Publications

@article{Libby1362757,
title = {Probabilistic Models for Predicting Mutational Routes to New Adaptive Phenotypes},
author = {Eric Libby and Peter A Lind},
doi = {10.21769/BioProtoc.3407},
year = {2019},
date = {2019-01-01},
journal = {Bio-protocol},
volume = {9},
number = {20},
institution = {IceLab},
abstract = {Understanding the translation of genetic variation to phenotypic variation is a fundamental problem in genetics and evolutionary biology. The introduction of new genetic variation through mutation can lead to new adaptive phenotypes, but the complexity of the genotype-to-phenotype map makes it challenging to predict the phenotypic effects of mutation. Metabolic models, in conjunction with flux balance analysis, have been used to predict evolutionary optimality. These methods however rely on large scale models of metabolism, describe a limited set of phenotypes, and assume that selection for growth rate is the prime evolutionary driver. Here we describe a method for computing the relative likelihood that mutational change will translate into a phenotypic change between two molecular pathways. The interactions of molecular components in the pathways are modeled with ordinary differential equations. Unknown parameters are offset by probability distributions that describe the concentrations of molecular components, the reaction rates for different molecular processes, and the effects of mutations. Finally, the likelihood that mutations in a pathway will yield phenotypic change is estimated with stochastic simulations. One advantage of this method is that only basic knowledge of the interaction network underlying a phenotype is required. However, it can also incorporate available information about concentrations and reaction rates as well as mutational biases and mutational robustness of molecular components. The method estimates the relative probabilities that different pathways produce phenotypic change, which can be combined with fitness models to predict evolutionary outcomes.},
keywords = {Adaptation, evolution, Evolutionary forecasting, Genotype-to-phenotype map, Mathematical modeling, Mutation},
pubstate = {published},
tppubtype = {article}
}

@article{Libby2019,
title = {Shortsighted Evolution Constrains the Efficacy of Long-Term Bet Hedging},
author = {Eric Libby and William C. Ratcliff},
url = {https://www.journals.uchicago.edu/doi/abs/10.1086/701786},
doi = {10.1086/701786},
journal = {The American Naturalist},
volume = {0},
abstract = {To survive unpredictable environmental change, many organisms adopt bet-hedging strategies that are initially costly but provide a long-term fitness benefit. The temporal extent of these deferred fitness benefits determines whether bet-hedging organisms can survive long enough to realize them. In this article, we examine a model of microbial bet hedging in which there are two paths to extinction: unpredictable environmental change and demographic stochasticity. In temporally correlated environments, these drivers of extinction select for different switching strategies. Rapid phenotype switching ensures survival in the face of unpredictable environmental change, while slower-switching organisms become extinct. However, when both switching strategies are present in the same population, then demographic stochasticity—enforced by a limited population size—leads to extinction of the faster-switching organism. As a result, we find a novel form of evolutionary suicide whereby selection in a fluctuating environment can favor bet-hedging strategies that ultimately increase the risk of extinction. Population structures with multiple subpopulations and dispersal can reduce the risk of extinction from unpredictable environmental change and shift the balance so as to facilitate the evolution of slower-switching organisms.},
keywords = {evolution, extinction, Libby},
pubstate = {forthcoming},
tppubtype = {article}
}