1. Department of Ecology and Evolutionary Biology, University of California, Santa Cruz, California 95064, USA
2. Department of Mathematics, Imperial College London, London SW7 2AZ, UK
3. Department of Mathematical Sciences and,
4. Department of Biology & Biochemistry, University of Bath, Bath BA2 7AY, UK
5. These authors contributed equally to this work.

Correspondence to: Laurence D. Hurst⁴ Correspondence and requests for materials should be addressed to L.D.H. (Email: l.d.hurst@bath.ac.uk)

Abstract

Given the difficulty of testing evolutionary and ecological theory in situ, in vitro model systems are attractive alternatives¹; however, can we appraise whether an experimental result is particular to the in vitro model, and, if so, characterize the systems likely to behave differently and understand why? Here we examine these issues using the relationship between phenotypic diversity and resource input in the T7–Escherichia coli co-evolving system as a case history. We establish a mathematical model of this interaction, framed as one instance of a super-class of host–parasite co-evolutionary models, and show that it captures experimental results. By tuning this model, we then ask how diversity as a function of resource input could behave for alternative co-evolving partners (for example, E. coli with lambda bacteriophages). In contrast to populations lacking bacteriophages, variation in diversity with differences in resources is always found for co-evolving populations, supporting the geographic mosaic theory of co-evolution². The form of this variation is not, however, universal. Details of infectivity are pivotal: in T7–E. coli with a modified gene-for-gene interaction, diversity is low at high resource input, whereas, for matching-allele interactions, maximal diversity is found at high resource input. A combination of in vitro systems and appropriately configured mathematical models is an effective means to isolate results particular to the in vitro system, to characterize systems likely to behave differently and to understand the biology underpinning those alternatives.

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