Properties of selected mutations and genotypic landscapes under Fisher's Geometric Model

The fitness landscape - the mapping between genotypes and fitness - determines properties of the process of adaptation. Several small genetic fitness landscapes have recently been built by selecting a handful of beneficial mutations and measuring fitness of all combinations of these mutations. Here we generate several testable predictions for the properties of these landscapes under Fisher's geometric model of adaptation (FGMA). When far from the fitness optimum, we analytically compute the fitness effect of beneficial mutations and their epistatic interactions. We show that epistasis may be negative or positive on average depending on the distance of the ancestral genotype to the optimum and whether mutations were independently selected or co-selected in an adaptive walk. Using simulations, we show that genetic landscapes built from FGMA are very close to an additive landscape when the ancestral strain is far from the optimum. However, when close to the optimum, a large diversity of landscape with substantial ruggedness and sign epistasis emerged. Strikingly, landscapes built from different realizations of stochastic adaptive walks in the same exact conditions were highly variable, suggesting that several realizations of small genetic landscapes are needed to gain information about the underlying architecture of the global adaptive landscape.Comment: 51 pages, 8 figure

Exploring the Fitness Landscape of TEM-1 Beta-Lactamase: A Survey of Intragenic Epistasis & the Fitness Effects of InDels

The distribution of fitness effects, or fitness landscape, of a protein offers a picture of the relationship between mutations and their effects on a broad scale. Mutations in a protein can have positive, negative, or neutral effects on its function. Mapping these effects on a large scale allows us to better understand evolutionary dynamics in nature and informs our ability to better engineer proteins in the laboratory. I used deep-mutational scanning techniques, including saturation mutagenesis, high-throughput selection, and DNA deep-sequencing, to explore two important and understudied aspects of the fitness landscape of TEM-1 beta-lactamase. First, I examined pairwise intragenic epistasis among sequential amino acid substitutions in TEM-1. Epistasis, or interactions between mutations, plays a central role in shaping the fitness landscape, but a clear picture of the prevalence and patterns of epistasis has yet to emerge. This study is the first to systematically examine pairwise epistasis throughout an entire protein performing its native function in its native host. I explored the relationship between epistasis and secondary structure, solvent accessibility, distance from the active site, amino acid identity, and individual mutant effect. I found pervasive negative epistasis, particularly in highly structured regions of the protein and among buried residues, and a high frequency of negative sign epistasis among individually beneficial mutations. Second, I present a near-comprehensive analysis of the fitness effects of single amino acid insertions and deletions (InDels) in TEM-1. Short InDels are a common type of mutation in nature, often having important consequences, such as opening new pathways for adaptation. InDels also represent a useful source of variation in the protein engineering toolbox. Yet despite their importance and utility, the distribution of fitness effects of InDels is vastly understudied compared to substitutions. I found InDels to be largely deleterious, but notable regions of tolerance were observed throughout the protein. I found secondary structure, weighted contact number, and evolutionary variation in class A beta-lactamases to be the most predictive of their fitness effects

A framework for evolutionary systems biology

<p>Abstract</p> <p>Background</p> <p>Many difficult problems in evolutionary genomics are related to mutations that have weak effects on fitness, as the consequences of mutations with large effects are often simple to predict. Current systems biology has accumulated much data on mutations with large effects and can predict the properties of knockout mutants in some systems. However experimental methods are too insensitive to observe small effects.</p> <p>Results</p> <p>Here I propose a novel framework that brings together evolutionary theory and current systems biology approaches in order to quantify small effects of mutations and their epistatic interactions <it>in silico</it>. Central to this approach is the definition of fitness correlates that can be computed in some current systems biology models employing the rigorous algorithms that are at the core of much work in computational systems biology. The framework exploits synergies between the realism of such models and the need to understand real systems in evolutionary theory. This framework can address many longstanding topics in evolutionary biology by defining various 'levels' of the adaptive landscape. Addressed topics include the distribution of mutational effects on fitness, as well as the nature of advantageous mutations, epistasis and robustness. Combining corresponding parameter estimates with population genetics models raises the possibility of testing evolutionary hypotheses at a new level of realism.</p> <p>Conclusion</p> <p>EvoSysBio is expected to lead to a more detailed understanding of the fundamental principles of life by combining knowledge about well-known biological systems from several disciplines. This will benefit both evolutionary theory and current systems biology. Understanding robustness by analysing distributions of mutational effects and epistasis is pivotal for drug design, cancer research, responsible genetic engineering in synthetic biology and many other practical applications.</p

A Multimodal Problem for Competitive Coevolution

Coevolutionary algorithms are a special kind of evolutionary algorithm with advantages in solving certain specific kinds of problems. In particular, competitive coevolutionary algorithms can be used to study problems in which two sides compete against each other and must choose a suitable strategy. Often these problems are multimodal - there is more than one strong strategy for each side. In this paper, we introduce a scalable multimodal test problem for competitive coevolution, and use it to investigate the effectiveness of some common coevolutionary algorithm enhancement techniques

An Open Framework for Constructing Continuous Optimization Problems

Many artificial benchmark problems have been proposed for different kinds of continuous optimization, e.g., global optimization, multi-modal optimization, multi-objective optimization, dynamic optimization, and constrained optimization. However, there is no unified framework for constructing these types of problems and possible properties of many problems are not fully tunable. This will cause difficulties for researchers to analyze strengths and weaknesses of an algorithm. To address these issues, this paper proposes a simple and intuitive framework, which is able to construct different kinds of problems for continuous optimization. The framework utilizes the k-d tree to partition the search space and sets a certain number of simple functions in each subspace. The framework is implemented into global/multimodal optimization, dynamic single objective optimization, multiobjective optimization, and dynamic multi-objective optimization, respectively. Properties of the proposed framework are discussed and verified with traditional evolutionary algorithms

Adaptation and self-organization in evolutionary algorithms

The objective of Evolutionary Computation is to solve practical problems (e.g.optimization, data mining) by simulating the mechanisms of natural evolution. This thesis addresses several topics related to adaptation and self-organization in evolving systems with the overall aims of improving the performance of Evolutionary Algorithms (EA), understanding its relation to natural evolution, and incorporating new mechanisms for mimicking complex biological systems. Part I of this thesis presents a new mechanism for allowing an EA to adapt its behavior in response to changes in the environment. Using the new approach, adaptation of EA behavior (i.e. control of EA design parameters) is driven by an analysis of population dynamics, as opposed to the more traditional use of fitness measurements. Comparisons with a number of adaptive control methods from the literature indicate substantial improvements in algorithm performance for a range of artificial and engineering design problems. Part II of this thesis involves a more thorough analysis of EA behavior based on the methods derived in Part 1. In particular, several properties of EA population dynamics are measured and compared with observations of evolutionary dynamics in nature. The results demonstrate that some large scale spatial and temporal features of EA dynamics are remarkably similar to their natural counterpart. Compatibility of EA with the Theory of Self-Organized Criticality is also discussed. Part III proposes fundamentally new directions in EA research which are inspired by the conclusions drawn in Part II. These changes involve new mechanisms which allow self-organization of the EA to occur in ways which extend beyond its common convergence in parameter space. In particular, network models for EA populations are developed where the network structure is dynamically coupled to EA population dynamics. Results indicate strong improvements in algorithm performance compared to cellular Genetic Algorithms and non-distributed EA designs. Furthermore, topological analysis indicates that the population network can spontaneously evolve to display similar characteristics to the interaction networks of complex biological systems

Functional and evolutionary implications of in silico gene deletions

Understanding how genetic modifications, individual or in combination, affect organismal fitness or other phenotypes is a challenge common to several areas of biology, including human health & genetics, metabolic engineering, and evolutionary biology. The importance of a gene can be quantified by measuring the phenotypic impact of its associated genetic perturbations "here and now", e.g. the growth rate of a mutant microbe. However, each gene also maintains a historical record of its cumulative importance maintained throughout millions of years of natural selection in the form of its degree of sequence conservation along phylogenetic branches. This thesis focuses on whether and how the phenotypic and evolutionary importance of genes are related to each other. Towards this goal, I developed a new approach for characterizing the phenotypic consequences of genetic modifications in genome-scale biochemical networks using constraint-based computational models of metabolism. In particular, I investigated the impact of gene loss events on fitness in the model organism Saccharomyces cerevisiae, and found that my new metric for estimating the cost of gene deletion correlates with gene evolutionary rate. I found that previous failures to uncover this correlation using similar techniques may have been the result of an incorrect assumption about how isoenzymes deletions affect the reaction they catalyze. I next hypothesized that the improvement my metric showed in predicting the cost of isoenzyme loss could translate into an improved capacity to predict the impact of pairs of gene deletions involving isoenzymes. Studies of such pair-wise genetic perturbations are important, because the extent to which a genetic perturbation modifies any given phenotype is often dependent on the genetic background upon which it has been performed. This lack of independence within sets of perturbations is termed epistasis. My results showed that, indeed, the new metric displays an increased capacity to predict epistatic interactions between pairs of genes. In addition to shedding light on the relationship between the functional and evolutionary importance of genes, further developments of our approach may lead to better prediction of gene knockout phenotypes, with applications ranging from metabolic engineering to the search for gene targets for therapeutic applications