1 00:00:16,740 --> 00:00:12,920 [Music] 2 00:00:20,070 --> 00:00:16,750 good afternoon living systems inevitably 3 00:00:22,859 --> 00:00:20,080 form ecosystems therefore in order to 4 00:00:25,260 --> 00:00:22,869 understand the origin of life we need to 5 00:00:29,720 --> 00:00:25,270 understand the origin of ecosystems and 6 00:00:32,490 --> 00:00:29,730 their stability and how ecosystem I 7 00:00:37,250 --> 00:00:32,500 interact which they plan planetary 8 00:00:39,990 --> 00:00:37,260 environments so this is a paragraph from 9 00:00:42,959 --> 00:00:40,000 anthropology roadmap proposed by NASA 10 00:00:45,840 --> 00:00:42,969 what it is says is that astrobiologists 11 00:00:48,389 --> 00:00:45,850 have long recognized that in order to 12 00:00:52,380 --> 00:00:48,399 understand how life can colonize a 13 00:00:55,549 --> 00:00:52,390 planet and how life can have enough 14 00:00:58,290 --> 00:00:55,559 abundance to show is observable I 15 00:01:02,160 --> 00:00:58,300 biosignatures we need to understand the 16 00:01:05,490 --> 00:01:02,170 formation and the maintenance of the the 17 00:01:09,029 --> 00:01:05,500 whole communities especially microbial 18 00:01:11,370 --> 00:01:09,039 communities for example bacteria and a 19 00:01:14,880 --> 00:01:11,380 layer predator versus and a Larry 20 00:01:17,639 --> 00:01:14,890 interaction with the environments so as 21 00:01:21,120 --> 00:01:17,649 we have learned from average shock 22 00:01:23,760 --> 00:01:21,130 yesterday we know life is a planetary 23 00:01:25,830 --> 00:01:23,770 response the planets get trapped in 24 00:01:28,169 --> 00:01:25,840 metastable States and the life helps 25 00:01:33,540 --> 00:01:28,179 them get out of that so in other words 26 00:01:39,540 --> 00:01:33,550 the purpose of life is to help planet to 27 00:01:41,580 --> 00:01:39,550 approach equilibrium and an in principle 28 00:01:44,160 --> 00:01:41,590 a particle process 29 00:01:47,520 --> 00:01:44,170 I also driven by the similar than a 30 00:01:50,729 --> 00:01:47,530 similar dynamic constraints but life can 31 00:01:53,370 --> 00:01:50,739 out-compete about your chemistry by 32 00:01:56,010 --> 00:01:53,380 finding new and faster metabolic 33 00:01:58,949 --> 00:01:56,020 pathways to utilize free energy gradient 34 00:02:01,050 --> 00:01:58,959 and how does life do this life 35 00:02:01,559 --> 00:02:01,060 thursday's by controlling information 36 00:02:05,729 --> 00:02:01,569 flow 37 00:02:08,609 --> 00:02:05,739 so once the one of the things that 38 00:02:10,550 --> 00:02:08,619 distinguish life and non-life is that 39 00:02:15,150 --> 00:02:10,560 life can control information flow and 40 00:02:18,300 --> 00:02:15,160 using information flow I to manipulate 41 00:02:20,960 --> 00:02:18,310 and impact its environments so when we 42 00:02:24,600 --> 00:02:20,970 talk about information flow it can be a 43 00:02:28,320 --> 00:02:24,610 generative gene flows and 44 00:02:32,010 --> 00:02:28,330 people think gene flow from mutations 45 00:02:35,070 --> 00:02:32,020 but later we'll see that there is a more 46 00:02:38,210 --> 00:02:35,080 much more powerful mechanism for during 47 00:02:42,570 --> 00:02:38,220 flow which is horizontal gene transfer 48 00:02:44,700 --> 00:02:42,580 horizontal field is a mechanism where 49 00:02:48,360 --> 00:02:44,710 the genetic functionalities can be 50 00:02:50,280 --> 00:02:48,370 swapped between different organisms so 51 00:02:50,940 --> 00:02:50,290 there are other different ingredients 52 00:02:54,449 --> 00:02:50,950 for life 53 00:02:57,650 --> 00:02:54,459 for example the eleven system is an eco 54 00:03:01,080 --> 00:02:57,660 system and it has two aspects 55 00:03:04,320 --> 00:03:01,090 why is population dynamics and for 56 00:03:06,479 --> 00:03:04,330 example the behavior of predator and 57 00:03:09,420 --> 00:03:06,489 prey in an ecosystem through person 58 00:03:13,229 --> 00:03:09,430 death processes and another aspect is 59 00:03:15,449 --> 00:03:13,239 metabolism query metabolic network 60 00:03:17,699 --> 00:03:15,459 inside organisms will form a global 61 00:03:21,479 --> 00:03:17,709 electronic market that transduces the 62 00:03:24,050 --> 00:03:21,489 energy flow so this to ask aspects has 63 00:03:29,699 --> 00:03:24,060 to be unified but so far we don't have a 64 00:03:32,610 --> 00:03:29,709 framework for that doing this talk we 65 00:03:35,190 --> 00:03:32,620 will focus mostly on the aspects of 66 00:03:38,190 --> 00:03:35,200 population dynamics with a little bit of 67 00:03:41,520 --> 00:03:38,200 energy flow while the next talk will 68 00:03:45,360 --> 00:03:41,530 focus on monopolizing but no population 69 00:03:47,820 --> 00:03:45,370 dynamics and also we will looking at the 70 00:03:50,280 --> 00:03:47,830 personal dynamics of marine microbes and 71 00:03:55,170 --> 00:03:50,290 allowing interaction with viruses and 72 00:04:00,390 --> 00:03:55,180 the energy flow will come from a the 73 00:04:04,320 --> 00:04:00,400 photo ingredients so this is a simple 74 00:04:08,160 --> 00:04:04,330 diagrammatic summary which says that a 75 00:04:09,930 --> 00:04:08,170 life living system can out-compete about 76 00:04:13,890 --> 00:04:09,940 its chemistry 77 00:04:18,599 --> 00:04:13,900 by utilizing gene flow metropolitan and 78 00:04:22,770 --> 00:04:18,609 population dynamics and this is the 79 00:04:25,500 --> 00:04:22,780 outline of my talk so the system were 80 00:04:28,380 --> 00:04:25,510 interesting is the marine Santa bacteria 81 00:04:31,020 --> 00:04:28,390 and that their creator viruses cosine of 82 00:04:33,300 --> 00:04:31,030 age and I will show that how general 83 00:04:36,240 --> 00:04:33,310 trend transfer between Sun and bacteria 84 00:04:38,430 --> 00:04:36,250 and a son of phage will form a 85 00:04:42,690 --> 00:04:38,440 collective Network that 86 00:04:46,080 --> 00:04:42,700 drives that are a whole ecosystem a2i to 87 00:04:48,990 --> 00:04:46,090 evolve and I will show that I build up a 88 00:04:50,970 --> 00:04:49,000 minimal model and the results will 89 00:04:53,640 --> 00:04:50,980 explain the range expansion and niche 90 00:04:57,540 --> 00:04:53,650 stirred stratification that have been 91 00:05:00,170 --> 00:04:57,550 observed in real data surprisely will 92 00:05:04,200 --> 00:05:00,180 see that predation in this case actually 93 00:05:08,790 --> 00:05:04,210 will stabilize a the ecosystem and it's 94 00:05:12,000 --> 00:05:08,800 been and beneficial to bacteria so let 95 00:05:16,380 --> 00:05:12,010 me quickly explain the spirit of 96 00:05:18,090 --> 00:05:16,390 modeling so conventionally I when people 97 00:05:20,160 --> 00:05:18,100 talk about bacteriophage they are 98 00:05:22,980 --> 00:05:20,170 thinking of prey in the predator so they 99 00:05:25,620 --> 00:05:22,990 model the system by using differential 100 00:05:28,320 --> 00:05:25,630 equations for example like a doctor for 101 00:05:30,840 --> 00:05:28,330 her type of models however that we 102 00:05:33,840 --> 00:05:30,850 explained before in a unified way 103 00:05:37,490 --> 00:05:33,850 including energy flow and information 104 00:05:41,040 --> 00:05:37,500 flow the interactions and relationships 105 00:05:44,190 --> 00:05:41,050 between bacteria and viruses are much 106 00:05:47,390 --> 00:05:44,200 more complicated so later I will show 107 00:05:52,620 --> 00:05:47,400 detail how we incorporate Elise 108 00:05:56,400 --> 00:05:52,630 ingredients in a model so the the the 109 00:05:59,250 --> 00:05:56,410 it's an ad hoc reservoir looking at is 110 00:06:02,280 --> 00:05:59,260 called perc attackers it's one of the 111 00:06:04,710 --> 00:06:02,290 most abundant openings on world and it 112 00:06:07,950 --> 00:06:04,720 has very small genome size is highly 113 00:06:12,090 --> 00:06:07,960 Streamlight but comparison ecoli has 114 00:06:15,540 --> 00:06:12,100 about 4 times larger of estreno sides 115 00:06:20,010 --> 00:06:15,550 and it affects a large amount of carbon 116 00:06:23,610 --> 00:06:20,020 others especially unlike most bacteria 117 00:06:27,870 --> 00:06:23,620 per caucus has no immune mechanism like 118 00:06:32,000 --> 00:06:27,880 CRISPR or profits so how does how does 119 00:06:36,090 --> 00:06:32,010 it avoid extinction under viral attack 120 00:06:40,530 --> 00:06:36,100 and it turns out prayer caucus find is 121 00:06:44,460 --> 00:06:40,540 defense against viruses by changing his 122 00:06:50,550 --> 00:06:44,470 surface protein where viruses would 123 00:06:52,290 --> 00:06:50,560 attach on cells and a and it appears to 124 00:06:56,189 --> 00:06:52,300 be a special signature 125 00:07:00,510 --> 00:06:56,199 this is yes genome coach genomic make 126 00:07:03,409 --> 00:07:00,520 island and we know is from it's probably 127 00:07:07,170 --> 00:07:03,419 from her intention transfer and 128 00:07:11,879 --> 00:07:07,180 therefore it shows a huge pan genome so 129 00:07:16,379 --> 00:07:11,889 a pan genome is a old question of genes 130 00:07:20,490 --> 00:07:16,389 of the same microbial species and and 131 00:07:23,939 --> 00:07:20,500 per caucus has a very large very large 132 00:07:27,360 --> 00:07:23,949 pen genome and is much larger than any 133 00:07:30,510 --> 00:07:27,370 single original world and that means 134 00:07:34,459 --> 00:07:30,520 that any two per kakaka cells would 135 00:07:40,559 --> 00:07:34,469 share only very few genes in common 136 00:07:43,920 --> 00:07:40,569 so on a much larger scope per caucus has 137 00:07:48,270 --> 00:07:43,930 another feature which is a niche 138 00:07:51,330 --> 00:07:48,280 register applications with to emerge 139 00:07:53,520 --> 00:07:51,340 subgroups for example here on a lamp a 140 00:07:57,019 --> 00:07:53,530 know it shows a light intensity as a 141 00:08:01,170 --> 00:07:57,029 function of death that bell in ocean and 142 00:08:04,350 --> 00:08:01,180 the there are two subgroups the to equal 143 00:08:07,469 --> 00:08:04,360 types of per caucus one is highlighted 144 00:08:10,370 --> 00:08:07,479 adapted a colonize near the surface of 145 00:08:14,370 --> 00:08:10,380 ocean another one is low-light adapted 146 00:08:17,790 --> 00:08:14,380 ecotype and our model will try to 147 00:08:20,279 --> 00:08:17,800 explain this phenomenon also Kolkata's 148 00:08:23,700 --> 00:08:20,289 phage calls anaphase carry for the 149 00:08:26,070 --> 00:08:23,710 vintage jeans and it has found that it's 150 00:08:29,879 --> 00:08:26,080 on a phage with polyphenols genes 151 00:08:35,339 --> 00:08:29,889 actually have higher per size when when 152 00:08:38,310 --> 00:08:35,349 they do the lysis and it turns out a 153 00:08:41,100 --> 00:08:38,320 sign of fish god these full of the genes 154 00:08:45,269 --> 00:08:41,110 from horizontal gene transfer so this is 155 00:08:48,180 --> 00:08:45,279 of phenology data I found by a penny 156 00:08:52,470 --> 00:08:48,190 reasons group and this is a published in 157 00:08:55,590 --> 00:08:52,480 the paper by Sullivan at all in 2006 so 158 00:08:58,370 --> 00:08:55,600 here it shows that a in the middle of 159 00:09:02,100 --> 00:08:58,380 bacterial gene for photosynthesis to 160 00:09:04,920 --> 00:09:02,110 adrenal drains and the viruses the genes 161 00:09:05,820 --> 00:09:04,930 appear in the middle of the bacterial 162 00:09:08,430 --> 00:09:05,830 genes 163 00:09:10,710 --> 00:09:08,440 so this is a the evidence of harsh on 164 00:09:16,590 --> 00:09:10,720 gene transfer that viruses cartaginés 165 00:09:19,530 --> 00:09:16,600 from bacteria so I their interpretation 166 00:09:22,560 --> 00:09:19,540 for this data is that bacteria viruses 167 00:09:25,079 --> 00:09:22,570 can form a collective States and create 168 00:09:27,360 --> 00:09:25,089 a global reservoir for photosynthesis 169 00:09:30,449 --> 00:09:27,370 genes that benefits both bacteria and 170 00:09:34,740 --> 00:09:30,459 viruses and in this talk we're going to 171 00:09:40,230 --> 00:09:34,750 a show by a mineral model how this can 172 00:09:42,949 --> 00:09:40,240 actually work so in our model I the 173 00:09:45,120 --> 00:09:42,959 mechanism for hard on gingers fear is 174 00:09:46,880 --> 00:09:45,130 down through the process called 175 00:09:51,360 --> 00:09:46,890 generalized transduction 176 00:09:53,490 --> 00:09:51,370 so in general instruction a virus finds 177 00:09:56,490 --> 00:09:53,500 a bacterial cell and infects it and 178 00:10:00,720 --> 00:09:56,500 insert is genome into the bacterial cell 179 00:10:04,199 --> 00:10:00,730 and it will hijack bacterias machinery 180 00:10:06,660 --> 00:10:04,209 and so make bacteria to produce many 181 00:10:10,230 --> 00:10:06,670 copies of viruses and the bacteria will 182 00:10:12,780 --> 00:10:10,240 die but occasionally very few of baby 183 00:10:16,470 --> 00:10:12,790 viruses would accidentally 184 00:10:20,940 --> 00:10:16,480 grab some bacteria cheek genes 185 00:10:23,910 --> 00:10:20,950 so this genes will function like normal 186 00:10:27,870 --> 00:10:23,920 viruses instead they were more like 187 00:10:31,170 --> 00:10:27,880 zombies so when when one of Samba 188 00:10:34,490 --> 00:10:31,180 viruses float and find new bacterial 189 00:10:37,319 --> 00:10:34,500 cell and in fact the new bacterial cell 190 00:10:40,170 --> 00:10:37,329 this new better cell won't die and 191 00:10:44,550 --> 00:10:40,180 instead they can utilize the bacterial 192 00:10:47,819 --> 00:10:44,560 genes from the previous bacteria so this 193 00:10:51,019 --> 00:10:47,829 is how the bacteria can can can 194 00:10:55,110 --> 00:10:51,029 penetrate from a viral pretty predation 195 00:10:58,889 --> 00:10:55,120 so how does that harder in gene transfer 196 00:11:07,819 --> 00:10:58,899 I can work correctly this is how the 197 00:11:10,889 --> 00:11:07,829 story can work yeah this color area 198 00:11:13,949 --> 00:11:10,899 shows BIOS a fear of bacteria and 199 00:11:17,610 --> 00:11:13,959 viruses and the plus sign here for 200 00:11:19,810 --> 00:11:17,620 simplicity stands for the beneficial 201 00:11:22,900 --> 00:11:19,820 genes that 202 00:11:26,290 --> 00:11:22,910 for example provides more efficient 203 00:11:30,540 --> 00:11:26,300 metabolism and the money signs I stands 204 00:11:34,840 --> 00:11:30,550 for jeans that that are less efficient 205 00:11:37,090 --> 00:11:34,850 so first viruses can get a get paid paid 206 00:11:40,600 --> 00:11:37,100 back bacterias genes from hard earned 207 00:11:42,610 --> 00:11:40,610 gene transfer and the key point here is 208 00:11:43,410 --> 00:11:42,620 that the viruses has higher mutation 209 00:11:46,690 --> 00:11:43,420 rate 210 00:11:49,600 --> 00:11:46,700 so therefore the pig the viruses are 211 00:11:53,260 --> 00:11:49,610 like a generator of new genes and they 212 00:11:55,660 --> 00:11:53,270 create either better gene or worse genes 213 00:11:58,780 --> 00:11:55,670 and they don't list genes back to 214 00:12:01,870 --> 00:11:58,790 bacteria and now bacteria got the 215 00:12:05,230 --> 00:12:01,880 nutrients from viruses and then our key 216 00:12:08,740 --> 00:12:05,240 point is that bacteria here the string 217 00:12:10,990 --> 00:12:08,750 Adu streamlining so they behave like a 218 00:12:12,990 --> 00:12:11,000 filter so they filled out of the 219 00:12:16,150 --> 00:12:13,000 belgians and only bacteria with 220 00:12:19,060 --> 00:12:16,160 beneficial genes can survive so by this 221 00:12:22,540 --> 00:12:19,070 way the bacteria will save the 222 00:12:27,579 --> 00:12:22,550 penetrator genes and will transfer these 223 00:12:29,890 --> 00:12:27,589 pathogens to back to viruses and viruses 224 00:12:32,110 --> 00:12:29,900 would get the better chains and that 225 00:12:35,100 --> 00:12:32,120 this processor will repeat again and 226 00:12:41,340 --> 00:12:35,110 again and in the end the whole biosphere 227 00:12:51,160 --> 00:12:46,360 next I'm going to show how show a 228 00:12:55,620 --> 00:12:51,170 sequence of models by adding a step by 229 00:13:00,220 --> 00:12:55,630 step a different evolutionary mechanisms 230 00:13:03,100 --> 00:13:00,230 to explain how the previous cartoon 231 00:13:05,230 --> 00:13:03,110 picture can actually work so first I'll 232 00:13:08,550 --> 00:13:05,240 consider a very simple model where 233 00:13:12,220 --> 00:13:08,560 bacteria and viruses have a type of 234 00:13:16,720 --> 00:13:12,230 infection predation gene and I will show 235 00:13:20,910 --> 00:13:16,730 how how our engines transfer well I will 236 00:13:24,190 --> 00:13:20,920 have is a attacked and AJ I have a 237 00:13:27,160 --> 00:13:24,200 considered different type of model where 238 00:13:30,240 --> 00:13:27,170 bacteria viruses have no type of genes 239 00:13:33,280 --> 00:13:30,250 which stands for photosynthesis 240 00:13:37,150 --> 00:13:33,290 efficiency and we'll see the effect 241 00:13:40,240 --> 00:13:37,160 hurricane transfer as well and for the 242 00:13:44,829 --> 00:13:40,250 next model I'll consider the case where 243 00:13:48,389 --> 00:13:44,839 they the genes have deleterious mutation 244 00:13:54,280 --> 00:13:48,399 and in the end I'll include a the photo 245 00:13:58,059 --> 00:13:54,290 gradient as the energy flow okay so this 246 00:14:00,610 --> 00:13:58,069 is the first model the first model is 247 00:14:02,590 --> 00:14:00,620 very simple we consider a bacteria and 248 00:14:08,129 --> 00:14:02,600 the viruses as prey and the prey here 249 00:14:13,329 --> 00:14:08,139 and here the ecological processes here 250 00:14:17,350 --> 00:14:13,339 like the chemical reactions so here 251 00:14:20,650 --> 00:14:17,360 bacteria we assume bacteria and viruses 252 00:14:29,259 --> 00:14:20,660 have a type of genes whose value stands 253 00:14:32,740 --> 00:14:29,269 for the ability to defense or attack so 254 00:14:36,790 --> 00:14:32,750 here for example a pea with Sabine des i 255 00:14:40,030 --> 00:14:36,800 stands for bacteria individual with some 256 00:14:43,180 --> 00:14:40,040 dream value i and the first process here 257 00:14:46,990 --> 00:14:43,190 is that this bacteria has some 258 00:14:51,939 --> 00:14:47,000 probability be per unit time to produce 259 00:14:54,340 --> 00:14:51,949 another sim copy and an hour process of 260 00:14:57,490 --> 00:14:54,350 partition means that a different 261 00:14:59,949 --> 00:14:57,500 bacteria would compete with the same 262 00:15:02,019 --> 00:14:59,959 food resource so one of bacteria would 263 00:15:06,480 --> 00:15:02,029 die and both bacteria and the viruses 264 00:15:11,230 --> 00:15:06,490 will die and then naturally with some 265 00:15:15,519 --> 00:15:11,240 probability and if in predation one 266 00:15:18,519 --> 00:15:15,529 bacteria in counts viruses if the virus 267 00:15:20,920 --> 00:15:18,529 carries a gene whose value is larger 268 00:15:24,639 --> 00:15:20,930 than the dream value of that bacterial 269 00:15:28,980 --> 00:15:24,649 cell there is a probability that this 270 00:15:32,679 --> 00:15:28,990 Petrea would be killed and a amount of 271 00:15:38,199 --> 00:15:32,689 faith per size of baby viruses will be 272 00:15:41,500 --> 00:15:38,209 produced and the problem of Liz Malo is 273 00:15:46,389 --> 00:15:41,510 largely the phage burst size and here is 274 00:15:47,140 --> 00:15:46,399 large is about ten to hundreds so we 275 00:15:50,250 --> 00:15:47,150 would imagine 276 00:15:52,780 --> 00:15:50,260 that the the viruses so could easily 277 00:15:54,970 --> 00:15:52,790 overwhelm to the hop-hop population and 278 00:15:56,860 --> 00:15:54,980 drive the extinction of bacteria and 279 00:15:59,590 --> 00:15:56,870 this is what are we observing our 280 00:16:02,050 --> 00:15:59,600 simulations this is a population of 281 00:16:06,070 --> 00:16:02,060 bacteria and viruses the Redis virus a 282 00:16:09,130 --> 00:16:06,080 bacteria is in boo and we see that the 283 00:16:12,130 --> 00:16:09,140 the population of viruses would 284 00:16:19,300 --> 00:16:12,140 oscillate a lot and finally we'll drive 285 00:16:23,560 --> 00:16:19,310 the population to extinction however 286 00:16:26,520 --> 00:16:23,570 life finds his own solution in reality 287 00:16:30,220 --> 00:16:26,530 we know that genes can mutate so we 288 00:16:33,430 --> 00:16:30,230 consider a mutation of the genes for 289 00:16:36,220 --> 00:16:33,440 example when a bacteria we dream well I 290 00:16:39,400 --> 00:16:36,230 give the first to another copy 291 00:16:42,280 --> 00:16:39,410 there's additional probability mu B here 292 00:16:44,560 --> 00:16:42,290 for a new copy to mutate into a 293 00:16:48,190 --> 00:16:44,570 different ring values okay 294 00:16:50,770 --> 00:16:48,200 so n stand for the predation so for 295 00:16:53,740 --> 00:16:50,780 predation there's a additional poverty 296 00:16:57,630 --> 00:16:53,750 movie that a lot of baby viruses would 297 00:17:03,070 --> 00:16:57,640 mutate into different gene values from 298 00:17:06,310 --> 00:17:03,080 its mother so in this case we found that 299 00:17:07,000 --> 00:17:06,320 the postpay tree and the viral genes can 300 00:17:10,329 --> 00:17:07,010 Co evolve 301 00:17:12,520 --> 00:17:10,339 so this shows Jun value the averaging 302 00:17:14,770 --> 00:17:12,530 values of bacteria and viruses as a 303 00:17:18,699 --> 00:17:14,780 function of time and we can see they're 304 00:17:21,810 --> 00:17:18,709 both of layering values can evolve and 305 00:17:24,390 --> 00:17:21,820 increase and it turns out in this case 306 00:17:31,150 --> 00:17:24,400 that the post bacteria and the viruses 307 00:17:33,240 --> 00:17:31,160 can come up and avoid the extinction so 308 00:17:39,040 --> 00:17:33,250 however there is a null problem 309 00:17:42,100 --> 00:17:39,050 apartments that usually the bacteria has 310 00:17:45,400 --> 00:17:42,110 slower mutation rate than viruses so in 311 00:17:47,530 --> 00:17:45,410 this case the virus would mutate much 312 00:17:52,390 --> 00:17:47,540 quicker than bacteria so it's gene 313 00:17:54,820 --> 00:17:52,400 values a grows faster in wait time in 314 00:17:56,590 --> 00:17:54,830 the end them means that virus will 315 00:18:01,080 --> 00:17:56,600 become stronger and stronger and 316 00:18:05,770 --> 00:18:01,090 eventually still outcompetes bacteria 317 00:18:08,770 --> 00:18:05,780 again my finds his own solution so if we 318 00:18:10,830 --> 00:18:08,780 consider how to drain gene transfer like 319 00:18:16,530 --> 00:18:10,840 a general instruction process we 320 00:18:20,920 --> 00:18:16,540 explained before so these two processes 321 00:18:23,080 --> 00:18:20,930 well describe the gene flow from 322 00:18:25,510 --> 00:18:23,090 bacteria of Tobias and in virus to 323 00:18:28,590 --> 00:18:25,520 bacteria and viruses with high mutation 324 00:18:32,050 --> 00:18:28,600 rate can keep generator new genes and 325 00:18:34,750 --> 00:18:32,060 they can transfer these genes to 326 00:18:37,360 --> 00:18:34,760 bacteria and eventually form a flow of 327 00:18:41,230 --> 00:18:37,370 beneficial genes and that beneficial 328 00:18:44,320 --> 00:18:41,240 gene flow will help let bacteria which 329 00:18:47,200 --> 00:18:44,330 have a slow medium rate so in our 330 00:18:50,500 --> 00:18:47,210 simulation we see again a coevolution of 331 00:18:54,670 --> 00:18:50,510 the genes in bacteria and the viruses 332 00:18:56,050 --> 00:18:54,680 and in it turns out that both bacteria 333 00:18:58,420 --> 00:18:56,060 and the viruses can keep later 334 00:19:02,410 --> 00:18:58,430 pollutions and the ecosystem become 335 00:19:05,050 --> 00:19:02,420 stable again so here we consider a 336 00:19:07,750 --> 00:19:05,060 second type of model previously we 337 00:19:10,620 --> 00:19:07,760 consider a packet and the viruses have a 338 00:19:13,840 --> 00:19:10,630 type with chain stands for infection 339 00:19:16,600 --> 00:19:13,850 predation but now here we consider a peg 340 00:19:18,910 --> 00:19:16,610 here and the viruses have a different 341 00:19:23,950 --> 00:19:18,920 type of chains and that gene stands for 342 00:19:27,460 --> 00:19:23,960 metal incidences efficiency policy needs 343 00:19:30,340 --> 00:19:27,470 ability so for example this bacteria P 344 00:19:34,860 --> 00:19:30,350 we submit hinges I stands for bacteria 345 00:19:39,250 --> 00:19:34,870 with some Jing value for managing Val I 346 00:19:43,210 --> 00:19:39,260 so it can and we here we for simplicity 347 00:19:46,060 --> 00:19:43,220 we said that I the the first rate of 348 00:19:49,480 --> 00:19:46,070 bacteria is a function of is 349 00:19:51,370 --> 00:19:49,490 photosynthesis value so different 350 00:19:55,480 --> 00:19:51,380 bacteria would have different birth rate 351 00:19:59,100 --> 00:19:55,490 and to create a new copy and similar for 352 00:20:03,330 --> 00:19:59,110 viruses so viruses to have a 353 00:20:06,970 --> 00:20:03,340 photosynthesis jiaying J would have a 354 00:20:10,840 --> 00:20:06,980 different verses that depends on that 355 00:20:14,090 --> 00:20:10,850 dream value so with high dream values 356 00:20:17,860 --> 00:20:14,100 that viruses would have higher 357 00:20:21,049 --> 00:20:17,870 Versailles so in your simulation 358 00:20:24,680 --> 00:20:21,059 similarly in a case that the virus has 359 00:20:27,890 --> 00:20:24,690 higher mutation rate the the viruses 360 00:20:30,919 --> 00:20:27,900 would quickly evolve into very hyper 361 00:20:34,850 --> 00:20:30,929 size and the population will eventually 362 00:20:38,000 --> 00:20:34,860 overwhelm by the bacterial population so 363 00:20:42,970 --> 00:20:38,010 it will drive by the bacteria to 364 00:20:47,600 --> 00:20:42,980 extinction so again if we consider 365 00:20:51,890 --> 00:20:47,610 hard-learned gene transfer then now the 366 00:20:54,100 --> 00:20:51,900 viruses can produce a good genes and and 367 00:20:57,320 --> 00:20:54,110 the transfer of this benefit range of 368 00:21:01,310 --> 00:20:57,330 bacteria then bacteria can also increase 369 00:21:04,549 --> 00:21:01,320 their purse size by utilize viruses 370 00:21:06,409 --> 00:21:04,559 James so in the end the photo sneezed 371 00:21:09,620 --> 00:21:06,419 rings in patreon the virus can call 372 00:21:16,880 --> 00:21:09,630 together again and their population I 373 00:21:20,120 --> 00:21:16,890 can be sustained so next we are going to 374 00:21:25,220 --> 00:21:20,130 consider a different case so in general 375 00:21:28,029 --> 00:21:25,230 for well adapted species I most 376 00:21:31,909 --> 00:21:28,039 mutations are neutral or deleterious 377 00:21:34,730 --> 00:21:31,919 Sudheer engines for mutation accumulates 378 00:21:37,549 --> 00:21:34,740 and they will make individuals die and 379 00:21:40,850 --> 00:21:37,559 the population will shrink in like hey 380 00:21:43,850 --> 00:21:40,860 it's really called mula stretchered in 381 00:21:47,360 --> 00:21:43,860 this case when we publish and decrease 382 00:21:50,930 --> 00:21:47,370 the demography stochastic would be more 383 00:21:54,860 --> 00:21:50,940 important and leading population go to 384 00:21:58,820 --> 00:21:54,870 extinction faster and we think that in 385 00:22:02,270 --> 00:21:58,830 range expansion that they in the like 386 00:22:04,580 --> 00:22:02,280 individuals expand is colony at the 387 00:22:06,529 --> 00:22:04,590 front of the demo for the population 388 00:22:09,850 --> 00:22:06,539 small so democrat party asians are 389 00:22:12,890 --> 00:22:09,860 important so this effect would be 390 00:22:15,620 --> 00:22:12,900 significant so we consider release 391 00:22:21,620 --> 00:22:15,630 affecting all model of follow signals 392 00:22:23,720 --> 00:22:21,630 genes so here we plot the fullest aging 393 00:22:26,870 --> 00:22:23,730 of bacteria and viruses function of time 394 00:22:27,950 --> 00:22:26,880 because of deleterious mutation the 395 00:22:32,629 --> 00:22:27,960 virus with high 396 00:22:35,239 --> 00:22:32,639 mutation rate would would decrease lay 397 00:22:38,180 --> 00:22:35,249 jeans very quickly and in the end of 398 00:22:43,269 --> 00:22:38,190 their population would collapse but if 399 00:22:47,359 --> 00:22:43,279 we turn on the halogen transfer then the 400 00:22:50,060 --> 00:22:47,369 the bacteria now can help viruses we can 401 00:22:52,759 --> 00:22:50,070 see that by looking at a distribution of 402 00:22:54,619 --> 00:22:52,769 a full of sneeze genes in bacteria and 403 00:22:58,399 --> 00:22:54,629 distribution of pollination gene in 404 00:23:01,369 --> 00:22:58,409 viruses so this black lines stands for 405 00:23:06,019 --> 00:23:01,379 the genes transferred from viruses to 406 00:23:08,960 --> 00:23:06,029 bacteria and here black pine stands for 407 00:23:11,749 --> 00:23:08,970 genes transferred from patriotic viruses 408 00:23:15,950 --> 00:23:11,759 and we see that the genes transferred 409 00:23:22,249 --> 00:23:15,960 from viruses to bacteria can range with 410 00:23:25,009 --> 00:23:22,259 a can range over a many values however 411 00:23:29,330 --> 00:23:25,019 the bacteria behaves like a filter that 412 00:23:32,690 --> 00:23:29,340 will keep only the genes which are 413 00:23:38,409 --> 00:23:32,700 beneficial and transfer back to viruses 414 00:23:42,580 --> 00:23:38,419 so viruses I now can have more a 415 00:23:47,269 --> 00:23:42,590 frequencies at a the beneficial trains 416 00:23:50,480 --> 00:23:47,279 so so it turns out because of hard rain 417 00:23:53,600 --> 00:23:50,490 transfer now the photos and any genes in 418 00:23:57,320 --> 00:23:53,610 bacteria viruses won't decrease they 419 00:24:01,100 --> 00:23:57,330 won't degenerate and then they can come 420 00:24:06,200 --> 00:24:01,110 off and their population can be stable 421 00:24:09,680 --> 00:24:06,210 in this ecosystem we can start your face 422 00:24:12,499 --> 00:24:09,690 Tigra of this model so here we plot the 423 00:24:16,249 --> 00:24:12,509 ratio of viruses with beneficial genes 424 00:24:18,980 --> 00:24:16,259 as a function of Harvey and transfer in 425 00:24:22,190 --> 00:24:18,990 the units of mutation rates the in 426 00:24:24,259 --> 00:24:22,200 principle we can change or mutate the 427 00:24:26,749 --> 00:24:24,269 horizontal gene transfer rate by 428 00:24:30,379 --> 00:24:26,759 changing the density of the population 429 00:24:32,029 --> 00:24:30,389 early in individuals here in our 430 00:24:35,869 --> 00:24:32,039 simulation we can choose higher interest 431 00:24:39,769 --> 00:24:35,879 rate and we found that only when the 432 00:24:41,030 --> 00:24:39,779 hard interest rate is large enough then 433 00:24:44,750 --> 00:24:41,040 the bacteria 434 00:24:47,450 --> 00:24:44,760 they can form a creative state so there 435 00:24:51,340 --> 00:24:47,460 will be enough beneficial in enough 436 00:24:55,280 --> 00:24:51,350 population of beneficial genes in 437 00:24:58,250 --> 00:24:55,290 viruses however we also found that a 438 00:25:01,640 --> 00:24:58,260 whole gene transfer rate cannot be too 439 00:25:05,780 --> 00:25:01,650 large if it's too large that means that 440 00:25:08,120 --> 00:25:05,790 all ofus would already have very good 441 00:25:13,580 --> 00:25:08,130 genes and more hard gene transfer were 442 00:25:16,460 --> 00:25:13,590 just eye at the last beneficial genes 443 00:25:22,820 --> 00:25:16,470 into a population so that will give the 444 00:25:25,940 --> 00:25:22,830 system a negative effect so here I want 445 00:25:30,650 --> 00:25:25,950 to address again why this is a 446 00:25:34,010 --> 00:25:30,660 collective state so we found that in our 447 00:25:35,930 --> 00:25:34,020 simulation it is very important that we 448 00:25:38,210 --> 00:25:35,940 have the both ways of heard during 449 00:25:41,600 --> 00:25:38,220 transfer from bacteria viruses and from 450 00:25:44,300 --> 00:25:41,610 viruses or bacteria so therefore they 451 00:25:46,460 --> 00:25:44,310 can keep dependable genes in both 452 00:25:50,930 --> 00:25:46,470 bacteria and viruses so in your 453 00:25:53,780 --> 00:25:50,940 simulation if we turn off either a one 454 00:25:56,870 --> 00:25:53,790 side of her gene transfer for example I 455 00:26:01,660 --> 00:25:56,880 at least I'm pwned the system work 456 00:26:04,430 --> 00:26:01,670 quickly just a class so this shows that 457 00:26:07,130 --> 00:26:04,440 this is a truly cracked state that 458 00:26:11,300 --> 00:26:07,140 requires both ways of how our interest 459 00:26:14,330 --> 00:26:11,310 rate between bacteria and viruses so 460 00:26:17,300 --> 00:26:14,340 finally we include the energy flow by 461 00:26:21,800 --> 00:26:17,310 adding a background of home ingredients 462 00:26:24,170 --> 00:26:21,810 for her gradient see this is the data I 463 00:26:26,510 --> 00:26:24,180 show you at the beginning and in our 464 00:26:28,760 --> 00:26:26,520 simulation we also create a background 465 00:26:36,230 --> 00:26:28,770 of photo intensity as function for tab 466 00:26:38,630 --> 00:26:36,240 and here for we include they I the photo 467 00:26:43,340 --> 00:26:38,640 intensity into the first rate of 468 00:26:46,670 --> 00:26:43,350 bacteria and also we the the test rate 469 00:26:49,460 --> 00:26:46,680 of bacteria depend on the photo 470 00:26:51,010 --> 00:26:49,470 intensity because the cells near surface 471 00:26:54,890 --> 00:26:51,020 wouldn't get burned 472 00:26:58,910 --> 00:26:54,900 okay so in your model 473 00:27:03,170 --> 00:26:58,920 we first start with low light adapted 474 00:27:08,000 --> 00:27:03,180 ecotype that evolve first so they 475 00:27:12,590 --> 00:27:08,010 initially occupied at the the parting of 476 00:27:15,890 --> 00:27:12,600 the sea and then they coexist waste of 477 00:27:18,500 --> 00:27:15,900 viruses and they can do horizontal 478 00:27:22,460 --> 00:27:18,510 transfer with the viruses and the wait 479 00:27:25,040 --> 00:27:22,470 turn on the simulations so eventually we 480 00:27:29,180 --> 00:27:25,050 can see that develop of this low light 481 00:27:32,090 --> 00:27:29,190 adaptive bacteria and evolve into a 482 00:27:36,260 --> 00:27:32,100 different eco type which is highlight 483 00:27:38,450 --> 00:27:36,270 adapted bacteria but the key point is 484 00:27:42,260 --> 00:27:38,460 that if we don't have halogen transfer 485 00:27:46,730 --> 00:27:42,270 it takes very very long time for the low 486 00:27:50,500 --> 00:27:46,740 light of bacteria to mutate into halogen 487 00:27:53,960 --> 00:27:50,510 sorry they highlighted ability by 488 00:27:57,770 --> 00:27:53,970 bacteria through just a pure random 489 00:27:59,750 --> 00:27:57,780 mutation so only through our gene 490 00:28:02,440 --> 00:27:59,760 transfer we can see a relative 491 00:28:06,740 --> 00:28:02,450 reasonable time scale that this 492 00:28:10,850 --> 00:28:06,750 evolution of ecosystem development could 493 00:28:13,910 --> 00:28:10,860 occur so I will show you an animation of 494 00:28:15,970 --> 00:28:13,920 our simulation where the yellow stands 495 00:28:19,810 --> 00:28:15,980 for low light a deadly bacteria 496 00:28:22,730 --> 00:28:19,820 advocating coexisting with the red 497 00:28:26,090 --> 00:28:22,740 viruses the eventual evolved form 498 00:28:31,010 --> 00:28:26,100 highlight a tablet bacteria which is in 499 00:28:33,700 --> 00:28:31,020 blue color so here we initially have 500 00:28:37,640 --> 00:28:33,710 localized area coexist with viruses and 501 00:28:42,020 --> 00:28:37,650 they do harder entrance transfer and the 502 00:28:45,650 --> 00:28:42,030 viruses I diffused with bacteria and 503 00:28:47,770 --> 00:28:45,660 help back to the low light at a Debra 504 00:28:51,140 --> 00:28:47,780 tyria develop into a highlight at the 505 00:28:56,650 --> 00:28:51,150 bacteria and we can see the distinct 506 00:29:04,180 --> 00:29:01,150 so our simulation results explain that 507 00:29:08,510 --> 00:29:04,190 bacteria and phage can help each other 508 00:29:10,190 --> 00:29:08,520 bacteria can get help from viruses 509 00:29:12,890 --> 00:29:10,200 because viruses have higher mutation 510 00:29:15,590 --> 00:29:12,900 rates so they behave like a generator of 511 00:29:20,270 --> 00:29:15,600 new genes the bacteria can get benefit 512 00:29:23,419 --> 00:29:20,280 of genes found Rises and viruses can get 513 00:29:25,820 --> 00:29:23,429 help from bacteria in spectra have new 514 00:29:30,020 --> 00:29:25,830 streamlining and have so mutation rate 515 00:29:33,290 --> 00:29:30,030 so big bacteria can help viruses to 516 00:29:35,750 --> 00:29:33,300 filled out that Jing's and keep only 517 00:29:37,610 --> 00:29:35,760 beneficial genes so in these ways they 518 00:29:42,860 --> 00:29:37,620 can help each other and inform 519 00:29:46,640 --> 00:29:42,870 collective States so here I like to 520 00:29:50,270 --> 00:29:46,650 summarize what is our mineral model can 521 00:29:52,340 --> 00:29:50,280 explain so the result of our model are 522 00:29:55,130 --> 00:29:52,350 consistent ways to the facts in perco 523 00:29:58,640 --> 00:29:55,140 caucus for example precocity has high 524 00:30:02,900 --> 00:29:58,650 string my genome and that limits the 525 00:30:04,910 --> 00:30:02,910 metabolic redundancy so the only way to 526 00:30:10,520 --> 00:30:04,920 improve metabolism is to improve the 527 00:30:17,960 --> 00:30:10,530 efficiency and we know and that means 528 00:30:21,140 --> 00:30:17,970 that a so I and we we know there is no 529 00:30:26,810 --> 00:30:21,150 no immune mechanism I CRISPR professors 530 00:30:29,570 --> 00:30:26,820 in purple caucus and and the that 531 00:30:32,600 --> 00:30:29,580 because bacteria don't need that the 532 00:30:35,330 --> 00:30:32,610 sunny bacteria can balance is i between 533 00:30:37,610 --> 00:30:35,340 the risk of predation and the benefit 534 00:30:41,510 --> 00:30:37,620 and the benefit from hard running 535 00:30:44,299 --> 00:30:41,520 transfer and that well reflects in the 536 00:30:47,990 --> 00:30:44,309 hi at a huge pen genome in perco caucus 537 00:30:52,070 --> 00:30:48,000 and the presence of a genomic i islands 538 00:30:55,700 --> 00:30:52,080 and any also they has chose nation 539 00:30:58,370 --> 00:30:55,710 certifications which is a result from 540 00:31:01,940 --> 00:30:58,380 the viral media the utilization of the 541 00:31:04,820 --> 00:31:01,950 energy flow and also our model shows 542 00:31:08,330 --> 00:31:04,830 that a sign of aged which carry full of 543 00:31:10,580 --> 00:31:08,340 these genes can can help improve full 544 00:31:15,520 --> 00:31:10,590 incisions in the bacteria and as 545 00:31:22,370 --> 00:31:15,530 consistent ways the fact in proto caucus 546 00:31:24,320 --> 00:31:22,380 so here are some take-home messages so 547 00:31:26,720 --> 00:31:24,330 at beginning I show that the planetary 548 00:31:31,310 --> 00:31:26,730 living system theory will require 549 00:31:33,860 --> 00:31:31,320 unified picture of I in ecology which 550 00:31:36,259 --> 00:31:33,870 are there are two aspects one is 551 00:31:38,690 --> 00:31:36,269 population biology and energy flow and 552 00:31:41,629 --> 00:31:38,700 we still need a unified framework for 553 00:31:44,509 --> 00:31:41,639 that and in this talk I use mostly 554 00:31:48,919 --> 00:31:44,519 population dynamics and a little energy 555 00:31:52,909 --> 00:31:48,929 flow and also ecosystems our collective 556 00:31:55,940 --> 00:31:52,919 and our model and simulation shows that 557 00:31:58,399 --> 00:31:55,950 sometimes the eCos instability should be 558 00:32:01,730 --> 00:31:58,409 understood by considering multi state 559 00:32:04,490 --> 00:32:01,740 scale interactions that ranges from the 560 00:32:08,269 --> 00:32:04,500 scale of genes to the scale of the whole 561 00:32:10,970 --> 00:32:08,279 environment another key points that 562 00:32:13,850 --> 00:32:10,980 information flow especially through 563 00:32:17,240 --> 00:32:13,860 halogen gene transfer that provides a 564 00:32:19,639 --> 00:32:17,250 gene flow that allows life to maintain 565 00:32:25,070 --> 00:32:19,649 and expand its range in thermodynamic 566 00:32:27,249 --> 00:32:25,080 radians so with that I stop here and 567 00:32:40,340 --> 00:32:27,259 I'll be happy to take any questions 568 00:32:46,090 --> 00:32:40,350 thank you all right thanks for the great 569 00:32:55,039 --> 00:32:49,490 so the discovery of photosystem 2 genes 570 00:32:58,669 --> 00:32:55,049 in in phage around 15 years ago was very 571 00:33:01,580 --> 00:32:58,679 curious so as I mentioned earlier in a 572 00:33:05,419 --> 00:33:01,590 passing comment about d1 so d1 protein 573 00:33:07,669 --> 00:33:05,429 is the PSB a protein which turns over 574 00:33:11,720 --> 00:33:07,679 very rapidly it also has one of the 575 00:33:15,430 --> 00:33:11,730 fastest promoters of any gene known in a 576 00:33:18,230 --> 00:33:15,440 eukaryotic cell so do you think that the 577 00:33:21,230 --> 00:33:18,240 virus has co-opted these genes in order 578 00:33:23,720 --> 00:33:21,240 to promote their own rapid reproductive 579 00:33:25,519 --> 00:33:23,730 capability within their host cell or is 580 00:33:27,769 --> 00:33:25,529 there some other reason that you can 581 00:33:29,570 --> 00:33:27,779 imagine that these genes very 582 00:33:37,039 --> 00:33:29,580 specifically were incorporated into 583 00:33:44,940 --> 00:33:41,909 so the questions is about the details of 584 00:33:47,430 --> 00:33:44,950 evolution of photosynthesis system genes 585 00:33:52,219 --> 00:33:47,440 but their incorporation of those genes 586 00:33:55,229 --> 00:33:52,229 into viruses into marine viruses rather 587 00:33:56,399 --> 00:33:55,239 was never you know when it was 588 00:34:02,700 --> 00:33:56,409 discovered it was thought it was an 589 00:34:04,259 --> 00:34:02,710 artifact and itself obviously not an 590 00:34:05,969 --> 00:34:04,269 artifact I just wondered if you have any 591 00:34:14,510 --> 00:34:05,979 thoughts as to why those genes would be 592 00:34:16,919 --> 00:34:14,520 in a virus they're very abundant right 593 00:34:27,419 --> 00:34:16,929 so then we try to understand the 594 00:34:35,190 --> 00:34:27,429 question a are you asking that a why 595 00:34:39,240 --> 00:34:35,200 those genes have to appear in viruses so 596 00:34:42,779 --> 00:34:39,250 in photosystem ii there are a series of 597 00:34:47,190 --> 00:34:42,789 genes in cyanobacteria and every alga 598 00:34:49,319 --> 00:34:47,200 and every plant that splits water that 599 00:34:51,750 --> 00:34:49,329 encodes for this one protein called the 600 00:34:57,450 --> 00:34:51,760 d1 protein that if you span one protein 601 00:35:01,170 --> 00:34:57,460 and the gene is photosystem PS be 602 00:35:06,089 --> 00:35:01,180 photosystem 2 subunit a right so it's PS 603 00:35:09,750 --> 00:35:06,099 PA all right now yes B yeah okay so now 604 00:35:12,630 --> 00:35:09,760 when we get to this gene it turns out 605 00:35:15,990 --> 00:35:12,640 this protein product of the of the gene 606 00:35:18,240 --> 00:35:16,000 turns over very rapidly about every 25 607 00:35:20,579 --> 00:35:18,250 to 30 minutes from the end of day when 608 00:35:24,660 --> 00:35:20,589 the light is shining so in order to keep 609 00:35:27,029 --> 00:35:24,670 that repair mechanism going the cell 610 00:35:30,779 --> 00:35:27,039 makes a lot of extra d1 so it has a 611 00:35:33,870 --> 00:35:30,789 promoter that is very very very highly 612 00:35:38,819 --> 00:35:33,880 demanding of RNA so it's a very rapid 613 00:35:40,980 --> 00:35:38,829 the turning over system so it's we use 614 00:35:43,559 --> 00:35:40,990 that promoter a lot in in in molecular 615 00:35:46,200 --> 00:35:43,569 biology to promote other genes I mean we 616 00:35:47,940 --> 00:35:46,210 just cut the the business end of the 617 00:35:48,630 --> 00:35:47,950 gene off and use the promoter so I'm 618 00:35:50,819 --> 00:35:48,640 just wondering 619 00:35:52,589 --> 00:35:50,829 if the gene if you don't have a positive 620 00:35:55,829 --> 00:35:52,599 feedback in the system here that the 621 00:35:58,079 --> 00:35:55,839 virus is actually selected or uses that 622 00:36:00,720 --> 00:35:58,089 gene from a selection point of view in 623 00:36:03,150 --> 00:36:00,730 order to promote its own birth size its 624 00:36:05,180 --> 00:36:03,160 own facilitation of its own growth 625 00:36:09,000 --> 00:36:05,190 within a host cell once it's invaded 626 00:36:10,410 --> 00:36:09,010 you're following my logic here so I'm 627 00:36:12,450 --> 00:36:10,420 just wondering if there's some feedback 628 00:36:14,370 --> 00:36:12,460 here that you could think of I mean is 629 00:36:15,720 --> 00:36:14,380 that you I don't I'm trying to 630 00:36:21,180 --> 00:36:15,730 understand why horizontal gene transfer 631 00:36:23,400 --> 00:36:21,190 of ps/2 genes is occurring so much those 632 00:36:26,490 --> 00:36:23,410 genes it's not Rubisco genes it's not 633 00:36:34,859 --> 00:36:26,500 you know it's not just any random gene 634 00:36:37,200 --> 00:36:34,869 yeah so I think the feedback I picture 635 00:36:41,339 --> 00:36:37,210 you are talking about we can kind of see 636 00:36:48,539 --> 00:36:41,349 that in this contributor right so let me 637 00:36:52,620 --> 00:36:48,549 quickly I repeat again so so the once 638 00:36:56,249 --> 00:36:52,630 the bacteria got some beneficial genes 639 00:37:01,319 --> 00:36:56,259 or more efficient felicitous genes from 640 00:37:03,240 --> 00:37:01,329 viruses the bacteria do streamlining so 641 00:37:07,109 --> 00:37:03,250 that means they keep only small size of 642 00:37:10,680 --> 00:37:07,119 genes small size of genomes so bacteria 643 00:37:13,890 --> 00:37:10,690 who is less efficient chains would just 644 00:37:17,339 --> 00:37:13,900 die out so therefore only bacterial 645 00:37:21,539 --> 00:37:17,349 cells with more efficient genes can 646 00:37:24,779 --> 00:37:21,549 stand out during selection so that will 647 00:37:26,839 --> 00:37:24,789 give who innate to horan transfer and 648 00:37:30,390 --> 00:37:26,849 dump will stream back to you virus 649 00:37:35,099 --> 00:37:30,400 viruses this world give a feedback 650 00:37:39,359 --> 00:37:35,109 effect that increase the change in the 651 00:37:48,390 --> 00:37:39,369 power feel of viruses I think tasks like 652 00:37:48,400 --> 00:37:54,870 [Music] 653 00:37:59,049 --> 00:37:57,279 other cyanobacteria have multiple copies 654 00:38:02,440 --> 00:37:59,059 of the photosystem two genes so I think 655 00:38:04,289 --> 00:38:02,450 it's Seneca sister says three copies to 656 00:38:06,069 --> 00:38:04,299 or two are different from each other and 657 00:38:09,490 --> 00:38:06,079 they're expressed differentially 658 00:38:11,710 --> 00:38:09,500 depending on the stresses I don't know 659 00:38:15,460 --> 00:38:11,720 here I mean - is there more than one 660 00:38:19,000 --> 00:38:15,470 copy in prochlorococcus when they do 661 00:38:20,859 --> 00:38:19,010 these harder in transferred if through 662 00:38:23,410 --> 00:38:20,869 the generalize transaction they would 663 00:38:26,950 --> 00:38:23,420 dump a lot of genes not just full of 664 00:38:30,000 --> 00:38:26,960 energy but also the genes that can coat 665 00:38:34,210 --> 00:38:30,010 the service protein that will help 666 00:38:45,609 --> 00:38:34,220 defense the viruses and that will have 667 00:38:51,490 --> 00:38:45,619 its benefits in the diversity it's not 668 00:38:53,799 --> 00:38:51,500 just wondering yeah beautiful work 669 00:38:55,510 --> 00:38:53,809 I have a question that perhaps bridges a 670 00:38:58,299 --> 00:38:55,520 little bit what Paul is talking about 671 00:39:01,120 --> 00:38:58,309 and extensions of these ideas that I was 672 00:39:04,180 --> 00:39:01,130 wondering about which is have you 673 00:39:07,660 --> 00:39:04,190 thought about for example you know 674 00:39:10,059 --> 00:39:07,670 having other types of organisms around 675 00:39:12,640 --> 00:39:10,069 other phytoplankton or other bacteria 676 00:39:14,260 --> 00:39:12,650 where but possibly in sort of rare 677 00:39:16,260 --> 00:39:14,270 circumstances you could have cross 678 00:39:18,730 --> 00:39:16,270 infections between the phage 679 00:39:20,980 --> 00:39:18,740 transferring genes across species being 680 00:39:24,099 --> 00:39:20,990 a font of innovation and the reason I 681 00:39:25,480 --> 00:39:24,109 ask that is that in the so in the 682 00:39:27,099 --> 00:39:25,490 evolution of prochlorococcus actually 683 00:39:29,370 --> 00:39:27,109 not just photosynthesis genes that are 684 00:39:31,599 --> 00:39:29,380 being transferred but all the major 685 00:39:32,980 --> 00:39:31,609 divergences within the lineage at the 686 00:39:34,450 --> 00:39:32,990 sort of macro from each nighttime skill 687 00:39:38,079 --> 00:39:34,460 look like they're horizontal genes 688 00:39:39,700 --> 00:39:38,089 coming from different groups so so you 689 00:39:40,779 --> 00:39:39,710 know in connection to what Paul is 690 00:39:42,250 --> 00:39:40,789 saying it's actually not just 691 00:39:43,750 --> 00:39:42,260 photosynthesis genes for which this sort 692 00:39:45,309 --> 00:39:43,760 of thing seems to happen and I just 693 00:39:48,789 --> 00:39:45,319 wondering about the role of phages in 694 00:39:51,849 --> 00:39:48,799 those kinds of transfers yes I believe 695 00:39:55,089 --> 00:39:51,859 well there are different examples that 696 00:39:57,700 --> 00:39:55,099 show similar mechanisms right so for 697 00:40:01,120 --> 00:39:57,710 example I there are different type of 698 00:40:04,029 --> 00:40:01,130 sound bacteria that now live in ocean 699 00:40:04,750 --> 00:40:04,039 but they've rocks so they will form 700 00:40:08,470 --> 00:40:04,760 different there 701 00:40:11,770 --> 00:40:08,480 rocks and they were utilize a different 702 00:40:15,280 --> 00:40:11,780 intensity and frequency of light depends 703 00:40:19,060 --> 00:40:15,290 on the depth of the rock layers yeah 704 00:40:23,050 --> 00:40:19,070 yeah and we also believe might be 705 00:40:24,400 --> 00:40:23,060 similar mechanism here right so there's 706 00:40:25,840 --> 00:40:24,410 a particular gene you might find 707 00:40:27,970 --> 00:40:25,850 interesting to look at which sits in the 708 00:40:30,580 --> 00:40:27,980 electron transfer chain called the 709 00:40:31,720 --> 00:40:30,590 plastic window terminal oxidase and that 710 00:40:34,180 --> 00:40:31,730 looks like it's being transferred 711 00:40:36,460 --> 00:40:34,190 between viruses of different different 712 00:40:39,930 --> 00:40:36,470 phytoplankton groups yes full bacteria 713 00:40:44,490 --> 00:40:39,940 suites of reefs muttering sighs and 714 00:40:47,260 --> 00:40:44,500 gasps we could say similar in fact that 715 00:40:51,790 --> 00:40:47,270 for example there might be evidence that 716 00:40:54,610 --> 00:40:51,800 you can find some genes in bacteria that 717 00:40:59,370 --> 00:40:54,620 come from viruses and that that would be 718 00:41:02,200 --> 00:40:59,380 a example that these mechanism can work 719 00:41:04,710 --> 00:41:02,210 so to just go back answer your question 720 00:41:07,060 --> 00:41:04,720 and poles and 4G Paul was just leaving 721 00:41:09,100 --> 00:41:07,070 but but if special here about 722 00:41:12,010 --> 00:41:09,110 photosynthesis genes these are both the 723 00:41:14,440 --> 00:41:12,020 bacteria and the phage can utilize them 724 00:41:16,840 --> 00:41:14,450 in their life cycle whereas other genes 725 00:41:18,880 --> 00:41:16,850 that are that are horizontally 726 00:41:21,970 --> 00:41:18,890 transferred from bacteria to bacteria 727 00:41:24,670 --> 00:41:21,980 the fade is just the bus that takes them 728 00:41:26,650 --> 00:41:24,680 from one place to the other but what's 729 00:41:28,930 --> 00:41:26,660 special and what is the feedback that 730 00:41:31,660 --> 00:41:28,940 Paul was asking about is the fact that 731 00:41:33,910 --> 00:41:31,670 both the bacteria and the phage are able 732 00:41:37,150 --> 00:41:33,920 to directly utilize the photosynthesis 733 00:41:39,160 --> 00:41:37,160 the PSB genes in their in their life 734 00:41:41,650 --> 00:41:39,170 cycle so that that's that's what gives 735 00:41:49,870 --> 00:41:41,660 you the non nonlinear feedback to 736 00:41:51,490 --> 00:41:49,880 stabilize the system hungin said these 737 00:41:57,990 --> 00:41:51,500 are the systems where we know there's 738 00:42:12,100 --> 00:41:58,000 other genes I can do other questions a 739 00:42:14,080 --> 00:42:12,110 couple more minutes so it's not a fair 740 00:42:18,060 --> 00:42:14,090 question because it's not well thought 741 00:42:20,190 --> 00:42:18,070 out but as I listened to the talk 742 00:42:22,830 --> 00:42:20,200 I have a question that's much like when 743 00:42:24,180 --> 00:42:22,840 we listen to chemistry talks when we 744 00:42:25,740 --> 00:42:24,190 listen to the chemistry talks at the 745 00:42:27,450 --> 00:42:25,750 beginning of the meeting we have a 746 00:42:29,790 --> 00:42:27,460 problem of getting from some starting 747 00:42:31,500 --> 00:42:29,800 point to some molecule and most things 748 00:42:34,800 --> 00:42:31,510 we try don't work and eventually we find 749 00:42:36,870 --> 00:42:34,810 a solution that gets there in population 750 00:42:38,520 --> 00:42:36,880 processes if you take the properties 751 00:42:39,960 --> 00:42:38,530 that you think are native properties of 752 00:42:42,300 --> 00:42:39,970 bacteria and you think our native 753 00:42:44,370 --> 00:42:42,310 properties of viruses and you plug them 754 00:42:46,320 --> 00:42:44,380 into a model the model goes unstable and 755 00:42:47,760 --> 00:42:46,330 everyone dies and you change some 756 00:42:50,160 --> 00:42:47,770 details and it goes unstable and 757 00:42:51,780 --> 00:42:50,170 everyone dies and eventually you work 758 00:42:56,180 --> 00:42:51,790 and you work and you find a model that 759 00:42:59,550 --> 00:42:56,190 can produce stable population processes 760 00:43:01,320 --> 00:42:59,560 at the end you tell a story where 761 00:43:03,240 --> 00:43:01,330 there's a kind of a general set of 762 00:43:06,390 --> 00:43:03,250 principles that you think are at work 763 00:43:08,760 --> 00:43:06,400 that viruses handle some jobs bacteria 764 00:43:11,450 --> 00:43:08,770 handle other jobs and it's a collective 765 00:43:15,090 --> 00:43:11,460 state that produces stable populations 766 00:43:17,670 --> 00:43:15,100 how much can you show that even if a 767 00:43:19,890 --> 00:43:17,680 particular model is wrong those 768 00:43:22,590 --> 00:43:19,900 principles are likely to be the real 769 00:43:25,410 --> 00:43:22,600 reason you have stable populations in 770 00:43:26,490 --> 00:43:25,420 the world and maybe some other model 771 00:43:28,590 --> 00:43:26,500 that's different than the one you 772 00:43:30,210 --> 00:43:28,600 thought of but that's organized by the 773 00:43:36,900 --> 00:43:30,220 same principles is still the correct 774 00:43:37,890 --> 00:43:36,910 explanation okay let me try to 775 00:43:42,900 --> 00:43:37,900 understand your question 776 00:43:48,260 --> 00:43:42,910 I guess you're asking that if this 777 00:43:51,510 --> 00:43:48,270 mechanism is robust enough or is just 778 00:43:54,150 --> 00:43:51,520 requires many different conditions to 779 00:43:59,750 --> 00:43:54,160 make it work okay so we single-edged 780 00:44:02,000 --> 00:43:59,760 mechanisms wrote robust first we can I 781 00:44:04,980 --> 00:44:02,010 predict the phase diagram and that 782 00:44:08,340 --> 00:44:04,990 Peregrine's is consistent with a little 783 00:44:15,330 --> 00:44:08,350 data and we can show a phenomena in real 784 00:44:17,730 --> 00:44:15,340 data and and it's not it doesn't require 785 00:44:20,220 --> 00:44:17,740 fine tuning of parameter in our 786 00:44:27,390 --> 00:44:20,230 simulations right so which means the 787 00:44:29,880 --> 00:44:27,400 result is stable so and we might be able 788 00:44:32,039 --> 00:44:29,890 to think of different kind of models 789 00:44:35,819 --> 00:44:32,049 that maybe if 790 00:44:38,760 --> 00:44:35,829 type of bacteria weighs Patriot was 791 00:44:42,779 --> 00:44:38,770 different mutation rate but that way in 792 00:44:48,150 --> 00:44:42,789 order to see this in fact some fun 793 00:44:52,109 --> 00:44:48,160 phenomena in real like what we I show 794 00:44:55,079 --> 00:44:52,119 here the niche stratification and the 795 00:44:59,779 --> 00:44:55,089 collective effects we also need some 796 00:45:03,569 --> 00:44:59,789 other conditions like which would be to 797 00:45:09,660 --> 00:45:03,579 manipulating my if it's not bacteria and 798 00:45:14,549 --> 00:45:09,670 viruses like in this Rio ecosystems we 799 00:45:26,370 --> 00:45:14,559 thing is I it could be I I wish I thing 800 00:45:28,620 --> 00:45:26,380 is a robust mechanism it has 801 00:45:30,720 --> 00:45:28,630 automatically homeostasis for the 802 00:45:33,000 --> 00:45:30,730 ecosystem built in whereas other 803 00:45:36,990 --> 00:45:33,010 proposed mechanisms for the stability of 804 00:45:39,180 --> 00:45:37,000 phage systems like low dimensionality or 805 00:45:43,500 --> 00:45:39,190 things like this that those really don't 806 00:45:44,789 --> 00:45:43,510 have homeostasis emerging naturally you 807 00:45:47,700 --> 00:45:44,799 have to do some fine-tuning of 808 00:45:50,250 --> 00:45:47,710 parameters as high and said here without 809 00:45:52,470 --> 00:45:50,260 if generically stable is a stage it's 810 00:45:54,420 --> 00:45:52,480 not the it's not the only mechanism that 811 00:45:57,180 --> 00:45:54,430 that can do this but in this particular 812 00:46:00,380 --> 00:45:57,190 system it seems to be the one that is 813 00:46:05,160 --> 00:46:00,390 appropriate because of the utilization 814 00:46:10,829 --> 00:46:05,170 by both biosphere and bacteria sphere of 815 00:46:12,359 --> 00:46:10,839 the funding systems all right it's this 816 00:46:13,330 --> 00:46:12,369 rate at time so let's thank the speaker 817 00:46:16,630 --> 00:46:13,340 again 818 00:46:35,630 --> 00:46:16,640 [Applause]