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New model provides insights into microbial evolution

Model of microbial evolution

Researchers at SynthSys have developed a novel modelling approach to predict how microbial communities might adapt in response to their changing environment. Such insights could be valuable in understanding how microbial communities emerge, establish, and diversify.

Communities of microbes grow on mixtures of metabolic resources, and there is often intense competition for each type of nutrient. Microbes in natural communities, for example in the gastrointestinal tract, do not try to consume all the available substrates but, instead, each species specializes to a few substrates.

This metabolic specialization is driven by cellular trade-offs, where importing and metabolizing one resource reduces a microbe's ability to import and metabolize another. Random genetic mutations can affect how these trade-offs are balanced, and communities evolve with a complex interdependence on their ecology. Consequently, we do not understand how, and under what conditions, this evolutionary process leads to communities that are stable and long-lived.

Dr Christos Josephides and Prof Peter Swain have developed a mathematical model that integrates aspects of intracellular constraints, ecological dynamics, and random mutational processes to describe the evolution of microbial communities. Using the model, the researchers could re-construct all evolutionary histories that lead to the same stable community and discovered that properties of these evolutionary trajectories can be used to predict the type of stable community that ultimately forms. They can therefore forecast whether a community will, for example, eventually collapse or diversify.

 

Published online: Josephides, C. and Swain, P. Predicting metabolic adaptation from networks of mutational paths. Nature Communications (2017) Vol 8 (1) 685

DOI: 10.1038/s41467-017- 00828-6

Image: An example of a network of evolutionary trajectories, which visualizes the evolutionary dynamics of a microbial community. Nodes represent different community states and are coloured according to how that community exploits the environment; arrows represent eco-evolutionary transitions, where a mutant population arises and out-competes a resident population. Squares show stable communities, resistant to mutation; circles show transitory ones. Two sample trajectories are indicated in red and blue.