A Concise User's Guide to ISAAC_FL: ISAAC's Mission-Fitness Landscape Mapper Program

AbstractMultidimensional fitness landscapes provide insights into the molecular basis of laboratory and natural evolution. To date, such efforts usually focus on limited protein families and a single enzyme trait, with little concern about the relationship between protein epistasis and conformational dynamics. Here, we report a multiparametric fitness landscape for a cytochrome P450 monooxygenase that was engineered for the regio- and stereoselective hydroxylation of a steroid. We develop a computational program to automatically quantify non-additive effects among all possible mutational pathways, finding pervasive cooperative signs and magnitude epistasis on multiple catalytic traits. By using quantum mechanics and molecular dynamics simulations, we show that these effects are modulated by long-range interactions in loops, helices and β-strands that gate the substrate access channel allowing for optimal catalysis. Our work highlights the importance of conformational dynamics on epistasis in an enzyme involved in secondary metabolism and offers insights for engineering P450s.

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A Comprehensive, High-Resolution Map of a Gene’s Fitness Landscape

Molecular Biology and Evolution ◽

10.1093/molbev/msw021 ◽

2016 ◽

Vol 33 (5) ◽

pp. 1378-1378 ◽

Cited By ~ 6

Author(s):

Elad Firnberg ◽

Jason W. Labonte ◽

Jeffrey J. Gray ◽

Marc Ostermeier

Keyword(s):

High Resolution ◽

Fitness Landscape ◽

High Resolution Map ◽

Resolution Map

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A Model for Analysing the Collective Dynamic Behaviour and Characterising the Exploitation of Population-Based Algorithms

Evolutionary Computation ◽

10.1162/evco_a_00107 ◽

2014 ◽

Vol 22 (1) ◽

pp. 159-188 ◽

Cited By ~ 5

Author(s):

Mikdam Turkey ◽

Riccardo Poli

Keyword(s):

Population Dynamics ◽

Dynamic Behaviour ◽

Fitness Landscape ◽

Population Based ◽

Levels Of Abstraction ◽

Collective Dynamic ◽

Model Studies ◽

Proposed Model ◽

Wide Range ◽

Multiple Levels

Several previous studies have focused on modelling and analysing the collective dynamic behaviour of population-based algorithms. However, an empirical approach for identifying and characterising such a behaviour is surprisingly lacking. In this paper, we present a new model to capture this collective behaviour, and to extract and quantify features associated with it. The proposed model studies the topological distribution of an algorithm's activity from both a genotypic and a phenotypic perspective, and represents population dynamics using multiple levels of abstraction. The model can have different instantiations. Here it has been implemented using a modified version of self-organising maps. These are used to represent and track the population motion in the fitness landscape as the algorithm operates on solving a problem. Based on this model, we developed a set of features that characterise the population's collective dynamic behaviour. By analysing them and revealing their dependency on fitness distributions, we were then able to define an indicator of the exploitation behaviour of an algorithm. This is an entropy-based measure that assesses the dependency on fitness distributions of different features of population dynamics. To test the proposed measures, evolutionary algorithms with different crossover operators, selection pressure levels and population handling techniques have been examined, which lead populations to exhibit a wide range of exploitation-exploration behaviours.

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On a Vector Space Representation in Genetic Algorithms for Sensor Scheduling in Wireless Sensor Networks

Evolutionary Computation ◽

10.1162/evco_a_00112 ◽

2014 ◽

Vol 22 (3) ◽

pp. 361-403 ◽

Cited By ~ 5

Author(s):

F. V. C. Martins ◽

E. G. Carrano ◽

E. F. Wanner ◽

R. H. C. Takahashi ◽

G. R. Mateus ◽

...

Keyword(s):

Fitness Landscape ◽

Combinatorial Problem ◽

Wireless Sensor ◽

Space Representation ◽

Network Dynamic ◽

Decision Variable ◽

Exact Methods ◽

Variable Space ◽

Combinatorial Space ◽

Evolutionary Operators

Recent works raised the hypothesis that the assignment of a geometry to the decision variable space of a combinatorial problem could be useful both for providing meaningful descriptions of the fitness landscape and for supporting the systematic construction of evolutionary operators (the geometric operators) that make a consistent usage of the space geometric properties in the search for problem optima. This paper introduces some new geometric operators that constitute the realization of searches along the combinatorial space versions of the geometric entities descent directions and subspaces. The new geometric operators are stated in the specific context of the wireless sensor network dynamic coverage and connectivity problem (WSN-DCCP). A genetic algorithm (GA) is developed for the WSN-DCCP using the proposed operators, being compared with a formulation based on integer linear programming (ILP) which is solved with exact methods. That ILP formulation adopts a proxy objective function based on the minimization of energy consumption in the network, in order to approximate the objective of network lifetime maximization, and a greedy approach for dealing with the system's dynamics. To the authors’ knowledge, the proposed GA is the first algorithm to outperform the lifetime of networks as synthesized by the ILP formulation, also running in much smaller computational times for large instances.

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A New Method for Modeling the Behavior of Finite Population Evolutionary Algorithms

Evolutionary Computation ◽

10.1162/evco_a_00001 ◽

2010 ◽

Vol 18 (3) ◽

pp. 451-489 ◽

Cited By ~ 1

Author(s):

Tatsuya Motoki

Keyword(s):

Markov Chain ◽

Evolutionary Algorithms ◽

Finite Population ◽

Fitness Landscape ◽

Markov Chain Model ◽

Search Space ◽

Chain Model ◽

Transition Matrices ◽

Memory Space ◽

Exact Model

As practitioners we are interested in the likelihood of the population containing a copy of the optimum. The dynamic systems approach, however, does not help us to calculate that quantity. Markov chain analysis can be used in principle to calculate the quantity. However, since the associated transition matrices are enormous even for modest problems, it follows that in practice these calculations are usually computationally infeasible. Therefore, some improvements on this situation are desirable. In this paper, we present a method for modeling the behavior of finite population evolutionary algorithms (EAs), and show that if the population size is greater than 1 and much less than the cardinality of the search space, the resulting exact model requires considerably less memory space for theoretically running the stochastic search process of the original EA than the Nix and Vose-style Markov chain model. We also present some approximate models that use still less memory space than the exact model. Furthermore, based on our models, we examine the selection pressure by fitness-proportionate selection, and observe that on average over all population trajectories, there is no such strong bias toward selecting the higher fitness individuals as the fitness landscape suggests.

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