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      Evolutionary Accessibility of Mutational Pathways

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          Abstract

          Functional effects of different mutations are known to combine to the total effect in highly nontrivial ways. For the trait under evolutionary selection (‘fitness’), measured values over all possible combinations of a set of mutations yield a fitness landscape that determines which mutational states can be reached from a given initial genotype. Understanding the accessibility properties of fitness landscapes is conceptually important in answering questions about the predictability and repeatability of evolutionary adaptation. Here we theoretically investigate accessibility of the globally optimal state on a wide variety of model landscapes, including landscapes with tunable ruggedness as well as neutral ‘holey’ landscapes. We define a mutational pathway to be accessible if it contains the minimal number of mutations required to reach the target genotype, and if fitness increases in each mutational step. Under this definition accessibility is high, in the sense that at least one accessible pathway exists with a substantial probability that approaches unity as the dimensionality of the fitness landscape (set by the number of mutational loci) becomes large. At the same time the number of alternative accessible pathways grows without bounds. We test the model predictions against an empirical 8-locus fitness landscape obtained for the filamentous fungus Aspergillus niger. By analyzing subgraphs of the full landscape containing different subsets of mutations, we are able to probe the mutational distance scale in the empirical data. The predicted effect of high accessibility is supported by the empirical data and is very robust, which we argue reflects the generic topology of sequence spaces. Together with the restrictive assumptions that lie in our definition of accessibility, this implies that the globally optimal configuration should be accessible to genome wide evolution, but the repeatability of evolutionary trajectories is limited owing to the presence of a large number of alternative mutational pathways.

          Author Summary

          Fitness landscapes describe the fitness of related genotypes in a given environment, and can be used to identify which mutational steps lead towards higher fitness under particular evolutionary scenarios. The structure of a fitness landscape results from the way mutations interact in determining fitness, and can be smooth when mutations have multiplicative effect or rugged when interactions are strong and of opposite sign. Little is known about the structure of real fitness landscapes. Here, we study the evolutionary accessibility of fitness landscapes by using various landscape models with tunable ruggedness, and compare the results with an empirical fitness landscape involving eight marker mutations in the fungus Aspergillus niger. We ask how many mutational pathways from a low-fitness to the globally optimal genotype are accessible by natural selection in the sense that each step increases fitness. We find that for all landscapes with lower than maximal ruggedness the number of accessible pathways increases with increases of the number of loci involved, despite decreases in the accessibility for each pathway individually. We also find that models with intermediate ruggedness describe the A. niger data best.

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          Most cited references53

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          The genetic landscape of a cell.

          A genome-scale genetic interaction map was constructed by examining 5.4 million gene-gene pairs for synthetic genetic interactions, generating quantitative genetic interaction profiles for approximately 75% of all genes in the budding yeast, Saccharomyces cerevisiae. A network based on genetic interaction profiles reveals a functional map of the cell in which genes of similar biological processes cluster together in coherent subsets, and highly correlated profiles delineate specific pathways to define gene function. The global network identifies functional cross-connections between all bioprocesses, mapping a cellular wiring diagram of pleiotropy. Genetic interaction degree correlated with a number of different gene attributes, which may be informative about genetic network hubs in other organisms. We also demonstrate that extensive and unbiased mapping of the genetic landscape provides a key for interpretation of chemical-genetic interactions and drug target identification.
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            • Record: found
            • Abstract: found
            • Article: not found

            Epistasis--the essential role of gene interactions in the structure and evolution of genetic systems.

            Epistasis, or interactions between genes, has long been recognized as fundamentally important to understanding the structure and function of genetic pathways and the evolutionary dynamics of complex genetic systems. With the advent of high-throughput functional genomics and the emergence of systems approaches to biology, as well as a new-found ability to pursue the genetic basis of evolution down to specific molecular changes, there is a renewed appreciation both for the importance of studying gene interactions and for addressing these questions in a unified, quantitative manner.
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              The neutral theory of molecular evolution.

              M Kimura (1979)
                Bookmark

                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, USA )
                1553-734X
                1553-7358
                August 2011
                August 2011
                18 August 2011
                : 7
                : 8
                : e1002134
                Affiliations
                [1 ]Institute of Theoretical Physics, University of Cologne, Köln, Germany
                [2 ]Laboratory of Genetics, Wageningen University, Wageningen, Netherlands
                University of Texas at Austin, United States of America
                Author notes

                Conceived and designed the experiments: JAGMdV. Performed the experiments: JAGMdV. Analyzed the data: JF AK JAGMdV JK. Contributed reagents/materials/analysis tools: JF AK. Wrote the paper: JF JAGMdV JK. Carried out numerical simulations: JF AK JK.

                Article
                PCOMPBIOL-D-11-00262
                10.1371/journal.pcbi.1002134
                3158036
                21876664
                286ab8d6-d67d-4b7f-8f16-3b1b8baa6ab8
                Franke et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 24 February 2011
                : 9 June 2011
                Page count
                Pages: 9
                Categories
                Research Article
                Biology
                Computational Biology
                Evolutionary Modeling
                Evolutionary Biology
                Evolutionary Processes
                Adaptation
                Mutation
                Evolutionary Theory
                Genetics
                Heredity
                Epistasis
                Microbiology
                Mycology
                Fungi
                Population Biology
                Population Genetics

                Quantitative & Systems biology
                Quantitative & Systems biology

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