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yeah okay so i'm harrison and i'll be

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talking about the evolution about about

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communities I'm at Arizona State and

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working with a hundred Kim who's a

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postdoc in my lab Jason Raymond Sarah

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Walker two advisors of mine okay so just

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a quick outline I'm going to talk about

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some research questions i'm interested

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in the motivation behind my work some of

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the basics because i think some of the

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stuff i'll be talking about a lot of

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people won't be really familiar with and

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then i'll give you a little bit more

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detail approach of the work that i'm

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doing in terms of the network analysis

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on the yellowstone communities and

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computational simulations and some

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working conclusions ok so the research

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questions that i want you guys to think

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about and that i'm kind of focused on

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for this particular talk and in general

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overall with my work how is the

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functional and taxonomic diversity of

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microbial community reflected in the

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topological properties of the metabolic

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network are there any interesting

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patterns that's probably overwhelming

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and I haven't really defined a lot of

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these things like topological properties

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and I'll do that in a couple minutes

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keep that in mind a little bit but I'll

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kind of get more high-level here so what

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others is what changes do microbial

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communities undergo while they're

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evolving alongside a changing

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environment and kind of what I'm getting

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at here is trying to figure out how wife

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and its environment co-evolved I think

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that's really important question that a

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lot of people overlook oftentimes people

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talk about life as adapting to its

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environment but not necessarily is the

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environment adapting to life okay so why

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do we all care a lot of times with

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biology traditionally people are very

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reductionist and how they think about

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things and kind of this whole idea with

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you know like the Human Genome Project

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and trying to understand you know maybe

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if we understand exactly how the genome

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works and exactly what every single gene

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does we'll know exactly how biology does

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everything recently people have kind of

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pulled away from that and instead of

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going top down and they're trying to

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work from the bottom up where if you

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understand genes then you'll understand

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organisms you know understand societies

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it's kind of more of a complex problem

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and so it's more of a complex

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Systems problem where there's a lot of

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interesting interactions between life

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and the different aspects of life in

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terms of at the organismal level and at

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the enzymatic level and also between

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organisms and between organisms and

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their environments and some kind of the

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broader implications of work of trying

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to understand how life and its

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environment interact climate ologies a

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big example so how are humans affecting

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the climate how's the climate affecting

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humans has a lot of implications for a

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life going forward on earth biomedicine

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a big thing right now is understanding

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the human microbiome there's a lot of

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money being funneled into that and

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that's kind of like how does your gut

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microbiome reflect your health and how

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can we kind of utilize that in order to

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understand diagnose people and also

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understand how to treat them different

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diseases planetary science is probably

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most obvious one for this crowd and so

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what effect does emerging life have on

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its environment so that's kind of like

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bio signatures and also how does the

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environment affect what kind of life is

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as possible to evolve okay so the

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approach that I'm going to be looking at

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for looking at how the cove alleged

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coevolution of life in its environment I

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work from two aspects I do some

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computational modeling so I try to

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simulate microbial communities and

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evolution of microbial communities and

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I'm also doing some data analysis from

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actual real hard field data so we have

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some Yellowstone field data and I'll be

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talking to you a little bit about that

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and then kind of how these two things

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get tied together is this idea of

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network analysis so you can construct

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networks from the metabolisms of both

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the simulated work that I'm doing and

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also from the metabolisms that we infer

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in these communities that we go out and

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sample and you can represent them as

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networks and then you can measure these

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different properties and networks and

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use that to compare them and we hope to

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infer the evolutionary history of

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accident microbial communities through

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these models which is constrained by the

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field data okay so just really basics a

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lot of people probably know this but

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just for any people who are more

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ingrained in astronomy metabolism is

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just the chemical processes that occur

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that keep life living

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metabolic pathways just a sequence of

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those processes in metabolic network is

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just interconnected pathways so just on

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the right here I just this is a

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metabolic network example so this is

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amino acid a bunch of amino acid

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synthesis and particular with the stuff

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that I'm doing with the field work

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metabolic networks that microbial

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communities can be inferred from

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metagenomic data so if you sequence the

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genes then you can link those to a

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database and the database tells you what

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enzymes those genes code for and then

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there's another database that says what

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reactions are associated with those

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enzymes and so using all that data you

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can construct a big Network and infer

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the metabolism of a community ok so what

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our networks this is something that's

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probably need a lot of people so network

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when I say network I just mean nodes

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plus edges and to pot when I say to

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Paula gee I mean the physical properties

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of network so there are different things

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you can measure in a network such a

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shape connectivity or like degree

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distribution and that's example i'm

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giving you here so i'm left i have a

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random Network which is just the US

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highway map and on the right I have a

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scale-free Network which is the u.s.

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like airport system and the degree

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distribution of these nodes means how

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many nodes are connected how many other

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nodes so for example there's a small

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number of nodes that are that have a few

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links most nodes in this network have

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you know more than one but less than

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like 10 and then there's a few nodes

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which have way more than 10 so what you

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think about is like most cities are

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connected to only a few other cities and

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that's pretty consistent with the

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highway system in America which is

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unlike the airport system where you have

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big hubs where you have a few cities

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which are really highly connected so you

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have a lot of you have a lot of note or

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a lot of nodes with only a few links but

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you only have a few nodes with a lot of

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links and that's what this graph is

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showing so it follows a power law

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distribution and scale-free networks are

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really interesting because people use

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them to uncover scale-free networks are

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indicative of

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ecology and a lot of instances so that's

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something which we looked at in our

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research okay so Yellowstone we have

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some collaborators they got data from

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Yellowstone and there's these 26

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different sampled metagenomes that we

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have and like I saying earlier from the

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metagenomes we can infer the metabolic

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networks that are within these

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communities so you link them to the

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genes that you find in the metagenomes

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that you like the jeans the enzymes the

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enzymes the reactions and when I talk

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about metabolic network I'm just saying

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what is the metabolism facilitated by

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all the enzymes in this particular

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community so it's all the possible

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reactions as facilitated by enzymes okay

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so we can do this for phototrophic

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communities in kemah trophic communities

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and it ends up that you have different

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looking networks and these networks

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actually have different properties so

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just based on the properties in network

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you can figure out if the community is

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phototrophic or kim atrophic and just

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here's a visual example you wouldn't be

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will tell just from this image but you

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can look at different parameters of each

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of these networks and figure that out in

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the middle you have all communities put

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together plus all possible reactions

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okay so here's an example of two

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particular communities that of those 26

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that I was talking about and here's the

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degree distribution plots so these are

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the same plots that you saw under the

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random Network image and also under the

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scale-free Network image with the

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highway system and the airport system

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and if you notice these look a lot more

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like the airport systems these actually

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are scale-free networks so they follow

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up power law distribution and it's

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really interesting because like i was

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saying there's a lot of biological

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networks that follow the scale free

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distribution so maybe it's not too

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surprising that when you look at the

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metabolism of a whole community of

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organisms that it also follows a

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scale-free distribution but this

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actually isn't something that people

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have looked at before people looked at

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the distribution within particular

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organisms so within a single organism

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does F a scale-free distribution it

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turns out it does there's no reason that

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if you put a lot of them together that

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your distribution would still be

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scale-free so this is actually kind of

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an interesting result um another thing

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that we did is here's the 26 sampled

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metagenomes

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and we plotted a number of enzyme

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commission numbers which this is the

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number of basically unique enzymes or

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unique reactions coded by organisms in

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these communities on the y-axis and on I

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x-axis we did the number of taxonomic

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families so it's like number of species

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basically and the real metagenomes have

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a lot less number of enzymes for the

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number of families in a particular

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community and if we sample artificially

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which is the these blue dots random

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families and just put them together with

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this if you put the same number families

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together randomly assembled from these

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different communities then you end up

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with a lot more different enzymes that

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are being coded for which isn't that

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surprising basically this just means

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that reactions are shared across

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communities so these communities are

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more optimized for their environment and

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for the other organisms around them

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whereas these ones they use symbol

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artificially or not and that's pretty

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much what you'd expect because you

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wouldn't want to waste resources doing

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reactions that someone else in your

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communities already doing okay now I'm

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kind of going to jump into the other

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aspect of what i was talking about which

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is that I do this computational modeling

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stuff and so in these models that I make

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I simulate the evolution of organisms

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and the organisms are defined by the

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enzymes that they contain so on this

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picture on the right here I just show

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you each of these little circles

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represents an organism and then they're

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divided into different little

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communities and they catalyze reactions

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based on their propensity and propensity

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is just a fancy way of saying well if

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you have a higher concentration of

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different substrates that's more likely

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directions going to be catalyzed

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especially if the reaction rate

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constants higher okay so this is some

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preliminary results for my model which

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basically just shows that it's behaving

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as it should be so look at this bottom

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plot first and this is just showing you

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species count over time so each of these

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different lines is a different species

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as defined by the enzymes it contains

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and you kind of see here that after some

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system time that a few species become

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more fit than

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rest of the things in the system but

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then they'd eventually die out just

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because the system is closed and there's

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no energy input to you burning all your

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energy that you have and then this top

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plot is just showing you the fraction

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the total organismal energy contained

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within a particular species so the total

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organismal energy combined all hundred

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species at the beginning is going to be

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one it's going to be one at the end

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because that doesn't change but you can

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just see the fraction of energy in these

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two species that dominate ends up

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increasing as you'd expect so this isn't

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supposed to be anything mind-blowing but

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I'm just showing you that I'm starting

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to replicate how microbial systems can

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evolve on my computer and that we can

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use this eventually to make networks to

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compare to those same networks I was

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showing you earlier okay so just going

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through few working conclusions these

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are bullet points I showed you earlier

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but Camino metabolic networks inferred

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from metagenomic data is indicated that

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they have a scale-free degree

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distribution just like organismal

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metabolic networks Rio metagenomes have

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much less Cadillac diversity than

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artificial metagenomes suggesting the

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reactions are shared across communities

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and that I've constructed this model and

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then it were working on it so that we

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can compare it to the empirical data

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that we have so thank you questions so I

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thought that plot that you have is

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really interesting that showing that

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these reactions are shared across

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communities because I think lots of

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times when you think about the origin of

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life we talk about RNA worlds or

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whatever and we sort of assumed that

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some geochemistry would be providing

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some functionality for primitive life

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but I think it's really cool to see that

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you know you don't even need to assume

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that primitive life was doing that

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modern life is very clearly doing that

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and it's really observable so do you

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have any insight about what kinds of

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functions might be more easily shared or

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do you have any way to get at that data

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we do have ways to get that data and we

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do have that data we just haven't

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investigated it this is kind of like the

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first level thing that we've done so

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that's probably the next goal is we want

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to investigate

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exactly what reactions tend to be shared

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have my own mic so if you don't have a

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closed system how do you how would you

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that's a good question and it's really

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hard to predict just gonna top my head

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but it'd be interesting because you

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wouldn't I mean like you'd be able to

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see things evolve more so in this system

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obviously whatever we put in the initial

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conditions if it's close it's going to

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live for a while and it's going to die

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but it's not really going to change and

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so if you had some open conditions r you

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I had energy flowing or you had a flow

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of metabolites in then you can probably

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see somewhere interesting dynamics

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that's a very vague answer but that's