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[Music]

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[Applause]

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yeah thank you so I think one of the

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overarching challenges facing

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astrobiology and origins of life

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research is this need to find a set of

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principles that we know can apply to

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both detailed and general problems of

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living systems in a variety of contexts

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and this is generally been challenging

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thus far and the hope is that we can

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someday find this set of principles

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today I'll talk mostly about how we can

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go from extant life to thinking about

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the types of general principles that

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that life points us towards so I think

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typically there are two ends of a

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spectrum for thinking about the

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principles of life at one end we have a

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set of abstract theoretical principles

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typically mathematical or computational

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and these are often very general and we

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should expect to apply to any living

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system for which we can shape them into

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one of these constructs and at the other

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end we have all of the life we've seen

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and the question is can we take all the

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life that we've seen and extract another

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set of principles from that as well and

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the hope is that from both ends of the

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spectrum we can find some set of general

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principles once you have those you can

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build some universal theory of life

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similar to the universal theories of

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chemistry and physics that we already

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have and then once you have this theory

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you can expand back out into specific

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applications such as thinking of a

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variety of origins of life or thinking

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about searching for life in

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astrobiological context so at this end

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of the spectrum where we have these

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abstract theoretical principles we

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already have some of these that's worth

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noting examples include the error

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threshold or pattern formation so for

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the error threshold we know if you can

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formalize how a system replicates and

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you can formalize what the inheritance

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mechanism is then you can set a maximum

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mutation rate in order for that system

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to be able to evolve now the trick here

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becomes abstracting a variety of systems

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into this framework but if you can do

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that we have a lot to say so this is one

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nice principle that we already have

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another would be pattern formation we

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understand how certain sorts of

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dynamical

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systems give rise to patterns naturally

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even give rise to sort of naturally

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replicating systems and so in this end

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of the spectrum we have two of a very

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long list of principles we've already

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built up and then for extant life we

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have a lot of very detailed knowledge

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about particular physiology or what our

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actual genetic inheritance mechanism

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looks like what the central dogma of

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molecular biology looks like and so the

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question is how do we take those

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specific details and generalize them

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into this set of general principles

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where they start to meet these abstract

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theoretical concepts that we already

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have and so this is what I'll talk about

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today is how we can look at life in a

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slightly different way than we typically

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have and start to find some generalities

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there one nice example is the

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observation of allometric scaling or

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power laws in biology so these show up

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for a variety of classes of organisms

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and what they typically show is that if

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you have many orders of magnitude and

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organism size plotted against some

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feature of an organism in this case

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metabolic rate you see that all

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organisms within a class fall along a

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single power law relationship with some

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amount of scatter so this is sort of

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amazing because it says that despite all

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of the detailed evolution that's

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occurring adaptation to particular

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niches particular evolutionary histories

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path dependency despite that there's

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some set of constraints common enough to

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a body plan to give rise to these single

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regularities for diverse organisms

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what's also interesting about these

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relationships is that they shift as you

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go across organism architecture so these

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are all single-cell bacteria then you

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shift to the single-cell eukaryotes and

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then you go to small multicellular

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eukaryotes and each time you do that the

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exponent of this power loss shifts

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telling you that there's some shift in

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constraints associated with architecture

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and metabolism so this is great and we

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can just understand what principles are

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causing this we might be able to say

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something very general about the

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connection between organism architecture

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and various physiological features we

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can also take some

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like this and make it much more detailed

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for example by setting up a very simple

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partitioning of metabolism so we say in

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general metabolism needs to be

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partitioned between some ability to

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produce new biomass and towards

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replication and some amount of energy to

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maintain the biomass that you already

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have because there are certainly decay

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processes and then we can say metabolism

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equals growth plus maintenance we can

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rewrite that in a more detailed bio

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energetic form where you have metabolism

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on the left-hand side here is equal to

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some power law and that equals some

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energy to produce a new unit of biomass

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times the rate of biomass production

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plus some energy to maintain an existing

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unit of biomass times how much mass you

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actually have and then you can solve all

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of this to predict a variety of things

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such as the growth trajectory of a

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single organism through its life cycle

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or the population growth rate of

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organisms where if we know the sort of

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average bioenergetic parameters of life

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which we do we can then make these broad

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predictions for say all bacteria all

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units are eukaryotes and all small

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multicellular organisms in terms of the

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the specific population growth rate as a

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function of body size and so again we

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see these architectural shifts showing

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up here whereas bacteria become larger

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they are grow faster and faster whereas

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the units are eukaryotes as body size

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evolved to be larger grow more slowly

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and this is due to the difference in

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these energetics as connected with

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architecture and we see that we can

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predict distinct scales where these

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architectures break down so we can set a

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minimum size on bacteria where

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maintenance becomes the entire

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metabolism we can set a maximum size on

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unis or eukaryotes again where

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maintenance becomes the entire

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metabolism and here we see that this

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does a good job of predicting where you

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stop seeing unicel eukaryotic life and

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you start seeing small multicellular

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eukaryotic life so this predicts the

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need for an evolutionary transition and

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this sets a lower bound and sort of the

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smallest bacteria we see which agrees

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very well with the the recent

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measurements out of gel Banfield's lab

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which has the new right world record

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holder for smallest life so this is

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really nice we now have these broad

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taxonomic predictions we can understand

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where architects

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fail and we can also continue to drill

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down on one class of organisms to try

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and understand something more specific

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about the physiology again from a very

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average or general perspective so for

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example one thing we can do is knowing

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what we know about the energetics of

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these organisms we can start to predict

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the need for ribosomes at various scales

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so we could take the average physiology

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of the central dogma and break out each

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component and understand what's driving

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that and so in this case I'm showing our

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theory for the predicted number of

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ribosomes converted to volume units as a

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function of cell size just in bacteria

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this is the total cell size this is sort

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of a best power-law fit to those data

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and here are the very messy data from a

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variety of bacteria where this is messy

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mostly because ribosomes are sort of one

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of the main dimensions where organisms

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tune physiology so we see here is we do

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a good job of predicting this broad

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consistency of organisms we also predict

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an upper bound on bacterial size right

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so we predict now that there becomes a

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distinct scale at which you would need

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more ribosomes than can fit in the cell

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so this now predicts an upper bound on

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bacteria which we didn't have in this

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relationship but because this growth

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rate is increasing quickly you need more

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you need ribosomes to keep up with that

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and so eventually that becomes

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impossible so we have another reason for

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this architecture breaking down now I

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won't show it but we've expanded this to

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sort of capture all of the main

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components of bacterial physiology here

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is total cell volume for reference and

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then everything else has converted to

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volume units for cross comparison and

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you can see that bacteria across their

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range have sort of this dramatic change

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in both physiology where they go from

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being mostly composed of proteins and

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DNA encapsulated in a membrane to being

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mostly RNA components as the cell

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becomes larger and eventually this

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limits their total size so this has a

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variety of implications for what you

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might measure stoichiometrically in an

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environment

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it says that on average bacterial

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physiology is not comparable and you

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should expect specific environments to

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select four distinct sizes of bacteria

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these are all really nice things to know

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ahead of time it also tells us something

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about physiological flexibility because

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you can see that at both the small and

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large end of bacteria they're running

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out of space for the essential

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physiology so we have the space

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constraint that limits now both ends of

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the spectrum this happens at the same

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size that we saw this energetic

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restriction and in between these two you

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have sort of this extra space seems to

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be mostly filled with water but what

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comes with that is an added flexibility

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to say increase the number of a

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particular protein in response to

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environmental fluctuation so we also now

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predict that there's differences and the

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responsiveness of bacterial physiology

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at different size scales again telling

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us something about what sorts of

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environment should to select for what

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sorts of species now everything I've

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said has been very much from an

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optimization perspective so we've looked

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at for example the central dogma in

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terms of what would be optimal for

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cellular physiology as a function of

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these size shifts but one could ask what

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are the boundaries of this physiology so

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it's hard to map what you would do in

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every particular niche but you might get

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around that by just saying what are sort

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of how far could you push to push this

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physiology to understand its its

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ultimate bound and so that's sort of the

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hope is how do we now start to abstract

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this even further to just say even for

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life as we know it what is what is the

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hard boundary around that look like for

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upper and lower bounds of various

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processes it's one way we can do that is

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to say well what happened if life had a

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differently different evolutionary

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history what if we discovered a ribosome

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that was better or worse than was

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discovered by the evolutionary

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trajectory that we had and so you can

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think about the ribosome basically as

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the number of base pairs that can

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produce per second given its size and so

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you get sort of this base pairs per

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second for volume is the critical

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parameter and all these models for

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ribosomes and if you took the ribosomes

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to be exactly the size as the one that

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we know but you said imagine it was able

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to process base pairs

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ten times faster or 40 times slower well

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if it could process 10 times faster you

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actually get an extra order of magnitude

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on the upper bound for bacteria so you

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should expect all bacteria to be larger

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you expect to see where that transition

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between bacterial architecture and

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unicellular unicel you carry I got

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architecture to be pushed to a larger

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size regime now it's up to the

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biochemists to decide if this is if this

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is actually possible or not but we can

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sort of in an abstract sense say once we

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know the biochemical limit we can back

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out on the physiological limit and now

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if the ribosome was 40 times slower you

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can't get encapsulated life at all so

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you if for all sizes it's impossible to

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have enough ribosomes to actually run

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cellular replication I'm holding some

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other features of cell physiology

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constant here that should be noted so

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you might be able to tune those a little

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bit but given that conditional statement

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we can sort of understand the effect of

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a better or worse ribosome discovered in

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evolutionary history so I think taking

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all of that together what this shows is

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that by looking at this macro physiology

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and thinking about these scaling laws

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there are ways to start to go from the

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broad diversity of life that we've seen

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to some set of general principles about

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that life I haven't talked much about it

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but in lots of the cases where we've

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looked and predicted this these cross

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species relationships we understand sort

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of the dominant physical constraint or

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the main physical process limiting that

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so that makes those predictions very

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general and then you can play the sorts

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of games I just described about the

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layering the detailed physiology on top

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of that to understand say the bounds of

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our life I mean so this gives us

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hopefully the possibility of going from

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physics to then a variety of

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physiological possibilities to

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understand the bounds of life or the you

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know how far we might expect something

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to exist away from the types of

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organisms that we already see so with

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that I'd like to thank a long set of

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collaborators many of whom are here

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today I'd like to acknowledge NASA and

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NSF for generous funding throughout

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various portions of these papers

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and with that I'd be happy to take any

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questions if we have time and thank you

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for your attention so that yeah there's

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a microphone up front please use it for

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questions hello okay hi Micah Wong

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University of Washington I I'm not

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really in this field but it was a really

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interesting talk and I've read in the

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work of like Nick Lane and others that

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the sort of cell size maximum can be

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attributed to a sort of surface area to

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volume ratio kind of thing where

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chemiosmosis happens along the surface

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area of cells and so it's wondering if

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this ribosomal limit is complementary or

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contradictory to that argument and if

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you have thoughts on that yeah I mean so

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I won't

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detail because we have a lot to say

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about that but the the broad point I

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will make is that many of these sort of

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limits occur at you know at the same

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place for different perspectives so this

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this size based you know just this

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packing problem for the smallest cells

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that limit is almost identical to the

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energetic problem and that really tells

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you that two things should be being

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co-opted amaizing

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right so it says that you have a set of

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constraints and evolution pushes on the

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constraints at most matters until

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they've sort of all read some boundary

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that they can't go beyond and so there's

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there's some very complicated Co

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optimization we haven't worked out yet

388
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related to why some of many of these

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constraints occur at the same scales and

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I think that relates to some of Nick

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lanes arguments well thank you more time

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for one more question

393
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I am I'm really struggling to understand

394
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why you think cell volume is a relevant

395
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sort of independent variable to describe

396
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these two processes because I guess

397
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thinking from my point of view is that

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biochemist I mean most bacteria are

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about one femto leader and you know that

400
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basically comprises a huge spectrum of

401
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things like e.coli that basically can

402
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double every 20 minutes and have 50,000

403
00:15:26,370 --> 00:15:23,920
ribosomes to things like you know soil

404
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bacteria that are the same size and

405
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basically double once every month and

406
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have maybe a thousand ribosomes and are

407
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just as happy in terms of they can

408
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persist in their niche so I guess if you

409
00:15:39,230 --> 00:15:37,150
could have like things that have such a

410
00:15:41,220 --> 00:15:39,240
different metabolic sort of you know

411
00:15:43,230 --> 00:15:41,230
lifestyle very different number

412
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ribosomes and are the same volume I

413
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don't

414
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understand why is the volume so relevant

415
00:15:49,710 --> 00:15:47,740
for your study yeah it's a great

416
00:15:51,600 --> 00:15:49,720
question I mean it's saying at the top

417
00:15:53,340 --> 00:15:51,610
level volumes are relevant parameter

418
00:15:56,970 --> 00:15:53,350
mostly because it interacts with a

419
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variety of physical constraints so where

420
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your boundary is has dramatic

421
00:16:01,440 --> 00:16:00,370
implications at the you know for the

422
00:16:04,050 --> 00:16:01,450
simplest example for things like

423
00:16:05,460 --> 00:16:04,060
diffusion right and so size immediately

424
00:16:07,290 --> 00:16:05,470
tells you something about the physics

425
00:16:09,180 --> 00:16:07,300
and that's one of the reasons that we

426
00:16:11,070 --> 00:16:09,190
think about this boundary as it

427
00:16:13,770 --> 00:16:11,080
constrained now how much you want to

428
00:16:15,660 --> 00:16:13,780
change that concentration within that

429
00:16:17,070 --> 00:16:15,670
boundary is a more complicated question

430
00:16:19,440 --> 00:16:17,080
something I should mention here too is

431
00:16:21,690 --> 00:16:19,450
that this is all for a maximum growth

432
00:16:25,110 --> 00:16:21,700
rate perspective and under these maximum

433
00:16:27,510 --> 00:16:25,120
growth rate considerations you often see

434
00:16:29,700 --> 00:16:27,520
shifts and cell size when you move away

435
00:16:33,330 --> 00:16:29,710
from those conditions so I think I think

436
00:16:36,140 --> 00:16:33,340
there are in tremendous relationships

437
00:16:39,090 --> 00:16:36,150
across you could even do something like

438
00:16:40,410 --> 00:16:39,100
concentration of the ribosome is your as

439
00:16:41,640 --> 00:16:40,420
you're generating parameter and then you

440
00:16:43,530 --> 00:16:41,650
would see a strong correlation with cell

441
00:16:45,500 --> 00:16:43,540
volume but it would D correlate from

442
00:16:47,550 --> 00:16:45,510
lots of other things because it's not as

443
00:16:49,140 --> 00:16:47,560
directly related to certain physical

444
00:16:50,880 --> 00:16:49,150
constraints so that I think it's it's

445
00:16:55,410 --> 00:16:50,890
the connection with with obvious physics