Theoretical Evolutionary Genetics · University of New Mexico
Center for Evolutionary and Theoretical Immunology
I develop mathematical approaches to evolutionary dynamics, with current emphasis on the evolution of sex, horizontal gene transfer, and prediction in evolving populations.
Featured project
The central claim is that sex and recombination need not be explained as exceptional biological add-ons. Natural selection itself can generate the negative associations that make genetic mixing advantageous, and at large scales the resulting selective response is dominated by rare, near-critical trajectories.
This program connects covariance, heavy-tailed sex potential, path ensembles, and nonequilibrium statistical mechanics. It also asks why non-additive fitness becomes progressively less consequential under coarse-graining, making universal structure visible above the microscopic details.
Current questions
Sex and recombination are usually treated as special biological mechanisms in need of special explanations. I am studying whether their selective advantage instead emerges as a generic, scale-dependent consequence of natural selection itself.
Current themes: covariance, path ensembles, critical trajectories, heavy-tailed sex potential, non-equilibrium statistical mechanics, and the coarse-graining of non-additive fitness.
Antibiotics enrich resistance plasmids, but do not by themselves create direct selection for horizontal transfer. Under vertical transmission, linkage between host and plasmid fitness together with ongoing adaptation can generate antagonistic host–plasmid associations and hence selection for conjugation and other forms of HGT.
Current themes: antibiotic-resistance plasmids, host–plasmid covariance, HGT pressure, multiscale transmission, and preemptive risk forecasting.
Evolution is contingent at the microscopic level, but some coarse-grained observables are robust. The goal is to identify quantities that can be updated in real time and used for forward prediction without reconstructing the full genotype-to-fitness map.
Current themes: distributions of fitness effects, Bayesian updating, extreme-value structure, real-time forecasting, and prediction from low-dimensional summaries.
Working principle
Evolutionary detail is high-dimensional and contingent. Prediction becomes more plausible when we ask which coarse-grained quantities are stable to those details.
Selected work
A representative short list.
Philip J. Gerrish, Ramiro Dominguez Aguilar & Alexandre Colato. In Handbook of Visual, Experimental and Computational Mathematics, pp. 109–134, Springer.
publicationA current line of work developing the claim that natural selection itself generates the associations that favor recombination.
N. S. C. Merleau, S. Pénisson, P. J. Gerrish, S. F. Elena & M. Smerlak. PLoS Computational Biology 17:e1009128.
publicationS. Pénisson, T. Singh, P. D. Sniegowski & P. J. Gerrish. Genetics 205:1305–1318.
publicationPhilip J. Gerrish & Paul D. Sniegowski. Journal of the Royal Society Interface.
publicationEarly experimental and theoretical work showing how competition among beneficial mutations constrains adaptation in microbes and RNA viruses.
Foundational work on the evolution of high mutation rates and the fate of beneficial mutations in asexual populations.
About
I work at the interface of population genetics, probability, and statistical mechanics. Much of my research asks whether apparently idiosyncratic evolutionary processes contain coarse-grained regularities that can be measured, explained, and eventually used for prediction.
My work has included mutation-rate evolution, clonal interference, genetic linkage, distributions of fitness effects, real-time evolutionary forecasting, recombination, horizontal gene transfer, and microbial evolution.
I received my PhD in Zoology from Michigan State University, where I worked with Richard Lenski, after earlier training in biosystems engineering, physics, and philosophy.
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