Paper: Understanding the limits to generalizability of experimental evolutionary models

Nature 455, 220-223 (11 September 2008) | doi:10.1038/nature07152; Received
31 January 2008; Accepted 5 June 2008


Understanding the limits to generalizability of experimental evolutionary
models
Samantha E. Forde 1,5, Robert E. Beardmore 2,5, Ivana Gudelj 2,3,5, Sinan S.
Arkin 2, John N. Thompson 1 & Laurence D. Hurst 4

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.

Abstract:
Given the difficulty of testing evolutionary and ecological theory in situ,
in vitro model systems are attractive alternatives; 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-evolution. 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.

Source: Nature
http://www.nature.com/nature/journal/v455/n7210/abs/nature07152.html

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