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Microbial Evolution

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Microbial Evolution: Modeling and Experimental Techniques

We will use this space for shared development of the write-up of ideas from the workshop.

Present at Workshop

Rosalind Allen (Edinburgh) Chris Bayliss (Leicester) Sam Brown (Oxford) Joanna Bryson (Bath) Dominique Chu (Kent) Steve Diggle (Nottingham) Chrisantha Fernando (Sussex) Richard Goldstein (NIMR) Simon Hickinbotham (York) Jon Hobman (Nottingham) Laurence Loewe (Wisconsin) Pete Lund (Birmingham) Gail Preston (Oxford) Jon Rowe (Birmingham) Daniel Rozen (Manchester) Dov Stekel (Nottingham) Peter Young (York)

Brief summary from eSI discussion

Big picture document of challenges at start of decade:

Moving Systems Biology on to include evolution. Things as they are to how they might be / why they have evolved. Moving from individuals to populations (many examples and challenges). Understanding function from evolutionary perspective. Next stage of testing adequacy of systems biology models: predictivity in an evolutionary context. Interfacing between different microbiology and computational communities. Timeliness because of current computational and experimental techniques and return on investment on systems biology. Why microbes: interesting in themselves because of antibiotic resistance, pathogenicity, industrial uses (synthetic biology). Also as models for all biology, social interactions etc. Why funded and how funded: importance of supporting two-postdoc grants against double-attack; funding of parameter measurements and careers for such individuals. Examples to illustrate these positions.

Text from mid-term report to eSI

Microbial evolution is rapid and important. It drives the emergence of antimicrobial-resistant bacteria (such as MRSA), new and emerging pathogens (such as enteroaggerative E. coli) and can lead to failure in industrial fermentation using of bacteria. Moreover, microbes can be used as tractable experimental models for exploring more general questions in evolution. Bacteria have social existences, and encounter social dilemmas seen in higher organisms, such as evolution of cooperation, competition, cheating and public-goods dilemmas.

From an experimental perspective, next generation DNA sequencing technologies provide an unprecedented opportunity to study evolution in microbes. These have uncovered remarkable diversity in microbial species. For example, there are now nearly sequenced 100 E. coli strains, including many human pathogens. Each strain typically has between 4000-5000 genes, consisting of a ‘core’ genome of about 2000 genes, and a ‘peripheral’ genome drawn from more than 15,000 genes found in all the strains. Thus the genetic diversity between E. coli strains is greater than the difference between humans and slime moulds. Similar diversity is found in other bacteria, such as Pseudomonas aeruginosa (an important pathogen in cystic fibrosis patients) and Rhizobium leguminosarum (responsible for nitrogen fixation).

The evolution and diversity of microbes is often driven by horizontal gene transfer, via mobile DNA elements, including plasmids, transposons and phage virus. These elements will frequently include the transfer of resistance or pathogenicity genes. Microbial evolution can also be driven by hypermutation, allowing rapid switching between different genotypes.

From a computational perspective, the field of ‘Systems Biology’ has been successful in developing models for how biological systems, such as bacteria, work as they are. These models are predictive in that they are capable of predicting an outcome on the basis of a different input stimulus. However, a truly predictive biology would also be able to make predictions about how the system might evolve when placed in a novel environment.

The field of ‘Computational Evolution’ allows researchers to simulate the processes of diversity, heredity and selection in computer programs, Thus it is possible to explore how a computer algorithm adapts, in an evolutionary sense, to an environmental challenge posed by the programmer. Thus there is an important research opportunity in applying computational evolution techniques within a systems biology context to address important problems in microbiology.

A number of factors are required to be successful. The first is the available of high performance and distributed computing. Here again, there are opportunities afforded by recent advances in clustering, multi-core processors and GPUs, enabling unprecedented computing power. More significant is the challenge of generating models with sufficient detail to be biologically relevant but with sufficient abstraction to be computationally tractable. This can be met in part by the development of appropriate computational paradigms and abstractions.

In summary, microbial evolution is a major challenge for human health and well-being, as well as being of interest for studying evolution in general. The experimental and computational opportunities afforded by next generation sequencing and modern high performance computation provide a timely opportunity for an evolutionary predictive computational systems biology applied to microorganisms.

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