequences other 585 00:22:41,510 --> 00:22:39,440 than you know duplicating them up to 586 00:22:43,350 --> 00:22:41,520 here and then the system had to give up 587 00:22:44,789 --> 00:22:43,360 because this is too far away from the 588 00:22:46,870 --> 00:22:44,799 melting temperature and then really 589 00:22:48,630 --> 00:22:46,880 evolve its own sequence 590 00:22:50,870 --> 00:22:48,640 but i would guess if you would give it a 591 00:22:53,350 --> 00:22:50,880 more proper template 592 00:22:56,230 --> 00:22:53,360 you would be able to tune it 593 00:22:58,310 --> 00:22:56,240 and make it start from the right place 594 00:23:00,710 --> 00:22:58,320 very elegant work thank you thank you 595 00:23:06,950 --> 00:23:00,720 very much let's approach victor again 596 00:23:11,909 --> 00:23:09,430 so lena is from the university of 597 00:23:14,789 --> 00:23:11,919 wisconsin-madison and he's going to tell 598 00:23:17,110 --> 00:23:14,799 us about a chemical ecosystem selection 599 00:23:25,350 --> 00:23:17,120 framework for studying the origins of 600 00:23:28,310 --> 00:23:27,029 i just don't want to jingle while i'm up 601 00:23:30,950 --> 00:23:28,320 here 602 00:23:32,950 --> 00:23:30,960 uh hi yeah so i want to tell you about 603 00:23:34,630 --> 00:23:32,960 some of the work that our research group 604 00:23:35,590 --> 00:23:34,640 has been doing over the last several 605 00:23:38,390 --> 00:23:35,600 years 606 00:23:40,390 --> 00:23:38,400 trying to resolve or at least contribute 607 00:23:42,070 --> 00:23:40,400 to the resolution of 608 00:23:44,149 --> 00:23:42,080 i think what is still the biggest 609 00:23:45,510 --> 00:23:44,159 mystery to me about the origin of life 610 00:23:48,070 --> 00:23:45,520 which is how 611 00:23:51,909 --> 00:23:48,080 non-living stuff non-living components 612 00:23:53,990 --> 00:23:51,919 can give rise to life to living systems 613 00:23:55,750 --> 00:23:54,000 in the complete absence of a prior 614 00:23:58,470 --> 00:23:55,760 living process 615 00:24:00,149 --> 00:23:58,480 namely evolution so our group has been 616 00:24:02,149 --> 00:24:00,159 using both theory and wet lab 617 00:24:03,990 --> 00:24:02,159 experiments to try to 618 00:24:06,710 --> 00:24:04,000 generate lifelike systems in a 619 00:24:08,390 --> 00:24:06,720 laboratory setting to try to answer this 620 00:24:12,710 --> 00:24:08,400 question 621 00:24:15,029 --> 00:24:12,720 what were the first evolvers 622 00:24:17,190 --> 00:24:15,039 what are the simplest chemical systems 623 00:24:19,430 --> 00:24:17,200 that we can imagine that are capable of 624 00:24:22,230 --> 00:24:19,440 adaptive evolution that themselves don't 625 00:24:23,750 --> 00:24:22,240 require evolution to appear 626 00:24:26,390 --> 00:24:23,760 now there have been many suggestions as 627 00:24:29,430 --> 00:24:26,400 to what the first evolvers might be how 628 00:24:32,310 --> 00:24:29,440 ranging from individual nucleic acid 629 00:24:33,750 --> 00:24:32,320 molecules to protocells to metabolic 630 00:24:35,590 --> 00:24:33,760 cycles 631 00:24:37,110 --> 00:24:35,600 and the scenario we find most plausible 632 00:24:38,549 --> 00:24:37,120 in our exploring 633 00:24:41,430 --> 00:24:38,559 is that the first evolvers were 634 00:24:43,909 --> 00:24:41,440 ecosystems of interacting auto catalytic 635 00:24:45,430 --> 00:24:43,919 cycles and one particular appeal of this 636 00:24:47,590 --> 00:24:45,440 model is that it could explain how 637 00:24:50,149 --> 00:24:47,600 evolution might be possible in the 638 00:24:51,750 --> 00:24:50,159 absence of genetic polymers as opposed 639 00:24:54,870 --> 00:24:51,760 to assuming that those genetic polymers 640 00:24:56,950 --> 00:24:54,880 were required for evolution to initiate 641 00:24:58,470 --> 00:24:56,960 but what would evolution look like in 642 00:25:00,390 --> 00:24:58,480 these systems that's been the primary 643 00:25:01,909 --> 00:25:00,400 challenge with these models 644 00:25:03,909 --> 00:25:01,919 and there have been a few ideas 645 00:25:06,070 --> 00:25:03,919 including auto catalytic core models 646 00:25:07,990 --> 00:25:06,080 like the one shown here that attributes 647 00:25:10,230 --> 00:25:08,000 the potential for evolvability to the 648 00:25:12,789 --> 00:25:10,240 presence of nested auto catalytic cores 649 00:25:14,390 --> 00:25:12,799 within larger auto catalytic networks 650 00:25:16,470 --> 00:25:14,400 but still these models have not 651 00:25:18,549 --> 00:25:16,480 comprehensively explained how evolution 652 00:25:20,390 --> 00:25:18,559 might initiate in these systems which is 653 00:25:22,070 --> 00:25:20,400 what our lab has been trying to tackle 654 00:25:24,149 --> 00:25:22,080 so we've really built on these models 655 00:25:27,110 --> 00:25:24,159 this is work led by our theoretical 656 00:25:29,029 --> 00:25:27,120 group primarily by postdoc zhang pang 657 00:25:31,269 --> 00:25:29,039 who have developed a chemical ecology 658 00:25:33,029 --> 00:25:31,279 paradigm to describe the behavior of 659 00:25:35,190 --> 00:25:33,039 sets of interacting auto catalytic 660 00:25:38,070 --> 00:25:35,200 cycles we refer to these as chemical 661 00:25:40,149 --> 00:25:38,080 ecosystems and the reason for this name 662 00:25:42,630 --> 00:25:40,159 is that they found that the behavior of 663 00:25:44,950 --> 00:25:42,640 even the simplest auto catalytic systems 664 00:25:47,350 --> 00:25:44,960 can be approximated or modeled using 665 00:25:48,870 --> 00:25:47,360 ecological principles and the growth of 666 00:25:51,430 --> 00:25:48,880 these systems can actually be 667 00:25:53,510 --> 00:25:51,440 approximated using logistic growth which 668 00:25:55,830 --> 00:25:53,520 essentially means that the behavior of 669 00:25:58,549 --> 00:25:55,840 these systems is essentially similar to 670 00:26:00,230 --> 00:25:58,559 populations of biological species 671 00:26:02,789 --> 00:26:00,240 and even more interestingly when you 672 00:26:05,830 --> 00:26:02,799 have pairs of interacting auto catalytic 673 00:26:08,230 --> 00:26:05,840 cycles that behave either as competitors 674 00:26:10,070 --> 00:26:08,240 mutualists or even predator and prey 675 00:26:12,390 --> 00:26:10,080 then they start to display dynamic 676 00:26:13,990 --> 00:26:12,400 behaviors that are very reminiscent of 677 00:26:16,230 --> 00:26:14,000 what you'd see in ecological 678 00:26:18,070 --> 00:26:16,240 interactions observed in biological 679 00:26:20,230 --> 00:26:18,080 populations 680 00:26:22,950 --> 00:26:20,240 now the big consequence of this chemical 681 00:26:25,110 --> 00:26:22,960 ecology model so far is that it suggests 682 00:26:27,669 --> 00:26:25,120 that chemical ecosystems might be able 683 00:26:29,669 --> 00:26:27,679 to evolve as a result of the sum total 684 00:26:32,070 --> 00:26:29,679 or at least a combination of different 685 00:26:34,710 --> 00:26:32,080 ecological interactions 686 00:26:36,950 --> 00:26:34,720 so we've actually been trying to explain 687 00:26:39,269 --> 00:26:36,960 how natural selection among such 688 00:26:41,029 --> 00:26:39,279 ecosystems could happen in natural 689 00:26:43,190 --> 00:26:41,039 settings so going beyond these models 690 00:26:45,110 --> 00:26:43,200 this abstract world and actually trying 691 00:26:46,870 --> 00:26:45,120 to conceive of how this might look like 692 00:26:49,110 --> 00:26:46,880 right how might this have happened at 693 00:26:50,950 --> 00:26:49,120 the origin of life by considering 694 00:26:53,430 --> 00:26:50,960 natural environments and the one we like 695 00:26:55,350 --> 00:26:53,440 to imagine is a sea floor 696 00:26:57,190 --> 00:26:55,360 recognizing that an important component 697 00:26:59,430 --> 00:26:57,200 of these environments is mineral 698 00:27:01,510 --> 00:26:59,440 surfaces now minerals have long been 699 00:27:03,590 --> 00:27:01,520 implicated in the origin of life for 700 00:27:05,990 --> 00:27:03,600 various reasons they do really cool 701 00:27:07,669 --> 00:27:06,000 things like absorb or stick to different 702 00:27:09,430 --> 00:27:07,679 organic components including some of the 703 00:27:12,149 --> 00:27:09,440 building blocks of life and they can 704 00:27:14,310 --> 00:27:12,159 also impart catalytic activities and in 705 00:27:16,230 --> 00:27:14,320 essence in essence replacing or 706 00:27:18,470 --> 00:27:16,240 preceding the function of biological 707 00:27:20,230 --> 00:27:18,480 enzymes 708 00:27:22,549 --> 00:27:20,240 and so we like to imagine that 709 00:27:24,470 --> 00:27:22,559 ecosystems uh chemical ecosystems that 710 00:27:26,950 --> 00:27:24,480 absorbed on mineral surfaces which we 711 00:27:29,590 --> 00:27:26,960 endearingly refer to as slimes for 712 00:27:31,590 --> 00:27:29,600 surface limited molecular ecosystems 713 00:27:34,310 --> 00:27:31,600 once they're absorbed to mineral 714 00:27:36,630 --> 00:27:34,320 surfaces if there's continual turnover 715 00:27:39,909 --> 00:27:36,640 of these surfaces with the exposure of 716 00:27:41,669 --> 00:27:39,919 new mineral faces removal of old ones 717 00:27:43,430 --> 00:27:41,679 this would effectively enrich for 718 00:27:46,070 --> 00:27:43,440 variants of these ecosystems that are 719 00:27:48,149 --> 00:27:46,080 better at getting from grain to grain 720 00:27:50,630 --> 00:27:48,159 so in principle this chemical ecosystem 721 00:27:53,029 --> 00:27:50,640 model could explain how you can get 722 00:27:55,110 --> 00:27:53,039 evolution and complexification 723 00:27:57,269 --> 00:27:55,120 without implicating compartments or even 724 00:27:59,269 --> 00:27:57,279 genetic polymers 725 00:28:01,350 --> 00:27:59,279 now of course a really important part of 726 00:28:04,070 --> 00:28:01,360 our work and what i've been focusing on 727 00:28:05,830 --> 00:28:04,080 in my phd is to try to validate and test 728 00:28:07,510 --> 00:28:05,840 this model empirically 729 00:28:09,190 --> 00:28:07,520 and to do this we've been developing an 730 00:28:11,350 --> 00:28:09,200 experimental framework 731 00:28:13,510 --> 00:28:11,360 that is designed to generate and study 732 00:28:15,269 --> 00:28:13,520 these slimes in a laboratory setting 733 00:28:17,350 --> 00:28:15,279 we've called it chemical ecosystem 734 00:28:19,510 --> 00:28:17,360 selection it's directly inspired by 735 00:28:21,590 --> 00:28:19,520 microbial ecosystem selection which you 736 00:28:23,269 --> 00:28:21,600 can think of as experimental evolution 737 00:28:25,269 --> 00:28:23,279 that allows you to select for community 738 00:28:26,310 --> 00:28:25,279 level traits as opposed to individual 739 00:28:28,310 --> 00:28:26,320 traits 740 00:28:30,950 --> 00:28:28,320 and the basic principle is very very 741 00:28:32,789 --> 00:28:30,960 simple it just involves combining some 742 00:28:35,269 --> 00:28:32,799 kind of food rich solution with a 743 00:28:37,750 --> 00:28:35,279 mineral phase so mineral grains and 744 00:28:40,470 --> 00:28:37,760 deploying an analog of experimental 745 00:28:42,950 --> 00:28:40,480 evolution with repeated dilution 746 00:28:45,510 --> 00:28:42,960 so to do this we incubate ingredients 747 00:28:47,750 --> 00:28:45,520 allow for slimes to establish themselves 748 00:28:50,389 --> 00:28:47,760 and then we go in and transfer a small 749 00:28:53,110 --> 00:28:50,399 subset from one generation usually about 750 00:28:55,029 --> 00:28:53,120 10 to 20 percent to a new reaction 751 00:28:57,510 --> 00:28:55,039 vessel containing fresh ingredients so 752 00:28:59,909 --> 00:28:57,520 fresh food inputs fresh uncolonized 753 00:29:02,070 --> 00:28:59,919 mineral surface and we do this over and 754 00:29:04,710 --> 00:29:02,080 over again with the hopes of selectively 755 00:29:06,149 --> 00:29:04,720 enriching for variance the variants here 756 00:29:07,510 --> 00:29:06,159 in this diagram are depicted by 757 00:29:09,190 --> 00:29:07,520 different colors 758 00:29:11,110 --> 00:29:09,200 that are better at getting from grain to 759 00:29:12,870 --> 00:29:11,120 grain so in effect imposing a form of 760 00:29:15,029 --> 00:29:12,880 natural selection 761 00:29:17,669 --> 00:29:15,039 and of course we routinely sample both 762 00:29:20,149 --> 00:29:17,679 the bulk solution the mineral grains and 763 00:29:22,310 --> 00:29:20,159 deploy a suite of different analytical 764 00:29:25,590 --> 00:29:22,320 techniques to track what's happening in 765 00:29:27,190 --> 00:29:25,600 response to the selection procedure 766 00:29:29,190 --> 00:29:27,200 now the really nice thing about this 767 00:29:30,789 --> 00:29:29,200 approach is that it isn't specific to a 768 00:29:32,950 --> 00:29:30,799 particular set of conditions so it's 769 00:29:34,870 --> 00:29:32,960 fairly agnostic in that regard 770 00:29:37,350 --> 00:29:34,880 it can be deployed basically on any 771 00:29:39,350 --> 00:29:37,360 combination of inputs and you can tailor 772 00:29:40,470 --> 00:29:39,360 it to test various hypotheses about what 773 00:29:42,950 --> 00:29:40,480 might be necessary 774 00:29:44,710 --> 00:29:42,960 for the origins of these processes 775 00:29:47,029 --> 00:29:44,720 now we've tested quite a few different 776 00:29:49,510 --> 00:29:47,039 combinations in our laboratory in this 777 00:29:51,830 --> 00:29:49,520 table i'm showing an example of a recipe 778 00:29:53,510 --> 00:29:51,840 that we've been exploring we've also 779 00:29:54,870 --> 00:29:53,520 played with different variations of this 780 00:29:57,269 --> 00:29:54,880 recipe 781 00:29:58,870 --> 00:29:57,279 it was inspired by the outputs of spark 782 00:30:00,549 --> 00:29:58,880 discharge experiments like the miller 783 00:30:02,630 --> 00:30:00,559 yuri experiment although we've made 784 00:30:03,909 --> 00:30:02,640 quite a few other additions 785 00:30:06,310 --> 00:30:03,919 the point here is that there's an 786 00:30:08,230 --> 00:30:06,320 infinite number of recipes you could try 787 00:30:10,630 --> 00:30:08,240 we actually recently published a sort of 788 00:30:12,870 --> 00:30:10,640 guide for what one might consider when 789 00:30:16,630 --> 00:30:12,880 putting together such a recipe uh what 790 00:30:18,389 --> 00:30:16,640 kinds of parameters you might consider 791 00:30:19,830 --> 00:30:18,399 and then and that's regardless of 792 00:30:21,510 --> 00:30:19,840 whether you're synthesizing it by 793 00:30:23,029 --> 00:30:21,520 simulating a particular process or 794 00:30:24,070 --> 00:30:23,039 assembling them from off-the-shelf 795 00:30:25,590 --> 00:30:24,080 reagents 796 00:30:27,909 --> 00:30:25,600 on the mineral side we've also tried 797 00:30:29,430 --> 00:30:27,919 quite a few we've targeted kind of crowd 798 00:30:32,070 --> 00:30:29,440 favorites among the origin of life 799 00:30:33,269 --> 00:30:32,080 community so things like iron sulfides 800 00:30:35,510 --> 00:30:33,279 clayman rolls we've tried 801 00:30:37,110 --> 00:30:35,520 montmorillonite and chloride i believe 802 00:30:39,750 --> 00:30:37,120 uh different phosphates but of course 803 00:30:41,269 --> 00:30:39,760 the list is goes on 804 00:30:43,110 --> 00:30:41,279 and then there are also a bunch of other 805 00:30:45,669 --> 00:30:43,120 parameters to consider like what the 806 00:30:47,350 --> 00:30:45,679 headspace is like is this an anoxic 807 00:30:48,470 --> 00:30:47,360 experiment do we include molecular 808 00:30:50,149 --> 00:30:48,480 oxygen 809 00:30:52,950 --> 00:30:50,159 uh what kind of incubation temperature 810 00:30:55,590 --> 00:30:52,960 are we talking about light no light 811 00:30:57,669 --> 00:30:55,600 the list goes on but some important ones 812 00:30:59,909 --> 00:30:57,679 are time between transfers so how long 813 00:31:01,830 --> 00:30:59,919 do we let these samples incubate before 814 00:31:03,669 --> 00:31:01,840 doing our serial dilution and then what 815 00:31:05,269 --> 00:31:03,679 is that dilution strength how strong is 816 00:31:06,630 --> 00:31:05,279 that selection 817 00:31:08,149 --> 00:31:06,640 and that is determined by how much is 818 00:31:09,750 --> 00:31:08,159 carried over from one generation to 819 00:31:11,590 --> 00:31:09,760 another so as you can see when you 820 00:31:13,029 --> 00:31:11,600 factor all these things in there's an 821 00:31:14,950 --> 00:31:13,039 infinite parameter space one might 822 00:31:17,190 --> 00:31:14,960 consider 823 00:31:18,789 --> 00:31:17,200 so one combination of conditions though 824 00:31:21,110 --> 00:31:18,799 that has been producing interesting 825 00:31:22,950 --> 00:31:21,120 patterns for us we published the results 826 00:31:24,789 --> 00:31:22,960 a few years ago 827 00:31:27,110 --> 00:31:24,799 we used a recipe that was quite similar 828 00:31:30,070 --> 00:31:27,120 to the one i just showed in that table 829 00:31:31,590 --> 00:31:30,080 we combined it with pyrite under anoxic 830 00:31:33,269 --> 00:31:31,600 conditions 831 00:31:34,789 --> 00:31:33,279 and in this case we tracked the 832 00:31:36,230 --> 00:31:34,799 concentration of free inorganic 833 00:31:37,590 --> 00:31:36,240 phosphate remaining at the end of a 834 00:31:39,669 --> 00:31:37,600 generation 835 00:31:41,269 --> 00:31:39,679 in 10 independently replicated lineages 836 00:31:42,950 --> 00:31:41,279 which is what you're seeing here and 837 00:31:45,029 --> 00:31:42,960 when we looked at the inorganic 838 00:31:46,389 --> 00:31:45,039 phosphate across generations we saw a 839 00:31:48,070 --> 00:31:46,399 really interesting pattern 840 00:31:50,149 --> 00:31:48,080 the reason by the way we tracked this 841 00:31:51,990 --> 00:31:50,159 particular proxy is because in that 842 00:31:54,310 --> 00:31:52,000 experiment we actually included atp as a 843 00:31:56,230 --> 00:31:54,320 phosphate source and we wanted a proxy 844 00:31:57,909 --> 00:31:56,240 for how much atp hydrolysis had been 845 00:31:59,909 --> 00:31:57,919 happening and when we looked at that you 846 00:32:01,029 --> 00:31:59,919 can see this really neat oscillatory 847 00:32:02,630 --> 00:32:01,039 pattern 848 00:32:03,750 --> 00:32:02,640 now there are lots of ways to interpret 849 00:32:05,190 --> 00:32:03,760 this pattern 850 00:32:07,909 --> 00:32:05,200 one of the simplest 851 00:32:09,350 --> 00:32:07,919 observations is that it is consistent 852 00:32:11,509 --> 00:32:09,360 with the presence of some kind of 853 00:32:13,909 --> 00:32:11,519 non-linear feedback loop which is 854 00:32:15,430 --> 00:32:13,919 consistent also with autocatalysis now 855 00:32:17,509 --> 00:32:15,440 of course we don't know for sure that 856 00:32:19,190 --> 00:32:17,519 that's what it is at the time we were 857 00:32:21,029 --> 00:32:19,200 not really equipped to do any in-depth 858 00:32:22,870 --> 00:32:21,039 chemical analysis we only had these very 859 00:32:23,990 --> 00:32:22,880 high level traits to track 860 00:32:25,350 --> 00:32:24,000 and we also 861 00:32:27,110 --> 00:32:25,360 were not able to carry out this 862 00:32:29,590 --> 00:32:27,120 experiment for longer you can see that 863 00:32:31,590 --> 00:32:29,600 once we saw these data we wish we had 864 00:32:33,029 --> 00:32:31,600 budgeted our materials to carry it out 865 00:32:34,470 --> 00:32:33,039 farther past 40 generations 866 00:32:36,470 --> 00:32:34,480 unfortunately we couldn't 867 00:32:39,110 --> 00:32:36,480 but all of this motivated the work i'm 868 00:32:40,950 --> 00:32:39,120 about to show you 869 00:32:42,710 --> 00:32:40,960 so 870 00:32:44,870 --> 00:32:42,720 naturally we wanted to repeat this 871 00:32:46,230 --> 00:32:44,880 experiment and do a few things carry out 872 00:32:49,029 --> 00:32:46,240 for longer 873 00:32:50,630 --> 00:32:49,039 but also couple it to more informative 874 00:32:53,190 --> 00:32:50,640 analytical techniques in particular 875 00:32:54,470 --> 00:32:53,200 chromatography mass spectrometry 876 00:32:57,190 --> 00:32:54,480 so 877 00:32:59,110 --> 00:32:57,200 the pandemic and supply chain issues hit 878 00:33:01,590 --> 00:32:59,120 us pretty hard however as i'm sure it 879 00:33:03,029 --> 00:33:01,600 did many of us in this room and we had 880 00:33:04,549 --> 00:33:03,039 to go back to the drawing board a little 881 00:33:06,310 --> 00:33:04,559 bit because a lot of the materials we 882 00:33:08,870 --> 00:33:06,320 were using to do the experiments 883 00:33:11,029 --> 00:33:08,880 previously were not easily available so 884 00:33:12,070 --> 00:33:11,039 plastic shortages rubber shortages were 885 00:33:14,070 --> 00:33:12,080 all 886 00:33:15,830 --> 00:33:14,080 kind of problematic for us but this 887 00:33:17,029 --> 00:33:15,840 forced us to adapt our format to 888 00:33:17,990 --> 00:33:17,039 actually something that was a little bit 889 00:33:19,750 --> 00:33:18,000 easier 890 00:33:21,430 --> 00:33:19,760 so we carried out an experiment in 891 00:33:23,029 --> 00:33:21,440 microtiter plates 892 00:33:25,350 --> 00:33:23,039 one advantage to this is that we could 893 00:33:27,110 --> 00:33:25,360 use uh better liquid handling techniques 894 00:33:29,190 --> 00:33:27,120 so we could use multi-channel pipettes 895 00:33:31,909 --> 00:33:29,200 and increase the throughput a little bit 896 00:33:33,990 --> 00:33:31,919 we could also look at multiple replicate 897 00:33:35,750 --> 00:33:34,000 lineages many more than we were able to 898 00:33:38,950 --> 00:33:35,760 before so we went from being able to 899 00:33:41,350 --> 00:33:38,960 handle maybe 10 20 at once to 96 900 00:33:42,070 --> 00:33:41,360 which was really helpful 901 00:33:45,350 --> 00:33:42,080 so 902 00:33:46,630 --> 00:33:45,360 using this format we used inputs that 903 00:33:49,110 --> 00:33:46,640 were similar to the ones i just showed 904 00:33:51,750 --> 00:33:49,120 you with a few key differences one is we 905 00:33:54,710 --> 00:33:51,760 replaced atp with chlorapatite as a 906 00:33:56,630 --> 00:33:54,720 mineral we also included pyrite and with 907 00:33:58,470 --> 00:33:56,640 a team of very talented undergraduate 908 00:34:00,470 --> 00:33:58,480 students and master's students we were 909 00:34:02,389 --> 00:34:00,480 able to carry out a long-term experiment 910 00:34:04,389 --> 00:34:02,399 for 70 generations 911 00:34:06,389 --> 00:34:04,399 which is the world record for longest 912 00:34:07,909 --> 00:34:06,399 ecosystem selection experiment so far 913 00:34:10,069 --> 00:34:07,919 maybe not experimental evolution but 914 00:34:12,869 --> 00:34:10,079 certainly this particular protocol 915 00:34:14,550 --> 00:34:12,879 we did that in about seven months so 916 00:34:16,149 --> 00:34:14,560 we learned a lot as we did this there 917 00:34:17,829 --> 00:34:16,159 were a lot of challenges but i think 918 00:34:19,430 --> 00:34:17,839 overall we kind of convinced ourselves 919 00:34:21,270 --> 00:34:19,440 that it was possible to do these high 920 00:34:23,190 --> 00:34:21,280 throughput experiments over long periods 921 00:34:25,030 --> 00:34:23,200 of time which will probably be very 922 00:34:26,710 --> 00:34:25,040 helpful in the future 923 00:34:28,310 --> 00:34:26,720 and of course as i mentioned our plan 924 00:34:29,750 --> 00:34:28,320 all along was to do this so we could 925 00:34:31,669 --> 00:34:29,760 couple it to more sophisticated 926 00:34:33,510 --> 00:34:31,679 analytical techniques which i'll tell 927 00:34:35,030 --> 00:34:33,520 you about now so really the gold 928 00:34:36,950 --> 00:34:35,040 standard for at least for the kind of 929 00:34:39,190 --> 00:34:36,960 analysis we were trying to do 930 00:34:41,669 --> 00:34:39,200 with these very complex mixtures 931 00:34:43,909 --> 00:34:41,679 is lcm sms so 932 00:34:45,829 --> 00:34:43,919 this is actually just a snapshot of our 933 00:34:47,990 --> 00:34:45,839 mixture that i just talked to you about 934 00:34:49,750 --> 00:34:48,000 after one generation and with quite 935 00:34:51,430 --> 00:34:49,760 stringent filtering we're still dealing 936 00:34:52,629 --> 00:34:51,440 with tens of thousands of unique 937 00:34:55,109 --> 00:34:52,639 features 938 00:34:57,109 --> 00:34:55,119 which is a big mess and making sense of 939 00:34:58,470 --> 00:34:57,119 this data is really difficult 940 00:35:00,790 --> 00:34:58,480 just for those who are curious this is a 941 00:35:02,630 --> 00:35:00,800 uplc msms system 942 00:35:05,670 --> 00:35:02,640 we're doing all of this acquisition in 943 00:35:07,990 --> 00:35:05,680 data dependent mode and this is really 944 00:35:10,230 --> 00:35:08,000 going to enable us to do untargeted 945 00:35:11,750 --> 00:35:10,240 screenings of products and the reason 946 00:35:14,390 --> 00:35:11,760 we're doing this ultimately is we'd like 947 00:35:17,030 --> 00:35:14,400 to be able to track distribution changes 948 00:35:18,550 --> 00:35:17,040 in our products across generations 949 00:35:20,470 --> 00:35:18,560 we're really lucky to benefit from the 950 00:35:21,589 --> 00:35:20,480 expertise of a postdoc stephanie colon 951 00:35:23,270 --> 00:35:21,599 santos 952 00:35:25,270 --> 00:35:23,280 who is an expert in this kind of 953 00:35:27,990 --> 00:35:25,280 analysis of making sense of really 954 00:35:30,310 --> 00:35:28,000 complex mixtures by lcm sms 955 00:35:32,470 --> 00:35:30,320 in mixtures that are relevant to origin 956 00:35:34,230 --> 00:35:32,480 of life questions so things like the 957 00:35:36,470 --> 00:35:34,240 combinatorial explosions you get out of 958 00:35:38,150 --> 00:35:36,480 foremost formamide reactions 959 00:35:39,670 --> 00:35:38,160 so our plan is to apply these methods 960 00:35:41,829 --> 00:35:39,680 that she's been developing to make sense 961 00:35:43,829 --> 00:35:41,839 of our own mixtures and we're really 962 00:35:46,950 --> 00:35:43,839 just starting to do that in this 963 00:35:48,470 --> 00:35:46,960 long-term ecosystem selection experiment 964 00:35:49,910 --> 00:35:48,480 but the other thing that we're trying to 965 00:35:51,990 --> 00:35:49,920 do is 966 00:35:53,750 --> 00:35:52,000 validate or confirm that there is 967 00:35:56,829 --> 00:35:53,760 autocatalysis occurring in these 968 00:35:58,950 --> 00:35:56,839 reactions and also possibly signs of 969 00:36:01,990 --> 00:35:58,960 evolvability and one way we hope to do 970 00:36:03,349 --> 00:36:02,000 this is to identify new products 971 00:36:05,430 --> 00:36:03,359 make standards for them make this 972 00:36:07,589 --> 00:36:05,440 quantitative and track the concentration 973 00:36:09,430 --> 00:36:07,599 and how it's changing across generations 974 00:36:11,190 --> 00:36:09,440 and seeing if we find evidence of super 975 00:36:13,030 --> 00:36:11,200 linear growth which would be indicative 976 00:36:15,109 --> 00:36:13,040 of auto catalytic growth 977 00:36:17,109 --> 00:36:15,119 we'd also can do 978 00:36:19,430 --> 00:36:17,119 things like pca and other data 979 00:36:21,430 --> 00:36:19,440 dimensionality reduction procedures to 980 00:36:24,470 --> 00:36:21,440 get a broad sense of how our different 981 00:36:25,990 --> 00:36:24,480 lineages are behaving across generations 982 00:36:28,150 --> 00:36:26,000 and these are actually just snapshots 983 00:36:30,870 --> 00:36:28,160 from that experiment i just showed you 984 00:36:33,750 --> 00:36:30,880 uh generation 69 and 70. what we're 985 00:36:35,190 --> 00:36:33,760 comparing here are our 24 experimental 986 00:36:37,829 --> 00:36:35,200 lineages 987 00:36:40,069 --> 00:36:37,839 compared to controls that are set up 988 00:36:41,750 --> 00:36:40,079 identically to those lineages with the 989 00:36:43,990 --> 00:36:41,760 same materials but don't have any 990 00:36:45,750 --> 00:36:44,000 history of transfer so we're comparing 991 00:36:48,390 --> 00:36:45,760 samples that have zero transfers versus 992 00:36:50,069 --> 00:36:48,400 ones that have 69 or 70. 993 00:36:52,150 --> 00:36:50,079 and when we do that we see interesting 994 00:36:54,390 --> 00:36:52,160 clustering we see these different 995 00:36:56,069 --> 00:36:54,400 groups we're not entirely sure what the 996 00:36:57,750 --> 00:36:56,079 significance significance of those 997 00:36:59,030 --> 00:36:57,760 groups are yet um we don't see any 998 00:37:00,790 --> 00:36:59,040 evidence of any kind of positional 999 00:37:02,230 --> 00:37:00,800 effects although we recognize there are 1000 00:37:03,670 --> 00:37:02,240 lots of different experimental 1001 00:37:05,430 --> 00:37:03,680 parameters that might explain this 1002 00:37:06,390 --> 00:37:05,440 clustering and we're working on figuring 1003 00:37:07,670 --> 00:37:06,400 that out 1004 00:37:09,670 --> 00:37:07,680 but we also see these interesting 1005 00:37:11,430 --> 00:37:09,680 outliers now the really cool thing would 1006 00:37:13,910 --> 00:37:11,440 have been if those outliers were the 1007 00:37:15,349 --> 00:37:13,920 same in different generations uh that 1008 00:37:17,030 --> 00:37:15,359 would indicate some kind of lineage 1009 00:37:19,990 --> 00:37:17,040 effect which would be a really excellent 1010 00:37:21,190 --> 00:37:20,000 crude level uh or crude way to 1011 00:37:23,430 --> 00:37:21,200 establish whether or not there's any 1012 00:37:25,270 --> 00:37:23,440 kind of history or memory happening here 1013 00:37:27,910 --> 00:37:25,280 that is not happening here at least not 1014 00:37:29,430 --> 00:37:27,920 um in this particular data set um these 1015 00:37:31,190 --> 00:37:29,440 are different lineages the two orange 1016 00:37:33,589 --> 00:37:31,200 dots you see kind of floating off on by 1017 00:37:35,430 --> 00:37:33,599 themselves or not the simulink lineage 1018 00:37:37,349 --> 00:37:35,440 okay 1019 00:37:39,589 --> 00:37:37,359 um so of course more work is needed to 1020 00:37:41,190 --> 00:37:39,599 understand these methods like i said we 1021 00:37:43,190 --> 00:37:41,200 are just in the beginning of making 1022 00:37:45,030 --> 00:37:43,200 sense of these data um here are some 1023 00:37:45,829 --> 00:37:45,040 examples of what we might be looking for 1024 00:37:47,910 --> 00:37:45,839 so 1025 00:37:50,390 --> 00:37:47,920 heritability plots where that allowed us 1026 00:37:52,390 --> 00:37:50,400 to try to plot trait values in one 1027 00:37:53,990 --> 00:37:52,400 generation versus another if we see any 1028 00:37:55,910 --> 00:37:54,000 kind of significant correlation that 1029 00:37:57,829 --> 00:37:55,920 might again be indicative of lineage 1030 00:37:59,829 --> 00:37:57,839 effects we also have a sense of what 1031 00:38:02,550 --> 00:37:59,839 things should look like with respect to 1032 00:38:05,270 --> 00:38:02,560 rate of propagation changes over time if 1033 00:38:07,270 --> 00:38:05,280 there is evolution operating or not 1034 00:38:09,990 --> 00:38:07,280 and so we'll ask you to stay tuned for 1035 00:38:11,750 --> 00:38:10,000 all of that and i will just end by 1036 00:38:13,510 --> 00:38:11,760 saying that we think that the chemical 1037 00:38:14,550 --> 00:38:13,520 ecology model in this experimental 1038 00:38:16,550 --> 00:38:14,560 framework 1039 00:38:19,270 --> 00:38:16,560 can help us resolve some key questions 1040 00:38:21,109 --> 00:38:19,280 about the origins of lifelike processes 1041 00:38:22,550 --> 00:38:21,119 in the absence of prior living processes 1042 00:38:24,550 --> 00:38:22,560 like evolution 1043 00:38:25,990 --> 00:38:24,560 and as a bonus we've also identified 1044 00:38:27,510 --> 00:38:26,000 conditions that provide really 1045 00:38:28,710 --> 00:38:27,520 interesting results 1046 00:38:30,630 --> 00:38:28,720 and so with that i'm just going to 1047 00:38:32,870 --> 00:38:30,640 finish by acknowledging the really large 1048 00:38:34,230 --> 00:38:32,880 group of people that make this possible 1049 00:38:35,750 --> 00:38:34,240 including the baum lab and other 1050 00:38:38,300 --> 00:38:35,760 collaborators both at university 1051 00:38:45,430 --> 00:38:38,310 wisconsin and beyond thank you 1052 00:38:45,440 --> 00:38:50,390 okay thank you so questions for lena 1053 00:38:55,910 --> 00:38:54,390 maybe are there questions online marco 1054 00:38:59,030 --> 00:38:55,920 no no tips 1055 00:39:01,829 --> 00:38:59,040 so i have a question uh so did you try 1056 00:39:03,670 --> 00:39:01,839 to split some lineages and also do you 1057 00:39:05,190 --> 00:39:03,680 see some contingencies in the 1058 00:39:08,150 --> 00:39:05,200 trajectories because when you talk about 1059 00:39:10,710 --> 00:39:08,160 lineages you you mean linear successions 1060 00:39:12,310 --> 00:39:10,720 but did you try to split sound 1061 00:39:13,750 --> 00:39:12,320 uh we have not tried to split any of the 1062 00:39:16,230 --> 00:39:13,760 lineages but that would be a really 1063 00:39:17,190 --> 00:39:16,240 interesting experiment to do um but no 1064 00:39:19,270 --> 00:39:17,200 we haven't tried that we just 1065 00:39:21,510 --> 00:39:19,280 independently propagated these lineages 1066 00:39:24,069 --> 00:39:21,520 vertically and are making sense of those 1067 00:39:27,750 --> 00:39:25,430 we have a question 1068 00:39:30,150 --> 00:39:27,760 a lovely talk so i saw in your slides 1069 00:39:32,069 --> 00:39:30,160 you flashed a little cartoon of an 1070 00:39:33,829 --> 00:39:32,079 orbeez mass spectrometer and i was just 1071 00:39:35,990 --> 00:39:33,839 wondering if you've taken 1072 00:39:37,829 --> 00:39:36,000 a sort of proteomics type of data 1073 00:39:40,390 --> 00:39:37,839 analysis approach to see if you have any 1074 00:39:41,670 --> 00:39:40,400 peptides growing in these slimes no we 1075 00:39:44,310 --> 00:39:41,680 have not but that would be really 1076 00:39:45,990 --> 00:39:44,320 interesting to do um yeah we're just in 1077 00:39:47,910 --> 00:39:46,000 the infancy like i'm not an analytical 1078 00:39:49,190 --> 00:39:47,920 chemist by training uh we actually just 1079 00:39:50,230 --> 00:39:49,200 gained access to this instrument 1080 00:39:51,510 --> 00:39:50,240 recently 1081 00:39:52,710 --> 00:39:51,520 um but yeah that would be really 1082 00:39:56,790 --> 00:39:52,720 interesting to do so i'd love to talk to 1083 00:40:07,030 --> 00:39:58,230 okay okay 1084 00:40:12,390 --> 00:40:10,069 we now welcome philippe reneger 1085 00:40:14,069 --> 00:40:12,400 from the harvard medical school and 1086 00:40:15,910 --> 00:40:14,079 philip is going to tell us about 1087 00:40:18,230 --> 00:40:15,920 efficient identification of auto 1088 00:40:22,950 --> 00:40:18,240 catalysis in chemical and biological 1089 00:40:27,910 --> 00:40:25,270 all right thank you for attending my 1090 00:40:31,190 --> 00:40:27,920 talk at the last day of the conference 1091 00:40:32,870 --> 00:40:31,200 i'll tell you a bit how we identify 1092 00:40:35,990 --> 00:40:32,880 auto catalysis in chemical and 1093 00:40:37,750 --> 00:40:36,000 biological networks with our efficient 1094 00:40:39,990 --> 00:40:37,760 sort of algorithm 1095 00:40:41,550 --> 00:40:40,000 so first i will 1096 00:40:44,470 --> 00:40:41,560 introduce the importance of 1097 00:40:46,710 --> 00:40:44,480 autocatalysis so autocatalysis as i'm 1098 00:40:48,790 --> 00:40:46,720 sure most of you are aware means that 1099 00:40:50,790 --> 00:40:48,800 there are chemical reactions where the 1100 00:40:53,510 --> 00:40:50,800 product is also the catalyst for the 1101 00:40:56,630 --> 00:40:53,520 reaction or where differently the 1102 00:40:58,950 --> 00:40:56,640 catalyst makes copies of itself which is 1103 00:41:01,990 --> 00:40:58,960 of course an important concept in the 1104 00:41:04,069 --> 00:41:02,000 field of abiogenesis because this type 1105 00:41:05,750 --> 00:41:04,079 of reaction is very stable against 1106 00:41:08,309 --> 00:41:05,760 environmental shocks and molecular 1107 00:41:11,349 --> 00:41:08,319 degradation and it also plays a major 1108 00:41:13,910 --> 00:41:11,359 role in many models how abiogenesis 1109 00:41:16,069 --> 00:41:13,920 could work because this is a chemical 1110 00:41:18,550 --> 00:41:16,079 analog of a system that is able to feed 1111 00:41:21,349 --> 00:41:18,560 on input and grow and reproduce 1112 00:41:23,990 --> 00:41:21,359 and in a way even life as it exists 1113 00:41:26,309 --> 00:41:24,000 today could be seen like that because 1114 00:41:30,470 --> 00:41:26,319 living organisms eat 1115 00:41:34,069 --> 00:41:30,480 sustain themselves grow and reproduce 1116 00:41:35,270 --> 00:41:34,079 so here are some favorite autocality 1117 00:41:37,750 --> 00:41:35,280 cycles 1118 00:41:40,390 --> 00:41:37,760 in the biological fields on the left 1119 00:41:42,230 --> 00:41:40,400 hand side you see the reductive citric 1120 00:41:44,790 --> 00:41:42,240 acid cycle which 1121 00:41:45,829 --> 00:41:44,800 absorbs carbon dioxide to make more of 1122 00:41:47,510 --> 00:41:45,839 itself 1123 00:41:50,390 --> 00:41:47,520 and on the right hand side you see the 1124 00:41:51,510 --> 00:41:50,400 glyoxylate cycle which is also based on 1125 00:41:55,030 --> 00:41:51,520 the 1126 00:41:58,150 --> 00:41:55,040 citric acid cycle but instead of 1127 00:41:59,190 --> 00:41:58,160 producing two equivalents of formal 1128 00:42:02,550 --> 00:41:59,200 for 1129 00:42:05,750 --> 00:42:02,560 carbon dioxide it makes dioxide and does 1130 00:42:07,270 --> 00:42:05,760 add it copies the catalytic species 1131 00:42:09,750 --> 00:42:07,280 itself 1132 00:42:12,390 --> 00:42:09,760 and there are also chemical analogs i 1133 00:42:14,470 --> 00:42:12,400 decided to use as an example the formos 1134 00:42:16,710 --> 00:42:14,480 reaction which is probably most familiar 1135 00:42:18,470 --> 00:42:16,720 to this community 1136 00:42:21,349 --> 00:42:18,480 in essence 1137 00:42:23,589 --> 00:42:21,359 form aldehyde polymerizes to 1138 00:42:25,670 --> 00:42:23,599 carbohydrates carbohydrate-like 1139 00:42:28,230 --> 00:42:25,680 structures and here are two of the 1140 00:42:30,550 --> 00:42:28,240 cycles because the form hosa system is 1141 00:42:32,790 --> 00:42:30,560 an actually rather complicated 1142 00:42:35,270 --> 00:42:32,800 convolution of auto catalytic cycles of 1143 00:42:37,030 --> 00:42:35,280 which a few are very active and most are 1144 00:42:40,069 --> 00:42:37,040 not 1145 00:42:42,309 --> 00:42:40,079 so when we look at all of these cycles 1146 00:42:44,790 --> 00:42:42,319 can we find commonalities 1147 00:42:47,829 --> 00:42:44,800 well the first and really obvious one 1148 00:42:49,750 --> 00:42:47,839 they're all of a cyclic structure 1149 00:42:51,829 --> 00:42:49,760 the second one is that in all of those 1150 00:42:53,910 --> 00:42:51,839 cycles you will see there is some 1151 00:42:57,030 --> 00:42:53,920 species some molecular species that 1152 00:42:59,670 --> 00:42:57,040 splits into two parts that are still in 1153 00:43:02,630 --> 00:42:59,680 the cycle i call this a quasi-dimer it 1154 00:43:04,069 --> 00:43:02,640 doesn't need to be dimeric but it is 1155 00:43:06,470 --> 00:43:04,079 able to break 1156 00:43:08,470 --> 00:43:06,480 and what you will also always see is 1157 00:43:11,670 --> 00:43:08,480 that there is some molecular species 1158 00:43:14,150 --> 00:43:11,680 these two fragments are united into 1159 00:43:16,790 --> 00:43:14,160 and it is not a coincidence that you see 1160 00:43:18,710 --> 00:43:16,800 these motives in all of these cycles i 1161 00:43:19,990 --> 00:43:18,720 presented before those are necessary 1162 00:43:22,309 --> 00:43:20,000 conditions 1163 00:43:25,030 --> 00:43:22,319 a catalytic cycle must fulfill in order 1164 00:43:27,349 --> 00:43:25,040 to be auto catalytic 1165 00:43:29,829 --> 00:43:27,359 so but if you look at this from a graph 1166 00:43:32,309 --> 00:43:29,839 algorithmic perspective this suggests a 1167 00:43:34,230 --> 00:43:32,319 detection algorithm doesn't it 1168 00:43:36,790 --> 00:43:34,240 let's pretend this grey blob on the 1169 00:43:38,790 --> 00:43:36,800 right hand side is some reaction network 1170 00:43:42,630 --> 00:43:38,800 you're interested in whether biological 1171 00:43:44,950 --> 00:43:42,640 or primordial or strictly chemical 1172 00:43:47,349 --> 00:43:44,960 how could you now find autocatalytic 1173 00:43:48,470 --> 00:43:47,359 cycles with what you have seen 1174 00:43:50,790 --> 00:43:48,480 first 1175 00:43:53,190 --> 00:43:50,800 go through all the reactions and find 1176 00:43:55,270 --> 00:43:53,200 those that have more than one product 1177 00:43:57,270 --> 00:43:55,280 this would be the dimer splitting 1178 00:43:59,829 --> 00:43:57,280 reactions 1179 00:44:01,910 --> 00:43:59,839 then find all of the molecules that can 1180 00:44:04,230 --> 00:44:01,920 be produced by at least two different 1181 00:44:07,270 --> 00:44:04,240 ways or at least with a coefficient 1182 00:44:09,349 --> 00:44:07,280 greater than one 1183 00:44:11,190 --> 00:44:09,359 then look for all the paths in the 1184 00:44:14,069 --> 00:44:11,200 network that combine the 1185 00:44:16,710 --> 00:44:14,079 lead from the reaction to the molecule 1186 00:44:18,069 --> 00:44:16,720 and here is one of the problems of the 1187 00:44:20,790 --> 00:44:18,079 algorithm 1188 00:44:22,550 --> 00:44:20,800 while finding the shortest path 1189 00:44:25,510 --> 00:44:22,560 is efficient 1190 00:44:26,630 --> 00:44:25,520 finding all paths is a combinatorial 1191 00:44:28,950 --> 00:44:26,640 problem 1192 00:44:31,990 --> 00:44:28,960 so we often need to regularize for 1193 00:44:35,270 --> 00:44:32,000 example by deciding we only want paths 1194 00:44:39,670 --> 00:44:37,750 and the fourth part of the algorithm 1195 00:44:41,510 --> 00:44:39,680 would be then to find all paths that 1196 00:44:43,109 --> 00:44:41,520 lead from the molecule back to the 1197 00:44:45,349 --> 00:44:43,119 reaction 1198 00:44:47,589 --> 00:44:45,359 and if you have more than two of the 1199 00:44:49,430 --> 00:44:47,599 paths from reaction to molecule and at 1200 00:44:50,710 --> 00:44:49,440 least one from the molecule to the 1201 00:44:53,910 --> 00:44:50,720 reaction 1202 00:44:57,349 --> 00:44:53,920 then as this pseudo code shows you have 1203 00:44:58,550 --> 00:44:57,359 found a possible auto catalytic cycle 1204 00:45:02,790 --> 00:44:58,560 basic 1205 00:45:06,150 --> 00:45:02,800 just based on the graph topology alone 1206 00:45:09,270 --> 00:45:06,160 so we looked for use cases of this 1207 00:45:11,109 --> 00:45:09,280 and we used terrestrial biological 1208 00:45:13,349 --> 00:45:11,119 networks because those are the largest 1209 00:45:15,670 --> 00:45:13,359 and most challenging ones 1210 00:45:17,829 --> 00:45:15,680 and so we went through the big database 1211 00:45:20,870 --> 00:45:17,839 which is openly accessible and went 1212 00:45:25,270 --> 00:45:20,880 through all of these different organisms 1213 00:45:26,230 --> 00:45:25,280 and ran our algorithm so this 1214 00:45:28,390 --> 00:45:26,240 this 1215 00:45:30,150 --> 00:45:28,400 column here shows the metabolites that 1216 00:45:32,550 --> 00:45:30,160 are not in the 1217 00:45:34,630 --> 00:45:32,560 energy currency system these are the 1218 00:45:36,390 --> 00:45:34,640 metabolites that are in the energy 1219 00:45:40,470 --> 00:45:36,400 currency system 1220 00:45:43,030 --> 00:45:40,480 atp adp amp and so on 1221 00:45:45,910 --> 00:45:43,040 and you see that we find up to dozens of 1222 00:45:47,190 --> 00:45:45,920 different auto catalytic cycles for each 1223 00:45:49,589 --> 00:45:47,200 of those 1224 00:45:51,750 --> 00:45:49,599 metabolic networks 1225 00:45:53,589 --> 00:45:51,760 the bracketed number here means we have 1226 00:45:56,470 --> 00:45:53,599 organized the 1227 00:45:59,109 --> 00:45:56,480 raw number of autocatalytic cycles into 1228 00:46:01,550 --> 00:45:59,119 sets where all the metabolites are equal 1229 00:46:04,790 --> 00:46:01,560 for example you see here are normal 1230 00:46:07,030 --> 00:46:04,800 anomalously large amounts 1231 00:46:09,349 --> 00:46:07,040 like 800 different cycles that's because 1232 00:46:13,349 --> 00:46:09,359 they include some enzyme that performs a 1233 00:46:15,109 --> 00:46:13,359 very common reaction like atp to adp 1234 00:46:17,270 --> 00:46:15,119 which can be implemented by a whole 1235 00:46:19,589 --> 00:46:17,280 plethora of different enzymes but it's 1236 00:46:22,150 --> 00:46:19,599 basically the same auto catalytic cycles 1237 00:46:26,150 --> 00:46:22,160 so the bracketed number here shows the 1238 00:46:32,069 --> 00:46:29,349 so as i said we had to regula regularize 1239 00:46:34,309 --> 00:46:32,079 the number of path lengths 1240 00:46:35,750 --> 00:46:34,319 because otherwise this combinatorial 1241 00:46:37,990 --> 00:46:35,760 algorithm 1242 00:46:39,430 --> 00:46:38,000 calculates for an indeterminable amount 1243 00:46:41,510 --> 00:46:39,440 of time 1244 00:46:43,190 --> 00:46:41,520 and maybe you have noticed this 1245 00:46:45,910 --> 00:46:43,200 little anomaly here in the field 1246 00:46:49,349 --> 00:46:45,920 dactylum where we suddenly find almost 8 1247 00:46:51,589 --> 00:46:49,359 000 different autocatalytic cycles 1248 00:46:54,550 --> 00:46:51,599 and upon closer inspection we found that 1249 00:46:57,270 --> 00:46:54,560 the network entry in the big database 1250 00:46:59,270 --> 00:46:57,280 has some very important errors in the 1251 00:47:01,109 --> 00:46:59,280 iron ii iron three 1252 00:47:02,710 --> 00:47:01,119 assignments which is adduct and which is 1253 00:47:05,190 --> 00:47:02,720 product and this in 1254 00:47:07,510 --> 00:47:05,200 induce this huge number of 1255 00:47:10,309 --> 00:47:07,520 fake cycles and below here you see the 1256 00:47:13,109 --> 00:47:10,319 corrected network which has an 1257 00:47:14,630 --> 00:47:13,119 expectedly low number of auto catalytic 1258 00:47:19,829 --> 00:47:14,640 cycles 1259 00:47:20,549 --> 00:47:19,839 like if jerry picked a few examples for 1260 00:47:23,030 --> 00:47:20,559 you 1261 00:47:25,670 --> 00:47:23,040 and i must stress that we do not know if 1262 00:47:28,069 --> 00:47:25,680 these auto catalytic cycles actually run 1263 00:47:30,549 --> 00:47:28,079 like that in the metabolism all we are 1264 00:47:33,109 --> 00:47:30,559 saying is that the topological features 1265 00:47:36,790 --> 00:47:33,119 of autocatalysis are there 1266 00:47:38,710 --> 00:47:36,800 but of course enzyme control 1267 00:47:41,510 --> 00:47:38,720 adds a whole new dimension we do not 1268 00:47:43,349 --> 00:47:41,520 consider at this stage for example here 1269 00:47:47,670 --> 00:47:43,359 are some carbohydrate-based 1270 00:47:50,390 --> 00:47:47,680 auto-catalytic cycles in homo sapiens 1271 00:47:52,950 --> 00:47:50,400 this one i found as a chemist kind of 1272 00:47:56,790 --> 00:47:52,960 curious because this is a terpenoid auto 1273 00:47:57,990 --> 00:47:56,800 catalytic cycle we found in homo sapiens 1274 00:47:59,990 --> 00:47:58,000 where 1275 00:48:02,069 --> 00:48:00,000 farnell 1276 00:48:03,990 --> 00:48:02,079 pure phosphate 1277 00:48:05,349 --> 00:48:04,000 assembles c5 1278 00:48:08,390 --> 00:48:05,359 bodies into 1279 00:48:10,710 --> 00:48:08,400 into more of itself 1280 00:48:14,950 --> 00:48:10,720 and here is one we found in the energy 1281 00:48:16,549 --> 00:48:14,960 currency metabolism where amp adp atp as 1282 00:48:18,390 --> 00:48:16,559 a set 1283 00:48:20,630 --> 00:48:18,400 catalyze the 1284 00:48:23,510 --> 00:48:20,640 absorption of adenosine 1285 00:48:26,710 --> 00:48:23,520 and of pep to make more 1286 00:48:28,470 --> 00:48:26,720 of adp to fuse basically those 1287 00:48:31,430 --> 00:48:28,480 and those 1288 00:48:33,990 --> 00:48:31,440 and pay special attention here we also 1289 00:48:35,589 --> 00:48:34,000 take one phosphate unit from gtp and 1290 00:48:37,829 --> 00:48:35,599 make gdp 1291 00:48:40,390 --> 00:48:37,839 because we found another system that has 1292 00:48:43,349 --> 00:48:40,400 this as a subsystem 1293 00:48:46,549 --> 00:48:43,359 and here is interesting because gdp is 1294 00:48:49,510 --> 00:48:46,559 recycled back to gtp 1295 00:48:51,270 --> 00:48:49,520 and so here gtp is necessary in a 1296 00:48:52,870 --> 00:48:51,280 stoichiometric amount to run 1297 00:48:55,670 --> 00:48:52,880 autocatalytically 1298 00:48:57,670 --> 00:48:55,680 but here it is only a catalytic amount 1299 00:48:59,990 --> 00:48:57,680 and the interesting thing here is the 1300 00:49:01,670 --> 00:49:00,000 number of auto catalytic cycles that run 1301 00:49:04,069 --> 00:49:01,680 simultaneously 1302 00:49:06,710 --> 00:49:04,079 will be controlled by the number of gdp 1303 00:49:12,549 --> 00:49:06,720 plus gdp that is available so this is 1304 00:49:17,750 --> 00:49:15,670 so here we also correlated the 1305 00:49:19,510 --> 00:49:17,760 autotrophic 1306 00:49:21,910 --> 00:49:19,520 property of 1307 00:49:24,069 --> 00:49:21,920 these organisms as determined by various 1308 00:49:26,790 --> 00:49:24,079 publications we found 1309 00:49:29,750 --> 00:49:26,800 to the number of auto catalytic cycles 1310 00:49:31,349 --> 00:49:29,760 divided by metabolic by 1311 00:49:34,309 --> 00:49:31,359 size to have a 1312 00:49:37,990 --> 00:49:34,319 entity of metabolic density and what we 1313 00:49:40,630 --> 00:49:38,000 find that the autotrophic 1314 00:49:42,549 --> 00:49:40,640 organisms have a higher density of auto 1315 00:49:44,870 --> 00:49:42,559 catalytic cycles 1316 00:49:46,870 --> 00:49:44,880 so 1317 00:49:49,750 --> 00:49:46,880 not sure if this has a 1318 00:49:50,950 --> 00:49:49,760 importance but it is there 1319 00:49:53,829 --> 00:49:50,960 and 1320 00:49:56,950 --> 00:49:53,839 what i want to say in the last part of 1321 00:49:59,430 --> 00:49:56,960 my talk is pattern auto catalysis 1322 00:50:02,150 --> 00:49:59,440 because so far in the 1323 00:50:03,910 --> 00:50:02,160 most in the most communities 1324 00:50:05,670 --> 00:50:03,920 autocatalysis is treated like a 1325 00:50:07,829 --> 00:50:05,680 molecular property but you need to 1326 00:50:08,790 --> 00:50:07,839 realize that chemistry does not happen 1327 00:50:11,030 --> 00:50:08,800 on the 1328 00:50:13,349 --> 00:50:11,040 level of entire molecules it happens on 1329 00:50:15,430 --> 00:50:13,359 the level of functional groups 1330 00:50:17,750 --> 00:50:15,440 so if you look at this autocatalytic 1331 00:50:20,390 --> 00:50:17,760 cycle i constructed it it is not 1332 00:50:22,470 --> 00:50:20,400 empirical but it is based on theoretical 1333 00:50:25,349 --> 00:50:22,480 predictions 1334 00:50:27,589 --> 00:50:25,359 where a primary amide catalyzes the 1335 00:50:31,349 --> 00:50:27,599 hydrolysis of 1336 00:50:33,190 --> 00:50:31,359 hydrogen cyanide to formamide 1337 00:50:35,109 --> 00:50:33,200 but if you really look under the hood 1338 00:50:37,030 --> 00:50:35,119 and you look at the involved functional 1339 00:50:38,309 --> 00:50:37,040 groups you will see what actually 1340 00:50:41,030 --> 00:50:38,319 happens 1341 00:50:42,790 --> 00:50:41,040 is that a primary amide catalyzes the 1342 00:50:44,549 --> 00:50:42,800 hydrolysis 1343 00:50:46,870 --> 00:50:44,559 of a nitrile 1344 00:50:49,510 --> 00:50:46,880 to another 1345 00:50:51,829 --> 00:50:49,520 primary amide so in the end the 1346 00:50:54,150 --> 00:50:51,839 concentration of the active functional 1347 00:50:56,950 --> 00:50:54,160 group does increase even if the 1348 00:50:59,109 --> 00:50:56,960 molecular auto catalytic cycle does not 1349 00:51:02,069 --> 00:50:59,119 directly show you this 1350 00:51:03,990 --> 00:51:02,079 so we suspect that even below all of 1351 00:51:06,630 --> 00:51:04,000 these auto catalytic cycles that we 1352 00:51:09,349 --> 00:51:06,640 found so far there is another layer of 1353 00:51:11,750 --> 00:51:09,359 shadow metabolism where auto catalytic 1354 00:51:14,150 --> 00:51:11,760 cycles might exist that we are not even 1355 00:51:15,990 --> 00:51:14,160 aware of yet 1356 00:51:26,710 --> 00:51:16,000 so with that i come to the end of my 1357 00:51:32,870 --> 00:51:29,990 okay some questions for philip 1358 00:51:34,710 --> 00:51:32,880 i can yeah 1359 00:51:37,430 --> 00:51:34,720 hi anthony burnett here from georgia 1360 00:51:40,069 --> 00:51:37,440 tech and i was wondering so when you 1361 00:51:42,230 --> 00:51:40,079 were looking through networks for 1362 00:51:43,670 --> 00:51:42,240 auto catalytic loops sorry 1363 00:51:44,870 --> 00:51:43,680 it was like you were looking at species 1364 00:51:46,390 --> 00:51:44,880 whether they were 1365 00:51:48,870 --> 00:51:46,400 autotrophic heterotrophic all these 1366 00:51:51,910 --> 00:51:48,880 things any thought of extending this 1367 00:51:54,470 --> 00:51:51,920 kind of analysis to metagenomes or 1368 00:51:57,589 --> 00:51:54,480 ecosystems of microbes 1369 00:51:59,510 --> 00:51:57,599 oh no we didn't do any of that we kept 1370 00:52:01,430 --> 00:51:59,520 it to the level of metabolic networks 1371 00:52:03,910 --> 00:52:01,440 and the reason is simply that i don't 1372 00:52:06,549 --> 00:52:03,920 know anything about that i'm a chemist 1373 00:52:08,390 --> 00:52:06,559 and a programmer by training so 1374 00:52:09,990 --> 00:52:08,400 we kept it at the level of pure 1375 00:52:11,990 --> 00:52:10,000 metabolites so far but it's an 1376 00:52:15,670 --> 00:52:12,000 interesting suggestion 1377 00:52:19,829 --> 00:52:17,750 hello uh first of all thank you for your 1378 00:52:21,990 --> 00:52:19,839 talk it was great um my name is tim i'm 1379 00:52:23,670 --> 00:52:22,000 from the university of wisconsin-madison 1380 00:52:26,710 --> 00:52:23,680 uh and i was wondering about your 1381 00:52:29,270 --> 00:52:26,720 finding with um the greater abundance of 1382 00:52:30,950 --> 00:52:29,280 autocatalysis in autotrophs 1383 00:52:33,190 --> 00:52:30,960 do you know which 1384 00:52:34,710 --> 00:52:33,200 categories of pathways could that be 1385 00:52:36,870 --> 00:52:34,720 responsible for and could it be an 1386 00:52:39,109 --> 00:52:36,880 artifact of coupling between carbon 1387 00:52:41,650 --> 00:52:39,119 fixation and some other 1388 00:52:42,870 --> 00:52:41,660 pathways like respiration 1389 00:52:45,190 --> 00:52:42,880 [Music] 1390 00:52:46,470 --> 00:52:45,200 we haven't even thought about that yet 1391 00:52:49,109 --> 00:52:46,480 but it 1392 00:52:50,710 --> 00:52:49,119 sounds interesting so no i cannot answer 1393 00:52:55,349 --> 00:52:50,720 that sorry 1394 00:53:00,470 --> 00:52:58,069 georgia tech so my question is you uh 1395 00:53:03,589 --> 00:53:00,480 you've done analysis on 1396 00:53:04,390 --> 00:53:03,599 networks using modern metabolic networks 1397 00:53:07,829 --> 00:53:04,400 yes 1398 00:53:10,309 --> 00:53:07,839 there was a paper a few years ago by 1399 00:53:12,069 --> 00:53:10,319 hyman hartmann and segre from boston 1400 00:53:14,470 --> 00:53:12,079 university 1401 00:53:17,270 --> 00:53:14,480 who attempted to show some 1402 00:53:20,870 --> 00:53:17,280 uh remnants of uh 1403 00:53:23,270 --> 00:53:20,880 catalytic networks without phosphates 1404 00:53:26,790 --> 00:53:23,280 uh so can you have you thought about 1405 00:53:31,670 --> 00:53:26,800 exploring going that level and exploring 1406 00:53:36,069 --> 00:53:34,870 your networks in at a much simpler 1407 00:53:37,190 --> 00:53:36,079 metabolic 1408 00:53:39,270 --> 00:53:37,200 level 1409 00:53:41,910 --> 00:53:39,280 oh absolutely we are planning on working 1410 00:53:43,829 --> 00:53:41,920 with daniel secret on similar questions 1411 00:53:45,910 --> 00:53:43,839 and using smaller networks would be 1412 00:53:48,309 --> 00:53:45,920 especially useful for us because then we 1413 00:53:49,510 --> 00:53:48,319 don't have to restrict our algorithm so 1414 00:53:50,309 --> 00:53:49,520 strictly 1415 00:53:52,790 --> 00:53:50,319 so 1416 00:53:56,549 --> 00:53:52,800 yes that is what we plan next 1417 00:53:56,559 --> 00:54:00,870 online questions maybe 1418 00:54:05,510 --> 00:54:04,309 and well i have a question so you um 1419 00:54:07,030 --> 00:54:05,520 you say the 1420 00:54:08,710 --> 00:54:07,040 rightly so that it's a necessary 1421 00:54:11,750 --> 00:54:08,720 condition only so 1422 00:54:14,150 --> 00:54:11,760 how could we focus as experimentalists 1423 00:54:17,030 --> 00:54:14,160 our attention on more specific cycles 1424 00:54:19,270 --> 00:54:17,040 that could be sufficiently 1425 00:54:21,109 --> 00:54:19,280 sufficient conditions for the catalyst 1426 00:54:23,270 --> 00:54:21,119 well for that we would have to do more 1427 00:54:24,390 --> 00:54:23,280 refinement because as you pointed out 1428 00:54:27,190 --> 00:54:24,400 correctly 1429 00:54:30,069 --> 00:54:27,200 our graph topological search 1430 00:54:32,069 --> 00:54:30,079 is just the 1431 00:54:35,349 --> 00:54:32,079 it's just a pre-screening because it is 1432 00:54:36,390 --> 00:54:35,359 a necessary but not sufficient condition 1433 00:54:38,710 --> 00:54:36,400 so 1434 00:54:41,030 --> 00:54:38,720 with regards to the experiment 1435 00:54:43,190 --> 00:54:41,040 what this detection can just do is point 1436 00:54:45,270 --> 00:54:43,200 out the auto catalytic cycles that could 1437 00:54:47,190 --> 00:54:45,280 be there you need to 1438 00:54:49,910 --> 00:54:47,200 you include additional data in 1439 00:54:52,309 --> 00:54:49,920 subsequent analysis 1440 00:54:54,390 --> 00:54:52,319 to make that distinction which ones are 1441 00:54:57,109 --> 00:54:54,400 interesting for example by 1442 00:55:00,789 --> 00:54:57,119 using consistent thermodynamic and or 1443 00:55:10,549 --> 00:55:02,549 thank you 1444 00:55:15,270 --> 00:55:13,530 so now we have an online talk 1445 00:55:17,510 --> 00:55:15,280 [Music] 1446 00:55:19,670 --> 00:55:17,520 by armin kiyani 1447 00:55:21,030 --> 00:55:19,680 so let's sign 1448 00:55:22,870 --> 00:55:21,040 display uh 1449 00:55:23,910 --> 00:55:22,880 i mean 1450 00:55:26,230 --> 00:55:23,920 yes 1451 00:55:28,150 --> 00:55:26,240 so uh armin from the university of 1452 00:55:30,069 --> 00:55:28,160 renningen is going to tell us about 1453 00:55:32,549 --> 00:55:30,079 integrating compartmentalization 1454 00:55:35,349 --> 00:55:32,559 metabolism and self-replication 1455 00:55:37,430 --> 00:55:35,359 the world de novo life 1456 00:55:39,109 --> 00:55:37,440 uh hi everyone my name is armin i'm 1457 00:55:41,030 --> 00:55:39,119 doing my phd at the university of 1458 00:55:42,470 --> 00:55:41,040 toronto again in the netherlands today 1459 00:55:45,270 --> 00:55:42,480 i'm going to talk to you about some of 1460 00:55:46,309 --> 00:55:45,280 the work i've been doing on integration 1461 00:55:49,990 --> 00:55:46,319 of 1462 00:55:50,950 --> 00:55:50,000 system 1463 00:55:54,230 --> 00:55:50,960 so 1464 00:55:55,910 --> 00:55:54,240 life has been known as 1465 00:55:59,510 --> 00:55:55,920 life as we know it is composed of 1466 00:56:02,549 --> 00:55:59,520 non-living methods like dna protein or 1467 00:56:07,510 --> 00:56:02,559 lipids however when these molecules are 1468 00:56:12,870 --> 00:56:10,549 in these two states is complex of 1469 00:56:14,950 --> 00:56:12,880 chemical reactions that can give rise to 1470 00:56:18,150 --> 00:56:14,960 some emergent behavior that we know it 1471 00:56:20,789 --> 00:56:18,160 as live so if we change the initial 1472 00:56:22,390 --> 00:56:20,799 condition or the initial molecules 1473 00:56:23,349 --> 00:56:22,400 probably we have different kinds of 1474 00:56:25,510 --> 00:56:23,359 chemical 1475 00:56:28,390 --> 00:56:25,520 reaction network and also different 1476 00:56:30,470 --> 00:56:28,400 emergent properties so the question is 1477 00:56:31,990 --> 00:56:30,480 is it possible to have life based on 1478 00:56:33,190 --> 00:56:32,000 different chemistry 1479 00:56:34,870 --> 00:56:33,200 um 1480 00:56:38,789 --> 00:56:34,880 we don't know because we have only one 1481 00:56:40,470 --> 00:56:38,799 example of life in there so we uh in our 1482 00:56:41,750 --> 00:56:40,480 group as well as many other gurus we are 1483 00:56:43,990 --> 00:56:41,760 trying to 1484 00:56:45,510 --> 00:56:44,000 make kind of fully synthetic life or de 1485 00:56:47,430 --> 00:56:45,520 novo life 1486 00:56:49,109 --> 00:56:47,440 add another example of 1487 00:56:51,109 --> 00:56:49,119 life indeed so 1488 00:56:53,990 --> 00:56:51,119 in order to make innova life we need to 1489 00:56:56,630 --> 00:56:54,000 focus on phenomena of life instead of 1490 00:56:59,109 --> 00:56:56,640 the example of life that we have so what 1491 00:57:00,870 --> 00:56:59,119 is what life is is a very uh 1492 00:57:03,190 --> 00:57:00,880 long-standing unanswered question 1493 00:57:04,789 --> 00:57:03,200 however we can describe life with its 1494 00:57:07,190 --> 00:57:04,799 features like metabolism 1495 00:57:09,510 --> 00:57:07,200 self-replication compartmentalization in 1496 00:57:11,589 --> 00:57:09,520 a regime for from equilibrium and also 1497 00:57:12,390 --> 00:57:11,599 capable of undergoing darwinian 1498 00:57:14,870 --> 00:57:12,400 evolution 1499 00:57:16,150 --> 00:57:14,880 so it is believed that if we can make 1500 00:57:17,750 --> 00:57:16,160 each of these 1501 00:57:19,829 --> 00:57:17,760 features synthetically and then 1502 00:57:23,190 --> 00:57:19,839 integrate all of them in one single 1503 00:57:27,030 --> 00:57:23,200 system what we can get uh one we get can 1504 00:57:29,430 --> 00:57:27,040 be called life probably so 1505 00:57:32,549 --> 00:57:29,440 that many different groups have tried to 1506 00:57:35,030 --> 00:57:32,559 make each of these properties separately 1507 00:57:37,030 --> 00:57:35,040 or integration of some of them among 1508 00:57:39,670 --> 00:57:37,040 them it seems that a server application 1509 00:57:42,069 --> 00:57:39,680 is a very good candidate because uh 1510 00:57:43,750 --> 00:57:42,079 living system need to make copy of 1511 00:57:45,430 --> 00:57:43,760 itself 1512 00:57:46,870 --> 00:57:45,440 so a few uh fully synthetic 1513 00:57:49,510 --> 00:57:46,880 self-replicating molecules have been 1514 00:57:52,150 --> 00:57:49,520 reported so far one of them by our group 1515 00:57:53,750 --> 00:57:52,160 a decade ago in which is work based on 1516 00:57:57,349 --> 00:57:53,760 dynamic combination or chemistry 1517 00:57:59,589 --> 00:57:57,359 approach uh the main molecule is this 1518 00:58:03,510 --> 00:57:59,599 molecule 1519 00:58:05,510 --> 00:58:03,520 with two tiles which is decorated with 1520 00:58:08,230 --> 00:58:05,520 endopeptide which are alternating 1521 00:58:10,870 --> 00:58:08,240 hydrophobic hydrophilic uh mine acid 1522 00:58:12,870 --> 00:58:10,880 open oxidation this tile group will be 1523 00:58:15,829 --> 00:58:12,880 oxidized to 1524 00:58:17,589 --> 00:58:15,839 disulfide bonds and make different micro 1525 00:58:19,990 --> 00:58:17,599 cycles and all of them are input in 1526 00:58:21,750 --> 00:58:20,000 equilibrium together however when one of 1527 00:58:24,230 --> 00:58:21,760 this micro cycle for example hexamer 1528 00:58:25,990 --> 00:58:24,240 here can stack on top of each other so 1529 00:58:28,150 --> 00:58:26,000 it draws all equilibrium towards 1530 00:58:31,670 --> 00:58:28,160 formation of itself in a autocatalytic 1531 00:58:34,069 --> 00:58:31,680 process and this fiber will grow 1532 00:58:35,990 --> 00:58:34,079 elongate and if we 1533 00:58:38,309 --> 00:58:36,000 mechanically agitate the system this 1534 00:58:40,549 --> 00:58:38,319 fiber will break and each of them can 1535 00:58:41,349 --> 00:58:40,559 grow again so the whole process is kind 1536 00:58:43,510 --> 00:58:41,359 of 1537 00:58:46,150 --> 00:58:43,520 autocatholic 1538 00:58:49,430 --> 00:58:46,160 or exponential step replication so 1539 00:58:51,349 --> 00:58:49,440 another feature of life is metabolism 1540 00:58:54,309 --> 00:58:51,359 a few system in which 1541 00:58:56,470 --> 00:58:54,319 replicator and metabolism are integrated 1542 00:58:57,990 --> 00:58:56,480 have been reported a few of them in our 1543 00:59:01,190 --> 00:58:58,000 group i am going to show one of them 1544 00:59:03,750 --> 00:59:01,200 that i'm going to come back to later so 1545 00:59:06,069 --> 00:59:03,760 we showed that our replicators are able 1546 00:59:07,910 --> 00:59:06,079 to recruit um 1547 00:59:10,150 --> 00:59:07,920 cofactor which is performing here which 1548 00:59:13,270 --> 00:59:10,160 is not active in the solution but as 1549 00:59:15,430 --> 00:59:13,280 soon as it incorporates inside the 1550 00:59:17,750 --> 00:59:15,440 replicator it would be activated and by 1551 00:59:20,630 --> 00:59:17,760 shining light it converts triple oxygen 1552 00:59:23,030 --> 00:59:20,640 to single oxygen then single oxygen is 1553 00:59:25,270 --> 00:59:23,040 very active oxidizing agent it can 1554 00:59:27,430 --> 00:59:25,280 accelerate oxidation of monomer to 1555 00:59:29,829 --> 00:59:27,440 trimer testomer or 1556 00:59:32,390 --> 00:59:29,839 other microcycle and accelerate the 1557 00:59:33,190 --> 00:59:32,400 replication so indeed in this system we 1558 00:59:36,549 --> 00:59:33,200 have 1559 00:59:38,549 --> 00:59:36,559 our replicators are able to make our own 1560 00:59:40,710 --> 00:59:38,559 uh their own food 1561 00:59:42,789 --> 00:59:40,720 so it's the second feature the first 1562 00:59:44,710 --> 00:59:42,799 feature of life is compartmentalization 1563 00:59:47,990 --> 00:59:44,720 life is compartmentalized nerves to 1564 00:59:50,150 --> 00:59:48,000 protect itself from the environment so 1565 00:59:51,670 --> 00:59:50,160 it is the main topic of my research and 1566 00:59:52,870 --> 00:59:51,680 also the main topic that i'm going to 1567 00:59:54,069 --> 00:59:52,880 talk to you 1568 00:59:56,230 --> 00:59:54,079 today 1569 00:59:58,069 --> 00:59:56,240 so in order to make 1570 01:00:01,510 --> 00:59:58,079 integrate compartmentalization with 1571 01:00:03,510 --> 01:00:01,520 replication and metabolism we need a 1572 01:00:05,910 --> 01:00:03,520 compartment with some very special 1573 01:00:08,230 --> 01:00:05,920 features first of all our replicators 1574 01:00:10,390 --> 01:00:08,240 should be able to have positive feedback 1575 01:00:11,589 --> 01:00:10,400 on formation of compartment compartment 1576 01:00:13,109 --> 01:00:11,599 material 1577 01:00:15,190 --> 01:00:13,119 and also 1578 01:00:17,109 --> 01:00:15,200 the compartment material itself should 1579 01:00:20,630 --> 01:00:17,119 be able to form from building block and 1580 01:00:22,230 --> 01:00:20,640 then assemble as a compartment however 1581 01:00:25,829 --> 01:00:22,240 the building block shouldn't be able to 1582 01:00:28,069 --> 01:00:25,839 form compartments so then we can 1583 01:00:31,349 --> 01:00:28,079 make a positive feedback on formation of 1584 01:00:33,990 --> 01:00:31,359 compartment and the last thing 1585 01:00:34,710 --> 01:00:34,000 last but not least is partitioning so 1586 01:00:37,430 --> 01:00:34,720 our 1587 01:00:39,349 --> 01:00:37,440 lung positive sharp fiber should be able 1588 01:00:41,750 --> 01:00:39,359 to be accommodated inside compartment 1589 01:00:44,150 --> 01:00:41,760 because we need compartments for our 1590 01:00:45,750 --> 01:00:44,160 replicators so among different 1591 01:00:47,750 --> 01:00:45,760 compartments that have been studied over 1592 01:00:50,150 --> 01:00:47,760 the last years like i showed a few of 1593 01:00:52,789 --> 01:00:50,160 them here uh classification is very a 1594 01:00:54,470 --> 01:00:52,799 promising candidate because they are 1595 01:00:57,190 --> 01:00:54,480 boundary layers they don't have any 1596 01:01:00,549 --> 01:00:57,200 membrane so our positively charged fiber 1597 01:01:02,150 --> 01:01:00,559 are able to be partitioned inside so 1598 01:01:05,270 --> 01:01:02,160 class surveys are liquid liquid phase 1599 01:01:07,510 --> 01:01:05,280 separated drop that usually when the 1600 01:01:10,069 --> 01:01:07,520 affinity of some special molecules 1601 01:01:12,309 --> 01:01:10,079 together is higher than their affinity 1602 01:01:14,710 --> 01:01:12,319 to environment they form 1603 01:01:18,549 --> 01:01:14,720 this droplets and other molecules based 1604 01:01:20,710 --> 01:01:18,559 on the interactions can now may have a 1605 01:01:22,950 --> 01:01:20,720 tendency in order to be partitioned 1606 01:01:25,349 --> 01:01:22,960 inside this compartment however most of 1607 01:01:27,750 --> 01:01:25,359 compartment material or 1608 01:01:29,990 --> 01:01:27,760 sorry most of compartment droplets are 1609 01:01:32,309 --> 01:01:30,000 formed from compartment material 1610 01:01:34,870 --> 01:01:32,319 a system in which the compartment 1611 01:01:37,109 --> 01:01:34,880 material itself can be formed from its 1612 01:01:38,789 --> 01:01:37,119 own building block is very rare so in 1613 01:01:41,349 --> 01:01:38,799 order to address this problem that we 1614 01:01:43,750 --> 01:01:41,359 need for our uh system 1615 01:01:47,270 --> 01:01:43,760 uh i actually designed this villain 1616 01:01:49,910 --> 01:01:47,280 block so i use the same approach like 1617 01:01:52,230 --> 01:01:49,920 dynamical military chemistry i use the 1618 01:01:54,630 --> 01:01:52,240 benzene with two tiles but i changed the 1619 01:01:57,190 --> 01:01:54,640 step by chain to a block of positive and 1620 01:01:59,190 --> 01:01:57,200 block of negative charge so 1621 01:02:01,670 --> 01:01:59,200 number of lysine and a number of 1622 01:02:03,910 --> 01:02:01,680 glutamic acids is shown with m 1623 01:02:05,990 --> 01:02:03,920 the open oxidation as we expected they 1624 01:02:08,390 --> 01:02:06,000 form different microcycles then i 1625 01:02:10,230 --> 01:02:08,400 hypothesize that probably for one of 1626 01:02:12,630 --> 01:02:10,240 these microcycles the interaction should 1627 01:02:15,029 --> 01:02:12,640 be enough to go under phase separation 1628 01:02:16,950 --> 01:02:15,039 and in autocatalytic process draw all 1629 01:02:18,309 --> 01:02:16,960 equilibrium towards formation of the 1630 01:02:19,990 --> 01:02:18,319 cell based on that 1631 01:02:22,150 --> 01:02:20,000 we played around with the number of 1632 01:02:24,470 --> 01:02:22,160 different number of lysine and glutamic 1633 01:02:26,470 --> 01:02:24,480 acid and made different uh millenballer 1634 01:02:28,789 --> 01:02:26,480 that i'm showing you a few of them here 1635 01:02:31,190 --> 01:02:28,799 so as you see in this table it's based 1636 01:02:32,710 --> 01:02:31,200 on the concentration and the number of 1637 01:02:35,270 --> 01:02:32,720 n and m 1638 01:02:37,270 --> 01:02:35,280 by increasing the peptide lengths 1639 01:02:39,990 --> 01:02:37,280 the concentration in viscosity which 1640 01:02:42,710 --> 01:02:40,000 form uh would be higher would be wider 1641 01:02:45,349 --> 01:02:42,720 indeed so indeed in this system coaster 1642 01:02:47,109 --> 01:02:45,359 rate uh emerged from building blocks so 1643 01:02:49,510 --> 01:02:47,119 this concentration is concentration of 1644 01:02:52,710 --> 01:02:49,520 building block and for some people like 1645 01:02:55,670 --> 01:02:52,720 like k2 e2e as you see uh coaster of it 1646 01:02:57,750 --> 01:02:55,680 emerge only in one consonant in very 1647 01:03:00,789 --> 01:02:57,760 narrow concentration which is 12 around 1648 01:03:02,950 --> 01:03:00,799 12 millimolar however we could find some 1649 01:03:04,789 --> 01:03:02,960 building blocks in which and these three 1650 01:03:06,710 --> 01:03:04,799 that can form uh 1651 01:03:08,390 --> 01:03:06,720 classified can emerge from them in a 1652 01:03:11,349 --> 01:03:08,400 wider range 1653 01:03:12,870 --> 01:03:11,359 so with that um i'm going to show one of 1654 01:03:14,549 --> 01:03:12,880 them kinetic of one of them to you which 1655 01:03:16,470 --> 01:03:14,559 is k5b5 1656 01:03:18,870 --> 01:03:16,480 as you see at the beginning you have 1657 01:03:21,670 --> 01:03:18,880 lots of monomer then monomer decreasing 1658 01:03:23,349 --> 01:03:21,680 and the amount of tetramer is increasing 1659 01:03:26,069 --> 01:03:23,359 uh at the beginning the solution is 1660 01:03:27,589 --> 01:03:26,079 completely clear however after around 1661 01:03:29,910 --> 01:03:27,599 one day it would be turbid the 1662 01:03:32,150 --> 01:03:29,920 terribility emerged when 1663 01:03:34,470 --> 01:03:32,160 the tetramer image so tetramer is our 1664 01:03:36,870 --> 01:03:34,480 probably coaster rating material so 1665 01:03:39,510 --> 01:03:36,880 characterizing this assistant the 1666 01:03:40,309 --> 01:03:39,520 therapeutic one by uh electromicroscopy 1667 01:03:42,870 --> 01:03:40,319 show 1668 01:03:48,309 --> 01:03:42,880 very small droplets and however 1669 01:03:52,870 --> 01:03:50,789 from very small to very big 1670 01:03:55,750 --> 01:03:52,880 which is shown in the next video as you 1671 01:03:57,910 --> 01:03:55,760 see there are very small aggregates at 1672 01:03:59,029 --> 01:03:57,920 the beginning uh we use fluorescent dye 1673 01:04:01,510 --> 01:03:59,039 in order to 1674 01:04:03,750 --> 01:04:01,520 monitor the emergence but then the the 1675 01:04:05,510 --> 01:04:03,760 quads that emerge and they get bigger 1676 01:04:08,470 --> 01:04:05,520 and bigger and some of them coalesce 1677 01:04:11,190 --> 01:04:08,480 together to make even bigger clusters so 1678 01:04:13,029 --> 01:04:11,200 with that we have uh each of these 1679 01:04:15,190 --> 01:04:13,039 features separately 1680 01:04:17,510 --> 01:04:15,200 many other groups have tried to make 1681 01:04:19,510 --> 01:04:17,520 each of these features or system 1682 01:04:21,349 --> 01:04:19,520 capturing one or 1683 01:04:23,829 --> 01:04:21,359 a binary combination of two of these 1684 01:04:25,190 --> 01:04:23,839 features however a system in which all 1685 01:04:27,510 --> 01:04:25,200 three 1686 01:04:29,990 --> 01:04:27,520 features are combined or integrated 1687 01:04:32,150 --> 01:04:30,000 functionally together uh is not achieved 1688 01:04:34,630 --> 01:04:32,160 yet so it is something that i'm going to 1689 01:04:36,150 --> 01:04:34,640 talk a little bit in the rest of my talk 1690 01:04:38,470 --> 01:04:36,160 so the first 1691 01:04:41,190 --> 01:04:38,480 requirement was formation from building 1692 01:04:42,950 --> 01:04:41,200 block the second one is partitioning so 1693 01:04:45,270 --> 01:04:42,960 we um 1694 01:04:46,230 --> 01:04:45,280 labeled our fibers with cyan dye and 1695 01:04:47,109 --> 01:04:46,240 then 1696 01:04:49,029 --> 01:04:47,119 we 1697 01:04:50,789 --> 01:04:49,039 studied this with three of this 1698 01:04:52,390 --> 01:04:50,799 conservative form 1699 01:04:56,710 --> 01:04:52,400 building like a phone cast everything 1700 01:04:59,430 --> 01:04:56,720 wide range as you see for k5 e5 1701 01:05:01,430 --> 01:04:59,440 the partitioning is the highest 1702 01:05:03,510 --> 01:05:01,440 so the next thing is positive feedback 1703 01:05:05,990 --> 01:05:03,520 of replicator as i showed before our 1704 01:05:06,789 --> 01:05:06,000 replicators are able to harness light to 1705 01:05:09,829 --> 01:05:06,799 make 1706 01:05:13,510 --> 01:05:09,839 singlet oxygen so we can use this system 1707 01:05:16,150 --> 01:05:13,520 indeed what we did uh was we used the k5 1708 01:05:17,589 --> 01:05:16,160 e5 monomer which is which has two tiles 1709 01:05:21,109 --> 01:05:17,599 and then studied this in different 1710 01:05:23,430 --> 01:05:21,119 conditions so it is a tetramer 1711 01:05:26,069 --> 01:05:23,440 a survey material so i'm going i'm going 1712 01:05:28,870 --> 01:05:26,079 just to show test from our tracks so 1713 01:05:30,309 --> 01:05:28,880 then we add fiber and cofactor uh 1714 01:05:32,150 --> 01:05:30,319 without shining light you see the 1715 01:05:34,789 --> 01:05:32,160 profile is more or less the same it's 1716 01:05:37,270 --> 01:05:34,799 not very different however as soon as we 1717 01:05:39,029 --> 01:05:37,280 shine light to the system uh the kinetic 1718 01:05:41,670 --> 01:05:39,039 would be very different and all of the 1719 01:05:44,309 --> 01:05:41,680 monomer will be converted to tetramer in 1720 01:05:47,270 --> 01:05:44,319 couple of hours instead of two days so 1721 01:05:49,990 --> 01:05:47,280 it shows that um the fibers and the 1722 01:05:52,309 --> 01:05:50,000 fiber activity has positive feedback on 1723 01:05:54,549 --> 01:05:52,319 formation of plaster with what we 1724 01:05:55,430 --> 01:05:54,559 were looking for so in this video you 1725 01:05:57,990 --> 01:05:55,440 see 1726 01:05:59,589 --> 01:05:58,000 in this box there is no glass array 1727 01:06:02,230 --> 01:05:59,599 but also there are some 1728 01:06:04,870 --> 01:06:02,240 aggregates of fiber the red spots are 1729 01:06:07,990 --> 01:06:04,880 excited for frame so what you see uh 1730 01:06:11,510 --> 01:06:08,000 over time uh the quad survey emerge from 1731 01:06:14,069 --> 01:06:11,520 bunch of fibers and then they uh grow 1732 01:06:16,309 --> 01:06:14,079 and also some of them attach together 1733 01:06:18,069 --> 01:06:16,319 and make even bigger ones so with that 1734 01:06:20,950 --> 01:06:18,079 system actually we have positive 1735 01:06:22,630 --> 01:06:20,960 feedback based on only synthetic 1736 01:06:25,430 --> 01:06:22,640 chemistry 1737 01:06:27,910 --> 01:06:25,440 so the next one is to have some function 1738 01:06:28,630 --> 01:06:27,920 out of this system so in this system you 1739 01:06:30,069 --> 01:06:28,640 see 1740 01:06:31,349 --> 01:06:30,079 first we 1741 01:06:33,829 --> 01:06:31,359 can find 1742 01:06:35,029 --> 01:06:33,839 fibers with cofactor inside uh 1743 01:06:37,910 --> 01:06:35,039 cholesterol 1744 01:06:39,670 --> 01:06:37,920 and then we add food nutrition it's uh 1745 01:06:42,549 --> 01:06:39,680 the starting the building block of 1746 01:06:44,390 --> 01:06:42,559 cholesterol so we did it and we monitor 1747 01:06:47,109 --> 01:06:44,400 it over time as you see 1748 01:06:49,510 --> 01:06:47,119 in the left hand side without shining 1749 01:06:52,390 --> 01:06:49,520 light indeed we didn't excite buffering 1750 01:06:55,829 --> 01:06:52,400 so i call it dark so in the dark one you 1751 01:06:58,470 --> 01:06:55,839 see the monomer uh converted to tetramer 1752 01:07:00,710 --> 01:06:58,480 in around one day however in the right 1753 01:07:04,390 --> 01:07:00,720 hand side we shine it we excite full 1754 01:07:06,150 --> 01:07:04,400 frame as you see only in two or three 1755 01:07:07,910 --> 01:07:06,160 hours all of the monomer will be 1756 01:07:10,549 --> 01:07:07,920 converted to 1757 01:07:13,349 --> 01:07:10,559 test somewhere so it is shown in the 1758 01:07:15,430 --> 01:07:13,359 video as well as you can see in the same 1759 01:07:17,109 --> 01:07:15,440 time time scale 1760 01:07:19,829 --> 01:07:17,119 the one in the dark doesn't give that 1761 01:07:21,990 --> 01:07:19,839 much but the one in the light grow even 1762 01:07:24,230 --> 01:07:22,000 in the course of 10 minutes 1763 01:07:25,589 --> 01:07:24,240 so with that i would like to conclude 1764 01:07:27,349 --> 01:07:25,599 first of all we could make a 1765 01:07:29,670 --> 01:07:27,359 self-synthesizing class survey that they 1766 01:07:31,670 --> 01:07:29,680 form from the classified material and 1767 01:07:33,750 --> 01:07:31,680 custom materials from the building block 1768 01:07:35,670 --> 01:07:33,760 and also we showed that our 1769 01:07:36,789 --> 01:07:35,680 fibers are able to harness light in 1770 01:07:39,589 --> 01:07:36,799 order to 1771 01:07:41,349 --> 01:07:39,599 make their own compartment also they can 1772 01:07:44,390 --> 01:07:41,359 have positive feedback on growth of 1773 01:07:46,470 --> 01:07:44,400 their own compartment with that 1774 01:07:48,630 --> 01:07:46,480 i'm going to acknowledge some people 1775 01:07:51,029 --> 01:07:48,640 first of all steven motor my supervisor 1776 01:07:52,950 --> 01:07:51,039 and also my students uh jan and emino 1777 01:07:55,980 --> 01:07:52,960 and thank you for your attention i'm 1778 01:08:02,710 --> 01:07:55,990 happy to take any questions 1779 01:08:08,470 --> 01:08:04,230 thank you armin 1780 01:08:08,480 --> 01:08:13,750 we have one here 1781 01:08:17,590 --> 01:08:16,470 i i mean um really exciting interesting 1782 01:08:18,470 --> 01:08:17,600 talk 1783 01:08:20,470 --> 01:08:18,480 um 1784 01:08:22,390 --> 01:08:20,480 i was just trying to reconcile in my 1785 01:08:24,950 --> 01:08:22,400 head i mean the cartoons that you were 1786 01:08:26,950 --> 01:08:24,960 generally showing for the structure 1787 01:08:29,189 --> 01:08:26,960 of these entities had a very strong sort 1788 01:08:31,030 --> 01:08:29,199 of like fibril morphology 1789 01:08:32,229 --> 01:08:31,040 and then yet the images that you were 1790 01:08:34,149 --> 01:08:32,239 showing us have these kind of like 1791 01:08:35,269 --> 01:08:34,159 droplets and it's not 1792 01:08:37,110 --> 01:08:35,279 i mean generally at least in the 1793 01:08:38,870 --> 01:08:37,120 biophysics world we would normally think 1794 01:08:41,189 --> 01:08:38,880 of fibrils and droplets as you know 1795 01:08:43,669 --> 01:08:41,199 having distinct epitopes to associate 1796 01:08:46,309 --> 01:08:43,679 with them so i'm curious if you have any 1797 01:08:48,070 --> 01:08:46,319 hypothesis or model as to why 1798 01:08:49,749 --> 01:08:48,080 something that you think is forming kind 1799 01:08:52,309 --> 01:08:49,759 of like fibrils would form these 1800 01:08:54,630 --> 01:08:52,319 droplets 1801 01:08:57,110 --> 01:08:54,640 yeah that's very good question actually 1802 01:09:00,870 --> 01:08:57,120 uh because fiber as i mentioned from a 1803 01:09:03,669 --> 01:09:00,880 single oxygen so it's kind of 1804 01:09:04,950 --> 01:09:03,679 difficult to figure it out why 1805 01:09:07,990 --> 01:09:04,960 become 1806 01:09:09,829 --> 01:09:08,000 these fibers are not inside the password 1807 01:09:13,829 --> 01:09:09,839 at the beginning but what we have seen 1808 01:09:16,070 --> 01:09:13,839 is that over time by uh monitoring the 1809 01:09:17,430 --> 01:09:16,080 with another prop which is under 1810 01:09:19,910 --> 01:09:17,440 development 1811 01:09:21,749 --> 01:09:19,920 is the kind of fingerprint prop we can 1812 01:09:24,149 --> 01:09:21,759 say that fibers 1813 01:09:26,070 --> 01:09:24,159 later they can go inside but at the 1814 01:09:29,030 --> 01:09:26,080 beginning besides the fiber 1815 01:09:31,349 --> 01:09:29,040 are in micro meter range however the 1816 01:09:35,349 --> 01:09:31,359 droplets are very small so am i 1817 01:09:40,070 --> 01:09:37,430 yes 1818 01:09:44,309 --> 01:09:42,229 hi thank you for your talk becca guth 1819 01:09:46,789 --> 01:09:44,319 metzler from georgia tech 1820 01:09:48,870 --> 01:09:46,799 so if we take what we were seeing from 1821 01:09:51,749 --> 01:09:48,880 your videos and extend it these 1822 01:09:53,749 --> 01:09:51,759 coastervates would eventually merge and 1823 01:09:55,750 --> 01:09:53,759 form a single phase in which they're no 1824 01:09:59,110 --> 01:09:55,760 longer acting as compartments in the 1825 01:10:01,590 --> 01:09:59,120 same way so do you have any ideas of how 1826 01:10:04,630 --> 01:10:01,600 these coastervates could maybe split or 1827 01:10:07,510 --> 01:10:04,640 keep from merging into a single phase 1828 01:10:09,430 --> 01:10:07,520 actually another um yeah that's true it 1829 01:10:12,310 --> 01:10:09,440 takes around a couple of days and for 1830 01:10:15,669 --> 01:10:12,320 some of the life like the longer one or 1831 01:10:19,110 --> 01:10:15,679 k5b5 or even longer it takes around two 1832 01:10:20,870 --> 01:10:19,120 weeks to be uh one phase so around two 1833 01:10:23,990 --> 01:10:20,880 weeks they are stable so we have a still 1834 01:10:26,630 --> 01:10:24,000 two phases liquid liquid phase operation 1835 01:10:28,550 --> 01:10:26,640 and uh however four is fleeting this 1836 01:10:30,950 --> 01:10:28,560 passage is something that we are 1837 01:10:32,470 --> 01:10:30,960 hardly trying to figure it out and then 1838 01:10:34,709 --> 01:10:32,480 we're working on that so it's something 1839 01:10:35,669 --> 01:10:34,719 that is i think very uh 1840 01:10:38,229 --> 01:10:35,679 kind of 1841 01:10:40,630 --> 01:10:38,239 difficult to do because we we want to do 1842 01:10:43,270 --> 01:10:40,640 it with some chemical reaction not 1843 01:10:45,750 --> 01:10:43,280 mechanical agitation so for that you 1844 01:10:47,510 --> 01:10:45,760 haven't achieved yet but you're working 1845 01:10:48,550 --> 01:10:47,520 on 1846 01:10:50,550 --> 01:10:48,560 thank you 1847 01:10:51,910 --> 01:10:50,560 we have an online question 1848 01:10:53,430 --> 01:10:51,920 um it's from j 1849 01:10:55,750 --> 01:10:53,440 forsythe 1850 01:10:58,390 --> 01:10:55,760 um it says if the peptide sequence has 1851 01:11:01,189 --> 01:10:58,400 changed from blocks of k and e to random 1852 01:11:02,270 --> 01:11:01,199 or alternating ke sequences 1853 01:11:03,830 --> 01:11:02,280 is a 1854 01:11:06,470 --> 01:11:03,840 co-acer 1855 01:11:08,790 --> 01:11:06,480 formation reduced or loss 1856 01:11:11,990 --> 01:11:08,800 yeah we haven't tried that but i 1857 01:11:14,310 --> 01:11:12,000 i guess that with the uh we have 1858 01:11:17,430 --> 01:11:14,320 alternating positive and negative charge 1859 01:11:20,950 --> 01:11:17,440 because the the charge density on the 1860 01:11:23,030 --> 01:11:20,960 chain then is very very low i guess 1861 01:11:23,990 --> 01:11:23,040 we couldn't get class of it honestly and 1862 01:11:27,430 --> 01:11:24,000 even for 1863 01:11:29,270 --> 01:11:27,440 for a shorter one like k3 1864 01:11:31,750 --> 01:11:29,280 classified emerge only in very very 1865 01:11:34,229 --> 01:11:31,760 narrow range like just 12 from the molar 1866 01:11:38,229 --> 01:11:34,239 or a little bit different so i guess if 1867 01:11:40,550 --> 01:11:38,239 we uh have k e k e k something like this 1868 01:11:42,149 --> 01:11:40,560 uh we can't get it so however if you 1869 01:11:43,830 --> 01:11:42,159 haven't tried that and i think it will 1870 01:11:48,070 --> 01:11:43,840 be very interesting to see the behavior 1871 01:11:56,870 --> 01:11:50,149 okay thank you very much thank you again 1872 01:12:01,910 --> 01:12:00,149 and now we have veiter opu from the max 1873 01:12:03,189 --> 01:12:01,920 planck institute for mathematics in 1874 01:12:05,189 --> 01:12:03,199 sciences 1875 01:12:06,870 --> 01:12:05,199 and he's going to tell us about the 1876 01:12:09,030 --> 01:12:06,880 discovery of rna-based surface 1877 01:12:14,070 --> 01:12:09,040 self-reproducers from an experimental 1878 01:12:20,950 --> 01:12:17,990 thank you let me just share my screen 1879 01:12:22,870 --> 01:12:20,960 so okay so now we will be talking about 1880 01:12:25,189 --> 01:12:22,880 an ongoing project 1881 01:12:27,110 --> 01:12:25,199 where we try to design rna-based cycle 1882 01:12:28,709 --> 01:12:27,120 producers using a combination of 1883 01:12:31,830 --> 01:12:28,719 experiments and 1884 01:12:34,149 --> 01:12:31,840 computations basically 1885 01:12:36,390 --> 01:12:34,159 let me start by the context we are 1886 01:12:38,630 --> 01:12:36,400 talking about 1887 01:12:39,669 --> 01:12:38,640 supporting one scenario of the origin of 1888 01:12:42,229 --> 01:12:39,679 life 1889 01:12:44,470 --> 01:12:42,239 so not the whole story but some 1890 01:12:46,070 --> 01:12:44,480 specific windows where we already assume 1891 01:12:49,030 --> 01:12:46,080 that you already have some kind of 1892 01:12:51,270 --> 01:12:49,040 complex molecules such as nucleotides 1893 01:12:53,990 --> 01:12:51,280 and at some point in the history 1894 01:12:56,070 --> 01:12:54,000 um you reach a kind of complexity in 1895 01:12:57,510 --> 01:12:56,080 which you can 1896 01:12:59,750 --> 01:12:57,520 have the first 1897 01:13:02,149 --> 01:12:59,760 complex enough molecule that can 1898 01:13:04,870 --> 01:13:02,159 self-replicate so from itself produce 1899 01:13:07,430 --> 01:13:04,880 again other copies of itself so the 1900 01:13:10,149 --> 01:13:07,440 question we are trying to assess 1901 01:13:12,709 --> 01:13:10,159 is um how lucky are that kind of 1902 01:13:15,350 --> 01:13:12,719 molecules is that rare or is it possible 1903 01:13:19,270 --> 01:13:15,360 to have this kind of scenario 1904 01:13:21,910 --> 01:13:19,280 in the context of the theory work right 1905 01:13:25,510 --> 01:13:21,920 so to assess this question we try to 1906 01:13:28,790 --> 01:13:25,520 look in existing rna molecules so modern 1907 01:13:31,510 --> 01:13:28,800 rna natural ones and we know one example 1908 01:13:32,950 --> 01:13:31,520 of a self-reproducer that is based on 1909 01:13:35,270 --> 01:13:32,960 eric 1910 01:13:39,350 --> 01:13:35,280 so it's the group one entrant from the 1911 01:13:41,430 --> 01:13:39,360 other piece so this type family of rna 1912 01:13:43,750 --> 01:13:41,440 is known to be able to set flights from 1913 01:13:45,990 --> 01:13:43,760 the genome so you have the whole genome 1914 01:13:48,550 --> 01:13:46,000 and the sequences encoded in it and this 1915 01:13:50,790 --> 01:13:48,560 molecule is able to cut itself from the 1916 01:13:52,630 --> 01:13:50,800 genome and glue together the remaining 1917 01:13:54,790 --> 01:13:52,640 two strains 1918 01:13:56,950 --> 01:13:54,800 so from the 1919 01:13:58,070 --> 01:13:56,960 databases we have online we know only 1920 01:14:02,550 --> 01:13:58,080 about 1921 01:14:05,910 --> 01:14:02,560 3000 homologs of this family 1922 01:14:07,910 --> 01:14:05,920 i'm showing here below a tree of the sub 1923 01:14:08,950 --> 01:14:07,920 plus it's just to show that in the 1924 01:14:11,750 --> 01:14:08,960 family 1925 01:14:13,990 --> 01:14:11,760 you have a very diverse set of sequences 1926 01:14:15,750 --> 01:14:14,000 but also a very diverse set of 1927 01:14:17,669 --> 01:14:15,760 structures i'm talking about molecular 1928 01:14:19,510 --> 01:14:17,679 structures in this case 1929 01:14:21,750 --> 01:14:19,520 so 1930 01:14:24,070 --> 01:14:21,760 it performs self-application 1931 01:14:25,830 --> 01:14:24,080 in a specific manner so you start with 1932 01:14:27,350 --> 01:14:25,840 four different 1933 01:14:30,709 --> 01:14:27,360 fragments 1934 01:14:33,270 --> 01:14:30,719 first a non-covalent complex is formed 1935 01:14:35,110 --> 01:14:33,280 by self-assembly and this 1936 01:14:37,030 --> 01:14:35,120 non-covalent complex is able to 1937 01:14:38,709 --> 01:14:37,040 characterize the formation 1938 01:14:43,910 --> 01:14:38,719 of 1939 01:14:46,390 --> 01:14:43,920 violent gains between the fragments 1940 01:14:48,070 --> 01:14:46,400 so the question we are trying to assess 1941 01:14:50,709 --> 01:14:48,080 here is 1942 01:14:53,189 --> 01:14:50,719 whether those rnas are 1943 01:14:56,390 --> 01:14:53,199 very likely in the sql space and to 1944 01:14:59,910 --> 01:14:56,400 answer this question we look at all the 1945 01:15:02,310 --> 01:14:59,920 formulas in resounding sequences from 1946 01:15:05,990 --> 01:15:02,320 databases 1947 01:15:11,990 --> 01:15:08,830 we do an active exploration of the sql 1948 01:15:14,390 --> 01:15:12,000 space of groupon entries so we start by 1949 01:15:17,110 --> 01:15:14,400 building a generative model that will be 1950 01:15:18,070 --> 01:15:17,120 trained on natural sequences found 1951 01:15:20,149 --> 01:15:18,080 in 1952 01:15:21,270 --> 01:15:20,159 databases 1953 01:15:23,430 --> 01:15:21,280 and from 1954 01:15:26,070 --> 01:15:23,440 the model we have a sequence phase from 1955 01:15:28,709 --> 01:15:26,080 which we sample 1956 01:15:29,750 --> 01:15:28,719 possible values and in the end we test 1957 01:15:32,390 --> 01:15:29,760 them for 1958 01:15:34,550 --> 01:15:32,400 self-reproduction so in the end we take 1959 01:15:36,310 --> 01:15:34,560 the output of the experimental testing 1960 01:15:38,630 --> 01:15:36,320 of self-reproduction to correct the 1961 01:15:42,790 --> 01:15:38,640 model if necessary and will go forward 1962 01:15:45,030 --> 01:15:42,800 and explore all sequences what is that 1963 01:15:46,390 --> 01:15:45,040 at this point there are two main and 1964 01:15:47,430 --> 01:15:46,400 nodes 1965 01:15:49,189 --> 01:15:47,440 uh 1966 01:15:50,070 --> 01:15:49,199 what kind of generative model should we 1967 01:15:52,390 --> 01:15:50,080 use 1968 01:15:53,990 --> 01:15:52,400 to model such a complicated space as the 1969 01:15:57,030 --> 01:15:54,000 sequence space 1970 01:15:58,709 --> 01:15:57,040 and how can we uh 1971 01:16:01,030 --> 01:15:58,719 test for 1972 01:16:03,030 --> 01:16:01,040 that many sequences because in this case 1973 01:16:14,870 --> 01:16:03,040 if one wants to explore the sequence 1974 01:16:17,910 --> 01:16:15,750 the 1975 01:16:20,870 --> 01:16:17,920 experimental style developed first 1976 01:16:22,390 --> 01:16:20,880 starting a catholic asia that is divided 1977 01:16:23,430 --> 01:16:22,400 into steps 1978 01:16:25,110 --> 01:16:23,440 so 1979 01:16:27,590 --> 01:16:25,120 those two steps represent the two type 1980 01:16:29,350 --> 01:16:27,600 of catalytic activity group one engines 1981 01:16:31,189 --> 01:16:29,360 are able to perform 1982 01:16:33,030 --> 01:16:31,199 so you have as i said earlier the 1983 01:16:34,630 --> 01:16:33,040 cutting 1984 01:16:36,229 --> 01:16:34,640 reaction where 1985 01:16:38,630 --> 01:16:36,239 the 1986 01:16:40,390 --> 01:16:38,640 sequence is tested for 1987 01:16:43,430 --> 01:16:40,400 being able to cut 1988 01:16:46,229 --> 01:16:43,440 one part of itself so the splicing part 1989 01:16:49,910 --> 01:16:46,239 and the second phase is the testing 1990 01:16:51,910 --> 01:16:49,920 whether the rna is able to attach 1991 01:16:54,870 --> 01:16:51,920 a different fragment 1992 01:16:56,870 --> 01:16:54,880 so in the end of this essay you have 1993 01:16:57,990 --> 01:16:56,880 if the evidence is active when you're 1994 01:17:00,390 --> 01:16:58,000 testing 1995 01:17:02,630 --> 01:17:00,400 the area itself attached to a fragment 1996 01:17:04,070 --> 01:17:02,640 that you know the sequence 1997 01:17:06,070 --> 01:17:04,080 and then you can 1998 01:17:08,149 --> 01:17:06,080 use the output of the essay 1999 01:17:10,709 --> 01:17:08,159 send that to deep sequencing and then 2000 01:17:13,510 --> 01:17:10,719 have a bunch of reads on which we have 2001 01:17:16,470 --> 01:17:13,520 copies of you every name which 2002 01:17:18,630 --> 01:17:16,480 the if it's active some coffees of some 2003 01:17:20,470 --> 01:17:18,640 copies of them will have the substrate 2004 01:17:22,950 --> 01:17:20,480 attached to it so then in the end you 2005 01:17:25,669 --> 01:17:22,960 can complete the weight and estimate 2006 01:17:27,510 --> 01:17:25,679 kind of proxy of activity for this s 2007 01:17:30,550 --> 01:17:27,520 right 2008 01:17:33,510 --> 01:17:30,560 but still in the end of this you only 2009 01:17:34,709 --> 01:17:33,520 have a proxy for the self-reproduction 2010 01:17:37,830 --> 01:17:34,719 we know that 2011 01:17:40,390 --> 01:17:37,840 the modern cyber producers are able to 2012 01:17:42,390 --> 01:17:40,400 perform this but it's only a necessary 2013 01:17:48,070 --> 01:17:42,400 condition 2014 01:17:50,070 --> 01:17:48,080 to be the coupon engine but not um 2015 01:17:53,030 --> 01:17:50,080 surely to be a self-reproducer so in the 2016 01:17:55,110 --> 01:17:53,040 end we take a couple of them 2017 01:17:57,030 --> 01:17:55,120 in a long throughput manner in this 2018 01:17:59,590 --> 01:17:57,040 situation where we 2019 01:18:00,630 --> 01:17:59,600 cut them cut those sequences into two 2020 01:18:05,110 --> 01:18:00,640 parts 2021 01:18:10,630 --> 01:18:07,910 do them together in here so if they are 2022 01:18:12,790 --> 01:18:10,640 capable of reproducing themselves after 2023 01:18:15,750 --> 01:18:12,800 being cut we assume that they are able 2024 01:18:19,910 --> 01:18:17,430 so on the other side for the 2025 01:18:22,790 --> 01:18:19,920 computational part we started by 2026 01:18:24,790 --> 01:18:22,800 statistical exploration we use in this 2027 01:18:26,550 --> 01:18:24,800 case a model that is called black 2028 01:18:29,110 --> 01:18:26,560 protein analysis 2029 01:18:31,910 --> 01:18:29,120 the basic idea is that you take the 2030 01:18:35,110 --> 01:18:31,920 sequence of your target every name 2031 01:18:37,590 --> 01:18:35,120 you look for homologs in databases and 2032 01:18:40,790 --> 01:18:37,600 then you use low order statistics what i 2033 01:18:42,790 --> 01:18:40,800 mean here is frequencies of nucleotides 2034 01:18:45,750 --> 01:18:42,800 positions and the pair 2035 01:18:49,030 --> 01:18:45,760 of positions so using those kind of 2036 01:18:51,669 --> 01:18:49,040 frequencies one can parameterize a model 2037 01:18:53,990 --> 01:18:51,679 that will extract the statistical 2038 01:18:56,310 --> 01:18:54,000 signature of pairwise and single-side 2039 01:18:57,830 --> 01:18:56,320 frequencies and be able to 2040 01:18:59,270 --> 01:18:57,840 sample 2041 01:19:02,149 --> 01:18:59,280 new sequences 2042 01:19:04,630 --> 01:19:02,159 so with this we first tested our model 2043 01:19:06,550 --> 01:19:04,640 on a completely and untrained task which 2044 01:19:09,030 --> 01:19:06,560 is the contact prediction 2045 01:19:11,270 --> 01:19:09,040 and the model performed really well at 2046 01:19:13,430 --> 01:19:11,280 recovering contacts in the actual 2047 01:19:15,910 --> 01:19:13,440 structure of the 2048 01:19:18,630 --> 01:19:15,920 of the groupon engine targeted here 2049 01:19:21,189 --> 01:19:18,640 so we use that model to sample first a 2050 01:19:23,430 --> 01:19:21,199 small batch of white sequences that we 2051 01:19:25,110 --> 01:19:23,440 tested for the essay and one has been 2052 01:19:27,110 --> 01:19:25,120 found to be active for the 2053 01:19:29,669 --> 01:19:27,120 self-reproduction in this time 2054 01:19:32,149 --> 01:19:29,679 so we cut one of them into two pieces 2055 01:19:34,149 --> 01:19:32,159 and it was able to recover itself 2056 01:19:36,630 --> 01:19:34,159 and this variant was found at 44 2057 01:19:39,110 --> 01:19:36,640 mutation from absorption 2058 01:19:40,390 --> 01:19:39,120 however we saw one shortcoming 2059 01:19:43,110 --> 01:19:40,400 uh 2060 01:19:45,110 --> 01:19:43,120 for this approach which is if we look at 2061 01:19:47,669 --> 01:19:45,120 the sequence space like this and we have 2062 01:19:50,390 --> 01:19:47,679 our natural sequences here if the 2063 01:19:52,709 --> 01:19:50,400 underlying distribution is 2064 01:19:53,510 --> 01:19:52,719 following the natural one is fine but if 2065 01:19:56,709 --> 01:19:53,520 it's 2066 01:19:57,590 --> 01:19:56,719 only one subset of the natural of the 2067 01:19:59,590 --> 01:19:57,600 real 2068 01:20:00,630 --> 01:19:59,600 sephora producer then 2069 01:20:03,350 --> 01:20:00,640 you would have 2070 01:20:05,189 --> 01:20:03,360 kind of a limited exploration 2071 01:20:07,590 --> 01:20:05,199 so we thought maybe we can go for 2072 01:20:10,390 --> 01:20:07,600 something that is less vertical based on 2073 01:20:12,550 --> 01:20:10,400 the secondary structure of rnas so we 2074 01:20:15,910 --> 01:20:12,560 can compute second structure based on 2075 01:20:18,790 --> 01:20:15,920 sequences quite efficiently and use that 2076 01:20:21,430 --> 01:20:18,800 as we know this construction of existing 2077 01:20:24,149 --> 01:20:21,440 group one engine we can use that to 2078 01:20:26,070 --> 01:20:24,159 uh sample new sequences so we tested it 2079 01:20:28,629 --> 01:20:26,080 on deep conditional scan 2080 01:20:29,510 --> 01:20:28,639 data and we found correct trend in the 2081 01:20:33,270 --> 01:20:29,520 data 2082 01:20:35,590 --> 01:20:33,280 so we use this um this score to sample 2083 01:20:38,229 --> 01:20:35,600 new sequences using some genetic 2084 01:20:39,910 --> 01:20:38,239 algorithm and multicam exploration 2085 01:20:42,550 --> 01:20:39,920 however in the end 2086 01:20:46,149 --> 01:20:42,560 the few sequences we tested were not 2087 01:20:47,910 --> 01:20:46,159 active for the ac needed for the cipher 2088 01:20:50,470 --> 01:20:47,920 prediction test 2089 01:20:52,390 --> 01:20:50,480 so at this point uh we have a method 2090 01:20:54,149 --> 01:20:52,400 that is able to find active uh 2091 01:20:56,870 --> 01:20:54,159 environment but stay quite close to 2092 01:20:58,629 --> 01:20:56,880 giving observation and on the other side 2093 01:21:03,189 --> 01:20:58,639 we have the physics phase kind of 2094 01:21:06,470 --> 01:21:03,199 approach but uh is not very successful 2095 01:21:08,149 --> 01:21:06,480 at finding active sequences but then we 2096 01:21:09,189 --> 01:21:08,159 thought maybe we can combine both of 2097 01:21:10,149 --> 01:21:09,199 them 2098 01:21:13,189 --> 01:21:10,159 to 2099 01:21:15,510 --> 01:21:13,199 have both capabilities and then we 2100 01:21:17,510 --> 01:21:15,520 perform a high throughput screening in 2101 01:21:18,629 --> 01:21:17,520 which we test it for thousands of 2102 01:21:22,070 --> 01:21:18,639 parents 2103 01:21:24,470 --> 01:21:22,080 three main hypotheses whether uh first 2104 01:21:26,709 --> 01:21:24,480 whether the statistical model combined 2105 01:21:27,960 --> 01:21:26,719 with the structure is able to 2106 01:21:29,189 --> 01:21:27,970 enlarge the 2107 01:21:32,550 --> 01:21:29,199 [Music] 2108 01:21:34,629 --> 01:21:32,560 spectrum of possible designs 2109 01:21:37,750 --> 01:21:34,639 actively designed i mean the second 2110 01:21:40,830 --> 01:21:37,760 hypothesis is whether we need to model 2111 01:21:43,669 --> 01:21:40,840 interaction between position engineering 2112 01:21:46,629 --> 01:21:43,679 um so for this one we sampled sequences 2113 01:21:48,950 --> 01:21:46,639 according only on prof and third 2114 01:21:51,189 --> 01:21:48,960 we tested the hypothesis of the 2115 01:21:52,390 --> 01:21:51,199 secondary structure but now at the level 2116 01:21:56,149 --> 01:21:52,400 of thousand 2117 01:21:58,310 --> 01:21:56,159 values so you can see here the results 2118 01:22:01,830 --> 01:21:58,320 of this experiment where we have here 2119 01:22:03,830 --> 01:22:01,840 represented a bit more than 2 000 2120 01:22:05,830 --> 01:22:03,840 sequences that have been found at least 2121 01:22:07,669 --> 01:22:05,840 once attached to the substrate we were 2122 01:22:11,030 --> 01:22:07,679 looking for 2123 01:22:13,270 --> 01:22:11,040 so you can see here three main points 2124 01:22:15,669 --> 01:22:13,280 three main results first of all the more 2125 01:22:17,669 --> 01:22:15,679 you do mutation so you see here it's the 2126 01:22:19,990 --> 01:22:17,679 number of mutations from the reference 2127 01:22:23,110 --> 01:22:20,000 sequence right right in this case 2128 01:22:26,070 --> 01:22:23,120 and on the y-axis you have the activity 2129 01:22:28,070 --> 01:22:26,080 rate so the main 2130 01:22:31,350 --> 01:22:28,080 the first point here is that the more 2131 01:22:34,629 --> 01:22:31,360 you limitation the more you lose 2132 01:22:38,149 --> 01:22:34,639 activity and as it is in uh log scale 2133 01:22:40,550 --> 01:22:38,159 you use it exponentially quick quickly 2134 01:22:42,629 --> 01:22:40,560 the second point is that this problem is 2135 01:22:44,790 --> 01:22:42,639 actually quite difficult so we perform 2136 01:22:45,669 --> 01:22:44,800 some some random mutation and when you 2137 01:22:49,110 --> 01:22:45,679 do 2138 01:22:52,229 --> 01:22:49,120 only like 12 to 20 mutation completely 2139 01:22:55,270 --> 01:22:52,239 kill the activity 2140 01:22:57,990 --> 01:22:55,280 however if you combined this statistic 2141 01:23:00,629 --> 01:22:58,000 and structure we were able to explore 2142 01:23:04,950 --> 01:23:00,639 mutation space from plane mutation to 2143 01:23:07,430 --> 01:23:04,960 other obviously quite enhanced mutations 2144 01:23:09,990 --> 01:23:07,440 so in the end we took a few of them 2145 01:23:13,030 --> 01:23:10,000 tested them for the self prediction 2146 01:23:15,590 --> 01:23:13,040 and we found five active out of six 2147 01:23:18,149 --> 01:23:15,600 using this uh the pool of sequences we 2148 01:23:20,870 --> 01:23:18,159 have with the statistical structure 2149 01:23:22,950 --> 01:23:20,880 however we found on zero acting when we 2150 01:23:25,030 --> 01:23:22,960 considered the ones that were found for 2151 01:23:26,629 --> 01:23:25,040 the structure base so we're not 2152 01:23:28,390 --> 01:23:26,639 completely sure because it's still an 2153 01:23:29,270 --> 01:23:28,400 ongoing project but 2154 01:23:31,030 --> 01:23:29,280 uh 2155 01:23:33,590 --> 01:23:31,040 maybe for some of them there are some 2156 01:23:34,629 --> 01:23:33,600 kind of patterns that were that 2157 01:23:35,830 --> 01:23:34,639 enhance 2158 01:23:38,950 --> 01:23:35,840 cause 2159 01:23:42,550 --> 01:23:38,960 cross catalysis 2160 01:23:45,189 --> 01:23:42,560 uh here you can see a projection into 2161 01:23:47,270 --> 01:23:45,199 the first and second principal component 2162 01:23:51,189 --> 01:23:47,280 of these natural sequences and 2163 01:23:52,070 --> 01:23:51,199 projection of our designs here in color 2164 01:23:55,270 --> 01:23:52,080 and 2165 01:23:58,149 --> 01:23:55,280 the in the orange dots you can see the 2166 01:23:59,030 --> 01:23:58,159 sequences that were found with at least 2167 01:24:01,030 --> 01:23:59,040 one 2168 01:24:03,750 --> 01:24:01,040 suture so they were found at least 2169 01:24:05,669 --> 01:24:03,760 active so it's our pool of potentially 2170 01:24:09,430 --> 01:24:05,679 active variants which is 2171 01:24:11,590 --> 01:24:09,440 roughly more than 2 000 in these states 2172 01:24:12,870 --> 01:24:11,600 so to finish my presentation would like 2173 01:24:15,030 --> 01:24:12,880 to thank 2174 01:24:17,750 --> 01:24:15,040 uh collaborators so especially can you 2175 01:24:19,590 --> 01:24:17,760 know better did incredible work to for 2176 01:24:22,070 --> 01:24:19,600 the experience you could vlog yes here 2177 01:24:23,669 --> 01:24:22,080 we perform simulate all the 2178 01:24:25,750 --> 01:24:23,679 the experience site 2179 01:24:28,629 --> 01:24:25,760 and collaborative and computational 2180 01:24:30,550 --> 01:24:28,639 performance simulations and designs 2181 01:24:51,510 --> 01:24:30,560 thank you for your attention so i'm 2182 01:24:57,030 --> 01:24:54,709 online questions no 2183 01:24:59,189 --> 01:24:57,040 okay so we'll be able to discuss with 2184 01:25:01,110 --> 01:24:59,199 you later 2185 01:25:03,110 --> 01:25:01,120 i'll take the opportunity to 2186 01:25:06,470 --> 01:25:03,120 start the the next talk which is our 2187 01:25:09,270 --> 01:25:06,480 last talk is a short talk 2188 01:25:12,629 --> 01:25:10,470 sorry 2189 01:25:14,149 --> 01:25:12,639 the hub 2190 01:25:15,669 --> 01:25:14,159 and he's going to tell us about 2191 01:25:16,870 --> 01:25:15,679 combinatorial explosion versus 2192 01:25:18,790 --> 01:25:16,880 compression 2193 01:25:29,830 --> 01:25:18,800 what can we learn from multi-component 2194 01:25:29,840 --> 01:25:34,870 hello everyone take this off 2195 01:25:38,390 --> 01:25:36,870 hello everyone my name is vahab i'm a 2196 01:25:40,470 --> 01:25:38,400 graduate student here in the williams 2197 01:25:41,990 --> 01:25:40,480 lab at georgia tech and today i want to 2198 01:25:43,590 --> 01:25:42,000 talk to you about combinatorial 2199 01:25:45,750 --> 01:25:43,600 compression and explosion and what we're 2200 01:25:47,270 --> 01:25:45,760 doing in the laboratory to tip this 2201 01:25:50,229 --> 01:25:47,280 balance 2202 01:25:52,790 --> 01:25:50,239 so one obstacle in studying prebiotic 2203 01:25:54,790 --> 01:25:52,800 chemical reactions is this that they're 2204 01:25:56,950 --> 01:25:54,800 very heterogeneous and messy and as a 2205 01:25:58,950 --> 01:25:56,960 result we get this combinatorial 2206 01:26:00,790 --> 01:25:58,960 explosion where we get a lot of products 2207 01:26:03,110 --> 01:26:00,800 out and this has been uh 2208 01:26:04,709 --> 01:26:03,120 this was uh mentioned by some of the 2209 01:26:11,590 --> 01:26:04,719 earlier speakers in this session here 2210 01:26:16,070 --> 01:26:13,669 and what we're doing in the lab is using 2211 01:26:18,950 --> 01:26:16,080 a multi-component system we have nine 2212 01:26:20,550 --> 01:26:18,960 components shown here on the screen 2213 01:26:23,430 --> 01:26:20,560 eight of these are capable of 2214 01:26:25,350 --> 01:26:23,440 polymerization and through dehydration 2215 01:26:28,550 --> 01:26:25,360 so we call these building blocks and 2216 01:26:29,990 --> 01:26:28,560 what we do is dry out these samples for 2217 01:26:31,830 --> 01:26:30,000 72 hours 2218 01:26:33,910 --> 01:26:31,840 at a constant temperature of about 45 2219 01:26:36,709 --> 01:26:33,920 degrees celsius and at this mild 2220 01:26:38,950 --> 01:26:36,719 temperature under anoxic conditions 2221 01:26:41,189 --> 01:26:38,960 we're able to observe some novel trends 2222 01:26:42,310 --> 01:26:41,199 and particularly i want to focus on 2223 01:26:45,590 --> 01:26:42,320 combinatorial what we're calling 2224 01:26:50,870 --> 01:26:49,510 and so looking at some hplc data we can 2225 01:26:52,870 --> 01:26:50,880 see 2226 01:26:54,149 --> 01:26:52,880 we can separate by hydrophobicity and so 2227 01:26:56,629 --> 01:26:54,159 each of these peaks is going to 2228 01:26:57,590 --> 01:26:56,639 correspond to a specific chemical 2229 01:26:59,910 --> 01:26:57,600 product 2230 01:27:01,030 --> 01:26:59,920 and in the case of two building block 2231 01:27:03,430 --> 01:27:01,040 dry downs where we have just two 2232 01:27:07,350 --> 01:27:03,440 components here at the top in the blue 2233 01:27:08,390 --> 01:27:07,360 trace we get seven unique products out 2234 01:27:09,990 --> 01:27:08,400 and when we look at another two 2235 01:27:12,229 --> 01:27:10,000 component system or two building block 2236 01:27:15,510 --> 01:27:12,239 system uh then the red trace we have 2237 01:27:17,750 --> 01:27:15,520 eight distinct chemical species 2238 01:27:19,830 --> 01:27:17,760 now taking those two systems and 2239 01:27:21,669 --> 01:27:19,840 comparing them to a 2240 01:27:23,830 --> 01:27:21,679 that a building block system that i 2241 01:27:26,149 --> 01:27:23,840 showed you earlier in that system we 2242 01:27:28,310 --> 01:27:26,159 only see 11 and what's happening is 2243 01:27:29,669 --> 01:27:28,320 we're getting kind of a difference in 2244 01:27:31,510 --> 01:27:29,679 the way this 2245 01:27:32,870 --> 01:27:31,520 system is sampling the potential product 2246 01:27:35,270 --> 01:27:32,880 space 2247 01:27:36,070 --> 01:27:35,280 so we can visualize this using a fractal 2248 01:27:37,910 --> 01:27:36,080 tree 2249 01:27:39,430 --> 01:27:37,920 if we are looking at a two building 2250 01:27:41,110 --> 01:27:39,440 block system the 2251 01:27:42,310 --> 01:27:41,120 we're making a couple assumptions here 2252 01:27:44,149 --> 01:27:42,320 one of those is that we're not getting 2253 01:27:46,310 --> 01:27:44,159 anything longer than a trimer and that 2254 01:27:47,669 --> 01:27:46,320 we are only able to polymerize at two 2255 01:27:49,189 --> 01:27:47,679 sites on each 2256 01:27:50,470 --> 01:27:49,199 which is true for some but in some cases 2257 01:27:53,350 --> 01:27:50,480 our molecules can polymerize at more 2258 01:27:55,990 --> 01:27:53,360 locations some in just one 2259 01:27:57,350 --> 01:27:56,000 but there are 12 possibilities here 2260 01:27:59,990 --> 01:27:57,360 however when we 2261 01:28:01,510 --> 01:28:00,000 do this experimentally we are sampling 2262 01:28:03,910 --> 01:28:01,520 about two-thirds of the space we get 2263 01:28:05,270 --> 01:28:03,920 eight products out in um one of our 2264 01:28:07,110 --> 01:28:05,280 cases 2265 01:28:08,709 --> 01:28:07,120 seven in the other that i showed 2266 01:28:10,149 --> 01:28:08,719 when we compare this to the eight 2267 01:28:11,990 --> 01:28:10,159 building block system there's a much 2268 01:28:13,350 --> 01:28:12,000 larger theoretical combinatorial space 2269 01:28:15,030 --> 01:28:13,360 available 2270 01:28:16,709 --> 01:28:15,040 there's about 600 potential products 2271 01:28:19,669 --> 01:28:16,719 here again up to length trimer and 2272 01:28:22,149 --> 01:28:19,679 assuming just linkages at two locations 2273 01:28:23,590 --> 01:28:22,159 but in the experiments that we are doing 2274 01:28:25,510 --> 01:28:23,600 we only see 2275 01:28:26,950 --> 01:28:25,520 11 products and so we're sampling a much 2276 01:28:30,229 --> 01:28:26,960 smaller 2277 01:28:31,669 --> 01:28:30,239 proportion of this combinatorial space 2278 01:28:34,629 --> 01:28:31,679 and this is this is the effect that 2279 01:28:37,030 --> 01:28:34,639 we're calling combinatorial compression 2280 01:28:38,390 --> 01:28:37,040 and this is interesting for us because 2281 01:28:40,470 --> 01:28:38,400 it seems to indicate that certain 2282 01:28:41,669 --> 01:28:40,480 pathways are being selected for over 2283 01:28:42,629 --> 01:28:41,679 others 2284 01:28:44,229 --> 01:28:42,639 and 2285 01:28:46,629 --> 01:28:44,239 i'm going to wrap up by 2286 01:28:48,070 --> 01:28:46,639 saying that the takeaway i hope that you 2287 01:28:49,590 --> 01:28:48,080 are able to get from this is that adding 2288 01:28:51,030 --> 01:28:49,600 more components to your system isn't 2289 01:28:53,510 --> 01:28:51,040 necessarily going to cause a 2290 01:28:56,070 --> 01:28:53,520 combinatorial explosion and so maybe 2291 01:28:58,149 --> 01:28:56,080 don't be intimidated by it 2292 01:28:59,590 --> 01:28:58,159 that all being said we are doing other 2293 01:29:01,510 --> 01:28:59,600 work with this system and i would 2294 01:29:05,590 --> 01:29:01,520 encourage you to 2295 01:29:07,830 --> 01:29:05,600 attend kevita matanye's talk later today 2296 01:29:09,110 --> 01:29:07,840 and we also have a pre-print available 2297 01:29:10,229 --> 01:29:09,120 that just came out earlier this week if 2298 01:29:11,430 --> 01:29:10,239 you're interested in learning more about 2299 01:29:13,110 --> 01:29:11,440 some of the work we're doing with this 2300 01:29:14,709 --> 01:29:13,120 system 2301 01:29:16,149 --> 01:29:14,719 with that i'd like to acknowledge the 2302 01:29:18,149 --> 01:29:16,159 grants that supported this work from the 2303 01:29:19,510 --> 01:29:18,159 nsf and nasa as well as my advisor 2304 01:29:21,590 --> 01:29:19,520 lauren williams 2305 01:29:24,310 --> 01:29:21,600 miranda franco pinter and kavita matanya 2306 01:29:26,820 --> 01:29:24,320 as well as the entire williams lab and 2307 01:29:50,950 --> 01:29:26,830 that's all i have today thank you 2308 01:29:54,310 --> 01:29:53,189 from uh 2309 01:29:56,070 --> 01:29:54,320 very interesting result and i'm just 2310 01:29:57,510 --> 01:29:56,080 wondering um 2311 01:29:58,950 --> 01:29:57,520 you showed the eighth component the two 2312 01:30:00,790 --> 01:29:58,960 come on what if you have the a component 2313 01:30:02,709 --> 01:30:00,800 you start leaving out one of each one of 2314 01:30:04,629 --> 01:30:02,719 them how much does that affect the 2315 01:30:06,950 --> 01:30:04,639 landscape you see because it seems like 2316 01:30:08,310 --> 01:30:06,960 some of them might be dominating the 2317 01:30:09,669 --> 01:30:08,320 reaction pathways 2318 01:30:11,750 --> 01:30:09,679 i struggle to hear your question a 2319 01:30:14,149 --> 01:30:11,760 little bit but um you're asking if 2320 01:30:16,629 --> 01:30:14,159 certain uh molecules are dominating that 2321 01:30:21,189 --> 01:30:16,639 reaction space if you leave out one of 2322 01:30:25,350 --> 01:30:22,790 yeah so we're we're setting up these 2323 01:30:26,390 --> 01:30:25,360 binary dry downs of uh subsets of the 2324 01:30:27,990 --> 01:30:26,400 eight component system to figure out 2325 01:30:29,590 --> 01:30:28,000 which are the most reactive species we 2326 01:30:31,189 --> 01:30:29,600 are seeing that um some are more 2327 01:30:41,350 --> 01:30:31,199 reactive than others so yes that's 2328 01:30:46,709 --> 01:30:44,229 we'll talk um this may be somewhat 2329 01:30:49,350 --> 01:30:46,719 simple-minded question but 2330 01:30:50,470 --> 01:30:49,360 when you show the hplc with the peaks 2331 01:30:52,310 --> 01:30:50,480 and you say 2332 01:30:54,550 --> 01:30:52,320 these are like we have like 11 products 2333 01:30:56,229 --> 01:30:54,560 does that include the eight starting 2334 01:30:58,950 --> 01:30:56,239 materials so are there only only three 2335 01:31:01,270 --> 01:30:58,960 products or have you subtracted out the 2336 01:31:03,030 --> 01:31:01,280 the starting materials 2337 01:31:06,070 --> 01:31:03,040 uh so 2338 01:31:07,669 --> 01:31:06,080 the ones that were labeled are the 2339 01:31:09,990 --> 01:31:07,679 products so i don't think that they 2340 01:31:13,830 --> 01:31:10,000 include the starting materials they i 2341 01:31:15,430 --> 01:31:13,840 believe they elude out much earlier on 2342 01:31:16,709 --> 01:31:15,440 but i 2343 01:31:17,910 --> 01:31:16,719 don't know as much about the hplc that 2344 01:31:19,430 --> 01:31:17,920 was carried by somebody else so i i 2345 01:31:20,470 --> 01:31:19,440 could uh refer you to one of my 2346 01:31:26,229 --> 01:31:20,480 colleagues who might be able to better 2347 01:31:31,910 --> 01:31:29,750 hi uh lou chao from nasa goddard um 2348 01:31:32,790 --> 01:31:31,920 sorry if this is another hplc question 2349 01:31:34,870 --> 01:31:32,800 but 2350 01:31:37,350 --> 01:31:34,880 what solvent did you use and do you 2351 01:31:40,550 --> 01:31:37,360 expect that there are potentially other 2352 01:31:42,149 --> 01:31:40,560 targets that may be 2353 01:31:43,830 --> 01:31:42,159 not soluble in the solvent that you use 2354 01:31:46,870 --> 01:31:43,840 for the hplc 2355 01:31:48,229 --> 01:31:46,880 yeah so um yeah again i'm not i wasn't 2356 01:31:49,189 --> 01:31:48,239 the one working with hplc and i'm not as 2357 01:31:51,590 --> 01:31:49,199 familiar with it but i believe we're 2358 01:31:52,470 --> 01:31:51,600 using an acetonitrile water solvent 2359 01:31:54,550 --> 01:31:52,480 system 2360 01:31:56,310 --> 01:31:54,560 um and as far as whether there are 2361 01:31:57,590 --> 01:31:56,320 things that we're not seeing uh yes i 2362 01:31:58,950 --> 01:31:57,600 think that's possible and we're going to 2363 01:32:01,350 --> 01:31:58,960 be explaining other methods to see if we 2364 01:32:04,390 --> 01:32:01,360 see other products there 2365 01:32:14,390 --> 01:32:06,709 okay so let's thank all our speakers