title: Monotonicity of fitness landscapes and mutation rate control.
abstract: A common view in evolutionary biology is that mutation rates are minimised. However, studies in combinatorial optimisation and search have shown a clear advantage of using variable mutation rates as a control parameter to optimise the performance of evolutionary algorithms. Much biological theory in this area is based on Ronald Fisher's work, who used Euclidean geometry to study the relation between mutation size and expected fitness of the offspring in infinite phenotypic spaces. Here we reconsider this theory based on the alternative geometry of discrete and finite spaces of DNA sequences. First, we consider the geometric case of fitness being isomorphic to distance from an optimum, and show how problems of optimal mutation rate control can be solved exactly or approximately depending on additional constraints of the problem. Then we consider the general case of fitness communicating only partial information about the distance. We define weak monotonicity of fitness landscapes and prove that this property holds in all landscapes that are continuous and open at the optimum. This theoretical result motivates our hypothesis that optimal mutation rate functions in such landscapes will increase when fitness decreases in some neighbourhood of an optimum, resembling the control functions derived in the geometric case. We test this hypothesis experimentally by analysing approximately optimal mutation rate control functions in 115 complete landscapes of binding scores between DNA sequences and transcription factors. Our findings support the hypothesis and find that the increase of mutation rate is more rapid in landscapes that are less monotonic (more rugged). We discuss the relevance of these findings to living organisms.
authors.affiliation: School of Science and Technology, Middlesex University, London, NW4 4BT, UK.
authors.forename: Roman V
authors.initials: RV
authors.lastname: Belavkin
authors.affiliation: School of Computing and Mathematics, Keele University, Keele, ST5 5BG, UK. a.d.channon@keele.ac.uk.
authors.forename: Alastair
authors.initials: A
authors.lastname: Channon
authors.affiliation: School of Computing and Mathematics, Keele University, Keele, ST5 5BG, UK.
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Aston
authors.affiliation: Statslab, University of Cambridge, Cambridge, CB3 0WB, UK.
authors.forename: John
authors.initials: J
authors.lastname: Aston
authors.affiliation: Faculty of Life Sciences, University of Manchester, Manchester, M13 9PT, UK.
authors.forename: Rok
authors.initials: R
authors.lastname: Krašovec
authors.affiliation: Faculty of Life Sciences, University of Manchester, Manchester, M13 9PT, UK.
authors.forename: Christopher G
authors.initials: CG
authors.lastname: Knight
mesh_terms: D001483:Base Sequence; D005075:Biological Evolution; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D059645:Mutation Rate; D012641:Selection, Genetic
keywords: Adaptation; Fitness landscape; Mutation rate; Population genetics