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Probabilistic Models for Predicting Mutational Routes to New Adaptive Phenotypes
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics. IceLab.ORCID iD: 0000-0002-6569-5793
Umeå University, Faculty of Science and Technology, Department of Molecular Biology (Faculty of Science and Technology).ORCID iD: 0000-0003-1510-8324
2019 (English)In: Bio-protocol, ISSN 2331-8325, Vol. 9, no 20, article id 3407Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2019. Vol. 9, no 20, article id 3407
Keywords [en]
Evolutionary forecasting, Mathematical modeling, Adaptation, Mutation, Evolution, Genotype-to-phenotype map
National Category
Evolutionary Biology Mathematics
Research subject
evolutionary genetics; Mathematics
Identifiers
URN: urn:nbn:se:umu:diva-164303DOI: 10.21769/BioProtoc.3407ISI: 000492148000015OAI: oai:DiVA.org:umu-164303DiVA, id: diva2:1362757
Available from: 2019-10-21 Created: 2019-10-21 Last updated: 2019-11-20Bibliographically approved

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Libby, EricLind, Peter A.
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