Studying the evolution of byproduct cross-feeding in digital organisms
||Studying the evolution of byproduct cross-feeding in digital organisms|
||SNIC Medium Compute|
||György Barabas <firstname.lastname@example.org>|
||2020-05-28 – 2021-06-01|
||10611 10610 10203|
In metabolic networks, microbes often produce metabolites as a byproduct of consuming some resource. This can serve as the resource for another microbe, which then may produce yet another byproduct which serves as food for a third microbe - and so on. For example, the human gut microbiome is replete with organisms feeding off of the byproducts created by other microbes.
An important empirical observation is that, by and large, the maximum length of these metabolic chains is four. And yet, there do exist individual examples of metabolic chains 15 nodes long, even though they are highly atypical. Their very existence proves, however, that they are possible. The question is then: why do most metabolic networks stop at a meagre chain length of 4?
Here we intend to investigate this problem by letting artificial organisms evolve in a digital environment, to see if and when long metabolic chains can spontaneously evolve. We will use the digital evolution platform Avida. In Avida, self-replicating programs compete for various resources available inside a virtual computer environment. The system is such that whenever programs replicate themselves, there is a finite chance that a given machine instruction "mutates", becomes a randomly chosen different instruction instead. Thus, the three basic ingredients of evolution (variation, heritability, and differential fitness) are all in place, creating an open-ended evolution scenario whereby programs are free to evolve ingenious ways to replicate themselves.
We will initialize Avida with a basic resource that the organisms can consume. However, we set up the environment in a way that every time the resource is consumed, it creates a byproduct - which is also available for organisms, if they can evolve the ability to make use of them. Then, consumption of the byproduct creates yet another byproducts, and so on. As a first task, we will investigate whether in a well-mixed and undisturbed environment, long byproducy chains are able to evolve.
Next, we will see if similar or different results are obtained in disturbed and/or spatiall structured environments (both can be implemented in Avida). The main advantage of the digital organism approach is precisely that spatial structure and temporal disturbances can be adjusted and varied independently, which may not be possible in an experimental setting. Finally, we also want to see if the evolution of metabolic chains is sensitive to mutation rates. Briefly, very high mutataion rates may lead to unstable populations which, at the community level, cannot maintain very long chains. Once again, this would not be possible to study experimentally, as mutation rates are not experimentally adjustable parameters. They are, however easy to change inside a computer.
By synthesizing the results, we hope to be able to answer under what circumstances one expects long metabolic chains to evolve, and what are the factors preventing their evolution. The project will be run by me (PI György Barabás) and a Masters student (Johanna Orsholm), who will write a Masters Thesis work based on this research.