e – Science Institute Theme 14 Workshop: Microbiology Modelling and Evolution

In the last three days I have been running a focussed workshop at the e-Science Institute in Edinburgh on microbiology modelling and evolution. With 16 contributors roughly equally split between experimentalists and modelers, we presented and discussed scientific work at the interface between experimental and modelling approaches to evolution in microbiology.

At the start of the meeting, I set out my personal views on the challenges and opportunities at this interface – my hope was that at the end of the meeting my views would be in need of updating ! From a biological perspective, the challenges are around emergence of antimicrobial resistance and pathogenicity; the opportunities afforded by fast and cheap genome sequencing. From a modelling perspective, the challenges are around developing suitably realistic models, of generating experimentally testable hypotheses, and of gaining recognition of evolutionary computing as a means to test hypothesis; the opportunities are afforded by the capacity for long-term evolution by high performance computation.

Every talk was interesting and of a high standard – and the discussion was lively and engaging. A few points that came up that I found especially salient were: (i) microbes are not just interesting/important in themselves, but also as models for general biological processes, especially social processes, including host-pathogen interactions, evolution of cooperativity and evolution of communication. (ii) The challenge of genomic diversity is greater than I even imagined. I’m already used to genomic diversity in E. coli strains, but it was amazing to see it across so many bacteria studied, including Pseudomonas aeruginosa from a single patient’s lung, or Rhizobium from root nodules in clover and vetch in a single tiny patch. (iii) The importance of considering horizontal gene transfer, and thus different levels of selection, in models of mircobial evolution. (iv) The criticality of spatial organization in both real and simulated evolution, and thus the opportunity to use high performance computation for simulation of spatial scales as well as time scales.

I was heartened by the genuine sense of good will to work together at this interface, and a shared view that ‘Systems Biology’ needs to move beyond modelling how systems are now, into being able to explain why systems might have evolved to be the way they are, and predict how they might evolve.