Fitness Landscape Models

2003 ◽  
Author(s):  
M. E. J. Newman ◽  
R. G. Palmer
Keyword(s):  
Statistical Physics ◽  
Evolutionary Dynamics ◽  
Fitness Landscape ◽  
Real Life ◽  
Extinction Rate ◽  
Epistatic Interactions ◽  
Nk Model ◽  
Energy Models ◽  
Landscape Models ◽  
A Genome

Kauffman (1993, 1995; Kauffman and Levin 1987; Kauffman and Johnsen 1991) has proposed and studied in depth a class of models referred to as NK models, which are models of random fitness landscapes on which one can implement a variety of types of evolutionary dynamics and study the development and interaction of species. (The letters N and K do not stand for anything; they are the names of parameters in the model.) Based on the results of extensive simulations of NK models, Kauffman and co-workers have suggested a number of possible connections between the dynamics of evolution and the extinction rate. To a large extent it is this work which has sparked recent interest in biotic mechanisms for mass extinction. In this chapter we review Kauffman's work in detail. An NK model is a model of a single rugged landscape, which is similar in construction to the spin-glass models of statistical physics (Fischer and Hertz 1991), particularly p-spin models (Derrida 1980) and random energy models (Derrida 1981). Used as a model of species fitness the NK model maps the states of a model genome onto a scalar fitness W. This is a simplification of what happens in real life, where the genotype is first mapped onto phenotype and only then onto fitness. However, it is a useful simplification which makes simulation of the model for large systems tractable. As long as we bear in mind that this simplification has been made, the model can still teach us many useful things. The NK model is a model of a genome with N genes. Each gene has A alleles. In most of Kauffman's studies of the model he used A = 2, a binary genetic code, but his results are not limited to this case. The model also includes epistatic interactions between genes—interactions whereby the state of one gene affects the contribution of another to the overall fitness of the species. In fact, it is these epistatic interactions which are responsible for the ruggedness of the fitness landscape.

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 A family of fitness landscapes modeled through gene regulatory networks

2021 ◽  
Author(s):  
Chia-Hung Yang ◽  
Samuel V. Scarpino
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Evolutionary Dynamics ◽  
Fitness Landscape ◽  
Fitness Function ◽  
Fitness Landscapes ◽  
Relative Fitness ◽  
Landscape Models ◽  
Fitness Value ◽  
Gene Regulatory

AbstractOver 100 years, Fitness landscapes have been a powerful metaphor for understanding the evolution of biological systems. These landscapes describe how genotypes are connected to each other and are related according to relative fitness. Despite the high dimensionality of such real-world landscapes, empirical studies are often limited in their ability to quantify the fitness of different genotypes beyond point mutations, while theoretical works attempt statistical/mechanistic models to reason the overall landscape structure. However, most classical fitness landscape models overlook an instinctive constraint that genotypes leading to the same phenotype almost certainly share the same fitness value, since the information of genotype-phenotype mapping is rarely incorporated. Here, we investigate fitness landscape models through the lens of Gene Regulatory Networks (GRNs), where the regulatory products are computed from multiple genes and collectively treated as the phenotypes. With the assumption that regulatory mediators/products exhibit binary states, we prove topographical features of GRN fitness landscape models such as accessibility and connectivity insensitive to the choice of the fitness function. Furthermore, using graph theory, we deduce a mesoscopic structure underlying GRN fitness landscape models that retains necessary information for evolutionary dynamics with minimal complexity. We also propose an algorithm to construct such a mesoscopic backbone which is more efficient than the brute-force approach. Combined, this work provides mathematical implications for fitness landscape models with high-dimensional genotype-phenotype mapping, yielding the potential to elucidate empirical landscapes and their resulting evolutionary processes in a manner complementary to existing computational studies.

scholarly journals Dinosaurs in decline tens of millions of years before their final extinction

2016 ◽  
Vol 113 (18) ◽  
pp. 5036-5040 ◽  
Author(s):  
Manabu Sakamoto ◽  
Michael J. Benton ◽  
Chris Venditti
Keyword(s):  
Mass Extinction ◽  
Marked Reduction ◽  
Evolutionary Dynamics ◽  
General Pattern ◽  
Extinction Rate ◽  
Catastrophic Event ◽  
Extinction Event ◽  
Mass Extinction Event ◽  
First Time

Whether dinosaurs were in a long-term decline or whether they were reigning strong right up to their final disappearance at the Cretaceous–Paleogene (K-Pg) mass extinction event 66 Mya has been debated for decades with no clear resolution. The dispute has continued unresolved because of a lack of statistical rigor and appropriate evolutionary framework. Here, for the first time to our knowledge, we apply a Bayesian phylogenetic approach to model the evolutionary dynamics of speciation and extinction through time in Mesozoic dinosaurs, properly taking account of previously ignored statistical violations. We find overwhelming support for a long-term decline across all dinosaurs and within all three dinosaurian subclades (Ornithischia, Sauropodomorpha, and Theropoda), where speciation rate slowed down through time and was ultimately exceeded by extinction rate tens of millions of years before the K-Pg boundary. The only exceptions to this general pattern are the morphologically specialized herbivores, the Hadrosauriformes and Ceratopsidae, which show rapid species proliferations throughout the Late Cretaceous instead. Our results highlight that, despite some heterogeneity in speciation dynamics, dinosaurs showed a marked reduction in their ability to replace extinct species with new ones, making them vulnerable to extinction and unable to respond quickly to and recover from the final catastrophic event.

Landscape Models of Complex Change

2021 ◽  
pp. 554-580
Author(s):  
Daniel Albert ◽  
Martin Ganco
Keyword(s):  
Organizational Change ◽  
Future Research ◽  
Research Directions ◽  
Traditional Form ◽  
Nk Model ◽  
Complex Change ◽  
Landscape Models ◽  
Recent Advances ◽  
Complex Landscape ◽  
Future Research Directions

This chapter reviews recent advances in the NK modeling literature conceptualizing organizational change and innovation as a search over a complex landscape. It discusses both strengths and limitations of this perspective and delineates potential for future research directions. The key argument is that the NK model in its traditional form may be exhausting the theoretical insights that it can provide to the field. However, substantial modifications and extensions of the NK model or new classes of landscape models may provide fresh perspectives. Specifically, we consider the modeling efforts that endogenize the landscape construction as the next frontier in this literature. We also discuss several recent studies that incorporate various extensions of the NK model and allow for agent-driven changes to the landscape.

scholarly journals Environmental changes dictate selection and nonspecific epistasis in an empirical phenotype-environment-fitness landscape

2021 ◽  
Author(s):  
J.Z. Chen ◽  
D.M. Fowler ◽  
N. Tokuriki
Keyword(s):  
Selection Pressure ◽  
Evolutionary Dynamics ◽  
Environmental Changes ◽  
Fitness Landscape ◽  
Resistance Level ◽  
Phenotypic Changes ◽  
Environmental Selection ◽  
Selection Environment ◽  
Data Points ◽  
Environmental Dependence

SummaryThe fitness landscape, a function that maps genotypic and phenotypic changes to their effects on fitness, is an invaluable concept in evolutionary biochemistry. Though widely discussed, measurements of phenotype-fitness landscapes in proteins remain scarce. Here, we quantify all single mutational effects on fitness and phenotype (antibiotic resistance level) of VIM-2 β-lactamase (5600 variants) across a 64-fold range of ampicillin concentrations by deep mutational scanning. We then construct a phenotype-fitness landscape that takes variations in environmental selection pressure into account (a phenotype-environment-fitness landscape). We found that a simple, empirical landscape accurately models the ~39,000 mutational data points, which suggests the evolution of VIM-2 can be predicted based on the selection environment. Our landscape provides new quantitative knowledge on the evolution of the β-lactamases and proteins in general, particularly their evolutionary dynamics under sub-inhibitory antibiotic concentrations, as well as the mechanisms and environmental dependence of nonspecific epistasis.

Self-Replicating Patterns in 2D Linear Cellular Automata

2014 ◽  
Vol 24 (01) ◽  
pp. 1430002 ◽  
Author(s):  
Selman Uguz ◽  
Uḡur Sahin ◽  
Hasan Akin ◽  
Irfan Siap
Keyword(s):  
Cellular Automata ◽  
Self Assembly ◽  
Statistical Physics ◽  
Spatial Scales ◽  
Real Life ◽  
Continuous System ◽  
Random Number Generation ◽  
Two Dimensional ◽  
Textile Design ◽  
Genetics Research

This paper studies the theoretical aspects of two-dimensional cellular automata (CAs), it classifies this family into subfamilies with respect to their visual behavior and presents an application to pseudo random number generation by hybridization of these subfamilies. Even though the basic construction of a cellular automaton is a discrete model, its macroscopic behavior at large evolution times and on large spatial scales can be a close approximation to a continuous system. Beyond some statistical properties, we consider geometrical and visual aspects of patterns generated by CA evolution. The present work focuses on the theory of two-dimensional CA with respect to uniform periodic, adiabatic and reflexive boundary CA (2D PB, AB and RB) conditions. In total, there are 512 linear rules over the binary field ℤ2for each boundary condition and the effects of these CA are studied on applications of image processing for self-replicating patterns. After establishing the representation matrices of 2D CA, these linear CA rules are classified into groups of nine and eight types according to their boundary conditions and the number of neighboring cells influencing the cells under consideration. All linear rules have been found to be rendering multiple self-replicating copies of a given image depending on these types. Multiple copies of any arbitrary image corresponding to CA find innumerable applications in real life situation, e.g. textile design, DNA genetics research, statistical physics, molecular self-assembly and artificial life, etc. We conclude by presenting a successful application for generating pseudo numbers to be used in cryptography by hybridization of these 2D CA subfamilies.

Evolutionary dynamics, epistatic interactions, and biological information

2010 ◽  
Vol 266 (4) ◽  
pp. 584-594 ◽  
Author(s):  
Christopher C. Strelioff ◽  
Richard E. Lenski ◽  
Charles Ofria
Keyword(s):  
Evolutionary Dynamics ◽  
Biological Information ◽  
Epistatic Interactions

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.

Phylogenomic analysis of SARS-CoV-2 genomes from western India reveals unique linked mutations

2020 ◽  
Author(s):  
Dhiraj Paul ◽  
Kunal Jani ◽  
Janesh Kumar ◽  
Radha Chauhan ◽  
Vasudevan Seshadri ◽  
...  
Keyword(s):  
Evolutionary Dynamics ◽  
Fitness Landscape ◽  
Geographical Location ◽  
Specific Pattern ◽  
Distinct Pattern ◽  
Viral Fitness ◽  
Phylogenomic Analysis ◽  
Western India ◽  
Asymptomatic Patients ◽  
Major Cluster

AbstractIndia has become the third worst-hit nation by the COVID-19 pandemic caused by the SARS-CoV-2 virus. Here, we investigated the molecular, phylogenomic, and evolutionary dynamics of SARS-CoV-2 in western India, the most affected region of the country. A total of 90 genomes were sequenced. Four nucleotide variants, namely C241T, C3037T, C14408T (Pro4715Leu), and A23403G (Asp614Gly), located at 5’UTR, Orf1a, Orf1b, and Spike protein regions of the genome, respectively, were predominant and ubiquitous (90%). Phylogenetic analysis of the genomes revealed four distinct clusters, formed owing to different variants. The major cluster (cluster 4) is distinguished by mutations C313T, C5700A, G28881A are unique patterns and observed in 45% of samples. We thus report a newly emerging pattern of linked mutations. The predominance of these linked mutations suggests that they are likely a part of the viral fitness landscape. A novel and distinct pattern of mutations in the viral strains of each of the districts was observed. The Satara district viral strains showed mutations primarily at the 3′ end of the genome, while Nashik district viral strains displayed mutations at the 5′ end of the genome. Characterization of Pune strains showed that a novel variant has overtaken the other strains. Examination of the frequency of three mutations i.e., C313T, C5700A, G28881A in symptomatic versus asymptomatic patients indicated an increased occurrence in symptomatic cases, which is more prominent in females. The age-wise specific pattern of mutation is observed. Mutations C18877T, G20326A, G24794T, G25563T, G26152T, and C26735T are found in more than 30% study samples in the age group of 10-25. Intriguingly, these mutations are not detected in the higher age range 61-80. These findings portray the prevalence of unique linked mutations in SARS-CoV-2 in western India and their prevalence in symptomatic patients.ImportanceElucidation of the SARS-CoV-2 mutational landscape within a specific geographical location, and its relationship with age and symptoms, is essential to understand its local transmission dynamics and control. Here we present the first comprehensive study on genome and mutation pattern analysis of SARS-CoV-2 from the western part of India, the worst affected region by the pandemic. Our analysis revealed three unique linked mutations, which are prevalent in most of the sequences studied. These may serve as a molecular marker to track the spread of this viral variant to different places.

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