New Evolutionary Theory Via Simulation

2021 ◽  
pp. 75-96
Author(s):  
Gino Cattani ◽  
Mariano Mastrogiorgio
Keyword(s):  
Technological Innovation ◽  
Evolutionary Theory ◽  
Fitness Landscape ◽  
Simulation Modelling ◽  
Fitness Landscapes ◽  
Number Of Components ◽  
Nk Model ◽  
Technological Adaptation ◽  
Nk Landscapes ◽  
History Friendly Models

Simulation modelling is very common in evolutionary approaches to economics, strategy, and technological innovation. A well-established simulation framework is the NK model of fitness landscapes, which is particularly useful for modelling the processes of technological adaptation, whose difficulty is reflected into how a fitness landscape behaves as a function of the number of components and internal interdependencies of a technology. However, classical NK models become problematic when modelling different types of processes, such as technological exaptation, unless a broader family of NK models is considered. After reviewing the classical NK model, this chapter explores the potential of ‘generalized’ NK landscapes, followed by a review of other important simulation frameworks in evolutionary theory, such as holey landscapes, quantum-like approaches, and history-friendly models.

scholarly journals Lexical landscapes as largein silicodata for examining advanced properties of fitness landscapes

2019 ◽  
Author(s):  
Victor A. Meszaros ◽  
Miles D. Miller-Dickson ◽  
C. Brandon Ogbunugafor
Keyword(s):  
Evolutionary Theory ◽  
Empirical Data ◽  
In Silico ◽  
Evolutionary Biology ◽  
Fitness Landscape ◽  
Evolutionary Genetics ◽  
Higher Order ◽  
Fitness Landscapes ◽  
Adaptive Landscape ◽  
Data Sets

In silicoapproaches have served a central role in the development of evolutionary theory for generations. This especially applies to the concept of the fitness landscape, one of the most important abstractions in evolutionary genetics, and one which has benefited from the presence of large empirical data sets only in the last decade or so. In this study, we propose a method that allows us to generate enormous data sets that walk the line betweenin silicoand empirical: word usage frequencies as catalogued by the Google ngram corpora. These data can be codified or analogized in terms of a multidimensional empirical fitness landscape towards the examination of advanced concepts—adaptive landscape by environment interactions, clonal competition, higher-order epistasis and countless others. We argue that the greaterLexical Landscapesapproach can serve as a platform that offers an astronomical number of fitness landscapes for exploration (at least) or theoretical formalism (potentially) in evolutionary biology.

scholarly journals Analysis of statistical correlations between properties of adaptive walks in fitness landscapes

2020 ◽  
Vol 7 (1) ◽  
pp. 192118
Author(s):  
Sandro M. Reia ◽  
Paulo R. A. Campos
Keyword(s):  
Experimental Evolution ◽  
Fitness Landscape ◽  
Shortest Paths ◽  
Fitness Landscapes ◽  
The Other ◽  
Attraction Basin ◽  
Statistical Correlations ◽  
Adaptive Walks ◽  
Nk Landscapes ◽  
The Relationship

The fitness landscape metaphor has been central in our way of thinking about adaptation. In this scenario, adaptive walks are idealized dynamics that mimic the uphill movement of an evolving population towards a fitness peak of the landscape. Recent works in experimental evolution have demonstrated that the constraints imposed by epistasis are responsible for reducing the number of accessible mutational pathways towards fitness peaks. Here, we exhaustively analyse the statistical properties of adaptive walks for two empirical fitness landscapes and theoretical NK landscapes. Some general conclusions can be drawn from our simulation study. Regardless of the dynamics, we observe that the shortest paths are more regularly used. Although the accessibility of a given fitness peak is reasonably correlated to the number of monotonic pathways towards it, the two quantities are not exactly proportional. A negative correlation between predictability and mean path divergence is established, and so the decrease of the number of effective mutational pathways ensures the convergence of the attraction basin of fitness peaks. On the other hand, other features are not conserved among fitness landscapes, such as the relationship between accessibility and predictability.

scholarly journals Genetic Algorithms as a method to study adaptive walks in biological landscapes

2020 ◽  
Author(s):  
Edith Invernizzi ◽  
Graeme D Ruxton
Keyword(s):  
Genetic Algorithms ◽  
Evolutionary Biology ◽  
Fitness Landscape ◽  
Evolutionary Process ◽  
Simulation Modelling ◽  
Fitness Landscapes ◽  
Mathematical Methods ◽  
Reproductive Process ◽  
Modelling Framework ◽  
Phenotypic Fitness

AbstractThe metaphor of fitness landscapes is common in evolutionary biology, as a way to visualise the change in allele or phenotypic frequencies of a population under selection. Understanding how different factors in the evolutionary process affect the trajectory of the population across the landscape is of interest to both theoretical and empirical evolutionary biologists. However, fitness landscape studies often have to rely heavily on mathematical methods that are not easy to access by biologically trained researchers. Here, we used a method borrowed from engineering - genetic algorithms - to simulate the evolutionary process and study how different components affect the path taken through a phenotypic fitness landscape. In a simple study, we compare five selection models that reflect different degrees of dependency of fitness on trait quality: this includes strengths of selection, trait-quality dependent reproductive hierarchy and the amount of stochasticity in the reproductive process. We include an analysis of other evolutionary variables such as population size and mutation rate. We analyse a game theory problem, as a test landscape, that lends itself to analysis through a deterministic mathematical simulation, which we use for comparison. Our results show that there are differences in the speed with which different models of selection lead to the fitness optimum.Author summaryEvolution and adaptation in biology occurs in fitness landscapes, multidimensional spaces representing all possible genotypic or phenotypic combinations, where population adapt by following the cline of the fitness dimension. The study of adaptation on complex fitness landscapes has so far been limited by the need for mathematically heavy methods. Here, we present a simulation modelling framework, genetic algorithms, that can be used for evolutionary simulations of a population on a fitness landscape of chosen features and with custom evolutionary parameters.

scholarly journals On the (un-)predictability of a large intragenic fitness landscape

2016 ◽  
Author(s):  
Claudia Bank ◽  
Sebastian Matuszewski ◽  
Ryan T. Hietpas ◽  
Jeffrey D. Jensen
Keyword(s):  
Amino Acid ◽  
Fitness Landscape ◽  
Fitness Landscapes ◽  
Specific Information ◽  
Theoretical Approaches ◽  
Elevated Salinity ◽  
Theoretical Predictions ◽  
Experimental Approaches ◽  
Deterministic Processes ◽  
Negative Epistasis

AbstractThe study of fitness landscapes, which aims at mapping genotypes to fitness, is receiving ever-increasing attention. Novel experimental approaches combined with NGS methods enable accurate and extensive studies of the fitness effects of mutations – allowing us to test theoretical predictions and improve our understanding of the shape of the true underlying fitness landscape, and its implications for the predictability and repeatability of evolution.Here, we present a uniquely large multi-allelic fitness landscape comprised of 640 engineered mutants that represent all possible combinations of 13 amino-acid changing mutations at six sites in the heat-shock protein Hsp90 in Saccharomyces cerevisiae under elevated salinity. Despite a prevalent pattern of negative epistasis in the landscape, we find that the global fitness peak is reached via four positively epistatic mutations. Combining traditional and extending recently proposed theoretical and statistical approaches, we quantify features of the global multi-allelic fitness landscape. Using subsets of the data, we demonstrate that extrapolation beyond a known part of the landscape is difficult owing to both local ruggedness and amino-acid specific epistatic hotspots, and that inference is additionally confounded by the non-random choice of mutations for experimental fitness landscapes.Author SummaryThe study of fitness landscapes is fundamentally concerned with understanding the relative roles of stochastic and deterministic processes in adaptive evolution. Here, the authors present a uniquely large and complete multi-allelic intragenic fitness landscape of 640 systematically engineered mutations in yeast Hsp90. Using a combination of traditional and recently proposed theoretical approaches, they study the accessibility of the global fitness peak, and the potential for predictability of the fitness landscape topography. They report local ruggedness of the landscape and the existence of epistatic hotspot mutations, which together make extrapolation and hence predictability inherently difficult, if mutation-specific information is not considered.

scholarly journals The distribution of epistasis on simple fitness landscapes

2018 ◽  
Author(s):  
Christelle Fraïsse ◽  
John J. Welch
Keyword(s):  
Evolutionary Dynamics ◽  
Fitness Landscape ◽  
Null Model ◽  
Large Data ◽  
Fitness Landscapes ◽  
Mutational Bias ◽  
Data Set ◽  
Epistatic Interactions ◽  
Standing Variation ◽  
Mean And Variance

AbstractFitness interactions between mutations can influence a population’s evolution in many different ways. While epistatic effects are difficult to measure precisely, important information about the overall distribution is captured by the mean and variance of log fitnesses for individuals carrying different numbers of mutations. We derive predictions for these quantities from simple fitness landscapes, based on models of optimizing selection on quantitative traits. We also explore extensions to the models, including modular pleiotropy, variable effects sizes, mutational bias, and maladaptation of the wild-type. We illustrate our approach by reanalysing a large data set of mutant effects in a yeast snoRNA. Though characterized by some strong epistatic interactions, these data give a good overall fit to the non-epistatic null model, suggesting that epistasis might have little effect on the evolutionary dynamics in this system. We also show how the amount of epistasis depends on both the underlying fitness landscape, and the distribution of mutations, and so it is expected to vary in consistent ways between new mutations, standing variation, and fixed mutations.

scholarly journals Biophysical principles predict fitness landscapes of drug resistance

2016 ◽  
Vol 113 (11) ◽  
pp. E1470-E1478 ◽  
Author(s):  
João V. Rodrigues ◽  
Shimon Bershtein ◽  
Anna Li ◽  
Elena R. Lozovsky ◽  
Daniel L. Hartl ◽  
...  
Keyword(s):  
Drug Resistance ◽  
Protein Quality ◽  
Fitness Landscape ◽  
Catalytic Efficiency ◽  
Fitness Landscapes ◽  
Resistance Mutations ◽  
Chlamydia Muridarum ◽  
Complete Set ◽  
Essential Enzyme

Fitness landscapes of drug resistance constitute powerful tools to elucidate mutational pathways of antibiotic escape. Here, we developed a predictive biophysics-based fitness landscape of trimethoprim (TMP) resistance for Escherichia coli dihydrofolate reductase (DHFR). We investigated the activity, binding, folding stability, and intracellular abundance for a complete set of combinatorial DHFR mutants made out of three key resistance mutations and extended this analysis to DHFR originated from Chlamydia muridarum and Listeria grayi. We found that the acquisition of TMP resistance via decreased drug affinity is limited by a trade-off in catalytic efficiency. Protein stability is concurrently affected by the resistant mutants, which precludes a precise description of fitness from a single molecular trait. Application of the kinetic flux theory provided an accurate model to predict resistance phenotypes (IC50) quantitatively from a unique combination of the in vitro protein molecular properties. Further, we found that a controlled modulation of the GroEL/ES chaperonins and Lon protease levels affects the intracellular steady-state concentration of DHFR in a mutation-specific manner, whereas IC50 is changed proportionally, as indeed predicted by the model. This unveils a molecular rationale for the pleiotropic role of the protein quality control machinery on the evolution of antibiotic resistance, which, as we illustrate here, may drastically confound the evolutionary outcome. These results provide a comprehensive quantitative genotype–phenotype map for the essential enzyme that serves as an important target of antibiotic and anticancer therapies.

Algebraic Theory of Recombination Spaces

1997 ◽  
Vol 5 (3) ◽  
pp. 241-275 ◽  
Author(s):  
Peter F. Stadler ◽  
Günter P. Wagner
Keyword(s):  
Nearest Neighbor ◽  
Fitness Landscape ◽  
Algebraic Theory ◽  
Algebraic Approach ◽  
Search Space ◽  
Space Structure ◽  
Fitness Landscapes ◽  
Mathematical Representation ◽  
Laplacian Operator ◽  
Fourier Decomposition

A new mathematical representation is proposed for the configuration space structure induced by recombination, which we call “P-structure.” It consists of a mapping of pairs of objects to the power set of all objects in the search space. The mapping assigns to each pair of parental “genotypes” the set of all recombinant genotypes obtainable from the parental ones. It is shown that this construction allows a Fourier decomposition of fitness landscapes into a superposition of “elementary landscapes.” This decomposition is analogous to the Fourier decomposition of fitness landscapes on mutation spaces. The elementary landscapes are obtained as eigenfunctions of a Laplacian operator defined for P-structures. For binary string recombination, the elementary landscapes are exactly the p-spin functions (Walsh functions), that is, the same as the elementary landscapes of the string point mutation spaces (i.e., the hypercube). This supports the notion of a strong homomorphism between string mutation and recombination spaces. However, the effective nearest neighbor correlations on these elementary landscapes differ between mutation and recombination and among different recombination operators. On average, the nearest neighbor correlation is higher for one-point recombination than for uniform recombination. For one-point recombination, the correlations are higher for elementary landscapes with fewer interacting sites as well as for sites that have closer linkage, confirming the qualitative predictions of the Schema Theorem. We conclude that the algebraic approach to fitness landscape analysis can be extended to recombination spaces and provides an effective way to analyze the relative hardness of a landscape for a given recombination operator.

New Frontiers

2021 ◽  
pp. 7-27
Author(s):  
Gino Cattani ◽  
Mariano Mastrogiorgio
Keyword(s):  
Technological Innovation ◽  
Evolutionary Theory ◽  
Significant Influence ◽  
Evolutionary Biology ◽  
Innovation Process ◽  
Punctuated Equilibrium ◽  
Darwinian Evolution ◽  
Profound Impact ◽  
Equilibrium Speciation

Evolutionary thinking has grown significantly and has had a profound impact on various fields such as economics, strategy, and technological innovation. An important paradigm that underlies the evolutionary theory of innovation is neo-Darwinian evolution. According to this paradigm, evolution is gradualist and is based on the mechanisms of variation, selection, and retention. Starting from the 1970s, new theoretical advancements in evolutionary biology have recognized the central role of punctuated equilibrium, speciation, and exaptation in evolution and of Woesian dynamics. However, despite their significant influence in evolutionary biology, these advancements have been reflected only partially in evolutionary approaches to economics, strategy, and technological innovation. This chapter reviews these advancements and explores their key implications for innovation, such as the role of serendipity and unpre-stateability leading to disequilibrium in economics systems, and the importance of adopting an option-based logic during the innovation process.

scholarly journals The distribution of epistasis on simple fitness landscapes

2019 ◽  
Vol 15 (4) ◽  
pp. 20180881 ◽  
Author(s):  
Christelle Fraïsse ◽  
John J. Welch
Keyword(s):  
Evolutionary Dynamics ◽  
Fitness Landscape ◽  
Null Model ◽  
Fitness Landscapes ◽  
Mutational Bias ◽  
Variable Effect ◽  
Epistatic Effects ◽  
Standing Variation ◽  
Mean And Variance ◽  
Nucleolar Rna

Fitness interactions between mutations can influence a population’s evolution in many different ways. While epistatic effects are difficult to measure precisely, important information is captured by the mean and variance of log fitnesses for individuals carrying different numbers of mutations. We derive predictions for these quantities from a class of simple fitness landscapes, based on models of optimizing selection on quantitative traits. We also explore extensions to the models, including modular pleiotropy, variable effect sizes, mutational bias and maladaptation of the wild type. We illustrate our approach by reanalysing a large dataset of mutant effects in a yeast snoRNA (small nucleolar RNA). Though characterized by some large epistatic effects, these data give a good overall fit to the non-epistatic null model, suggesting that epistasis might have limited influence on the evolutionary dynamics in this system. We also show how the amount of epistasis depends on both the underlying fitness landscape and the distribution of mutations, and so is expected to vary in consistent ways between new mutations, standing variation and fixed mutations.

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