Student projects

OEB100, 2013

Summary

Our goal this year will be to better understand how a metabolic network responds to evolutionary pressure. The parts that make up a metabolic network are highly interdependent. The activity of an enzyme depends on the concentrations of its substrates and products, which in turn depend on the activities of the other enzymes in the metabolic network. The cell adds another level of complexity in that enzymes must share a fixed volume, collection of ribosomes, supply of ATP, NADH, etc. Mutations that change any component will necessarily affect the entire system in ways that are likely to be detrimental. Our aim will be to explore what effects these inter-dependencies can have on the course of evolution:

Given the unavoidably tangled interactions between the targets of mutations, how does a metabolic network ever manage to evolve? Do populations accumulate small mutations slowly, always adjusting for their side-effects, or can they find paths around these negative interactions? If we can predict these interactions (and we think we can) can we also predict how these populations will evolve?

Throughout, our approach to these questions will be both quantitative and experimental. Statistical computations and non-linear dynamics are just as important to understanding complex adaptive systems as are experimental manipulations and measurement.

Background

Classical evolutionary theory focuses on the fate of mutations whose contribution to fitness is predetermined. Yet the reasons behind why a mutation is adaptive, and how a series of these mutations leads to adaptation are extremely important. And in order to understand these why’s and how’s, we need to treat organisms as complex systems. Mutations cannot be dealt with simply by their effects on fitness, we also need to understand epistasis: how mutations change the system, and thereby change the effects of all other potential mutations.

This semester we will work with a mathematical model of the inter-dependencies between several kinds mutations, then test its predictions by

  1. evolving populations of our model organism in the lab,
  2. and manipulating the physiology of our model organism to simulate the effects of possible mutations.

In the laboratory

We will focus our attention on the the bacterium Methylobacterium extorquens. This bug is famous for its lovely pink color and for having a set of enzymes (figure 1) that allow it to get all of its carbon and energy from methanol molecules! This is a rare ability, and seems to show up in unexpected leaves of the tree of life. M. extorquens is our model for understanding how methanol metabolism works and how it evolves.

inkscape_map.png

The metabolic network responsible for carbon and energy metabolism in M. extorquens.

This network is small because we are treating most of the cell as one reaction, “Everything else”. We focus on the enzymes that handle methanol and its immediate derivatives because this is where the selective pressure is greatest. (In fact, we’ll be working with a mutant that the professor made which should further focus natural selection on these enzymes. Instead of its native enzymes, the mutant has 3 enzymes from a completely different bacterium which happen to do the same thing.) This huge reduction of complexity does two things: it reduces the number of targets for genetic screening and manipulations, and it lets us capture the system in a tractable mathematical model.

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An adaptive landscape for a two enzyme system in E. coli [Dykhuizen1987]. The measured points seemed to fit the landscape pretty well.

[Dykhuizen1987]Metabolic flux and fitness. DE Dykhuizen, AM Dean, DL Hartl. Genetics. 1987 January; 115(1): 25–31.