Evolutionary Study of Interethnic Cooperation

NASA Astrophysics Data System (ADS)

Kvasnicka, Vladimir; Pospichal, Jiri

The purpose of this communication is to present an evolutionary study of cooperation between two ethnic groups. The used model is stimulated by the seminal paper of J. D. Fearon and D. D. Laitin (Explaining Interethnic Cooperation, American Political Science Review, 90 (1996), pp. 715-735), where the iterated prisoner's dilemma was used to model intra- and interethnic interactions. We reformulated their approach in a form of evolutionary prisoner's dilemma method, where a population of strategies is evolved by applying simple reproduction process with a Darwin metaphor of natural selection (a probability of selection to the reproduction is proportional to a fitness). Our computer simulations show that an application of a principle of collective guilt does not lead to an emergence of an interethnic cooperation. When an administrator is introduced, then an emergence of interethnic cooperation may be observed. Furthermore, if the ethnic groups are of very different sizes, then the principle of collective guilt may be very devastating for smaller group so that intraethnic cooperation is destroyed. The second strategy of cooperation is called the personal responsibility, where agents that defected within interethnic interactions are punished inside of their ethnic groups. It means, unlikely to the principle of collective guilt, that there exists only one type of punishment, loosely speaking, agents are punished "personally." All the substantial computational results were checked and interpreted analytically within the theory of evolutionary stable strategies. Moreover, this theoretical approach offers mechanisms of simple scenarios explaining why some particular strategies are stable or not.

Algorithmic Mechanism Design of Evolutionary Computation.

PubMed

Pei, Yan

2015-01-01

We consider algorithmic design, enhancement, and improvement of evolutionary computation as a mechanism design problem. All individuals or several groups of individuals can be considered as self-interested agents. The individuals in evolutionary computation can manipulate parameter settings and operations by satisfying their own preferences, which are defined by an evolutionary computation algorithm designer, rather than by following a fixed algorithm rule. Evolutionary computation algorithm designers or self-adaptive methods should construct proper rules and mechanisms for all agents (individuals) to conduct their evolution behaviour correctly in order to definitely achieve the desired and preset objective(s). As a case study, we propose a formal framework on parameter setting, strategy selection, and algorithmic design of evolutionary computation by considering the Nash strategy equilibrium of a mechanism design in the search process. The evaluation results present the efficiency of the framework. This primary principle can be implemented in any evolutionary computation algorithm that needs to consider strategy selection issues in its optimization process. The final objective of our work is to solve evolutionary computation design as an algorithmic mechanism design problem and establish its fundamental aspect by taking this perspective. This paper is the first step towards achieving this objective by implementing a strategy equilibrium solution (such as Nash equilibrium) in evolutionary computation algorithm.

Algorithmic Mechanism Design of Evolutionary Computation

PubMed Central

2015-01-01

We consider algorithmic design, enhancement, and improvement of evolutionary computation as a mechanism design problem. All individuals or several groups of individuals can be considered as self-interested agents. The individuals in evolutionary computation can manipulate parameter settings and operations by satisfying their own preferences, which are defined by an evolutionary computation algorithm designer, rather than by following a fixed algorithm rule. Evolutionary computation algorithm designers or self-adaptive methods should construct proper rules and mechanisms for all agents (individuals) to conduct their evolution behaviour correctly in order to definitely achieve the desired and preset objective(s). As a case study, we propose a formal framework on parameter setting, strategy selection, and algorithmic design of evolutionary computation by considering the Nash strategy equilibrium of a mechanism design in the search process. The evaluation results present the efficiency of the framework. This primary principle can be implemented in any evolutionary computation algorithm that needs to consider strategy selection issues in its optimization process. The final objective of our work is to solve evolutionary computation design as an algorithmic mechanism design problem and establish its fundamental aspect by taking this perspective. This paper is the first step towards achieving this objective by implementing a strategy equilibrium solution (such as Nash equilibrium) in evolutionary computation algorithm. PMID:26257777

Ensemble Architecture for Prediction of Enzyme-ligand Binding Residues Using Evolutionary Information.

PubMed

Pai, Priyadarshini P; Dattatreya, Rohit Kadam; Mondal, Sukanta

2017-11-01

Enzyme interactions with ligands are crucial for various biochemical reactions governing life. Over many years attempts to identify these residues for biotechnological manipulations have been made using experimental and computational techniques. The computational approaches have gathered impetus with the accruing availability of sequence and structure information, broadly classified into template-based and de novo methods. One of the predominant de novo methods using sequence information involves application of biological properties for supervised machine learning. Here, we propose a support vector machines-based ensemble for prediction of protein-ligand interacting residues using one of the most important discriminative contributing properties in the interacting residue neighbourhood, i. e., evolutionary information in the form of position-specific- scoring matrix (PSSM). The study has been performed on a non-redundant dataset comprising of 9269 interacting and 91773 non-interacting residues for prediction model generation and further evaluation. Of the various PSSM-based models explored, the proposed method named ROBBY (pRediction Of Biologically relevant small molecule Binding residues on enzYmes) shows an accuracy of 84.0 %, Matthews Correlation Coefficient of 0.343 and F-measure of 39.0 % on 78 test enzymes. Further, scope of adding domain knowledge such as pocket information has also been investigated; results showed significant enhancement in method precision. Findings are hoped to boost the reliability of small-molecule ligand interaction prediction for enzyme applications and drug design. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

On the design of neuro-controllers for individual and social learning behaviour in autonomous robots: an evolutionary approach

NASA Astrophysics Data System (ADS)

Pini, Giovanni; Tuci, Elio

2008-06-01

In biology/psychology, the capability of natural organisms to learn from the observation/interaction with conspecifics is referred to as social learning. Roboticists have recently developed an interest in social learning, since it might represent an effective strategy to enhance the adaptivity of a team of autonomous robots. In this study, we show that a methodological approach based on artifcial neural networks shaped by evolutionary computation techniques can be successfully employed to synthesise the individual and social learning mechanisms for robots required to learn a desired action (i.e. phototaxis or antiphototaxis).

Application of evolutionary computation in ECAD problems

NASA Astrophysics Data System (ADS)

Lee, Dae-Hyun; Hwang, Seung H.

1998-10-01

Design of modern electronic system is a complicated task which demands the use of computer- aided design (CAD) tools. Since a lot of problems in ECAD are combinatorial optimization problems, evolutionary computations such as genetic algorithms and evolutionary programming have been widely employed to solve those problems. We have applied evolutionary computation techniques to solve ECAD problems such as technology mapping, microcode-bit optimization, data path ordering and peak power estimation, where their benefits are well observed. This paper presents experiences and discusses issues in those applications.

IDEA: Interactive Display for Evolutionary Analyses.

PubMed

Egan, Amy; Mahurkar, Anup; Crabtree, Jonathan; Badger, Jonathan H; Carlton, Jane M; Silva, Joana C

2008-12-08

The availability of complete genomic sequences for hundreds of organisms promises to make obtaining genome-wide estimates of substitution rates, selective constraints and other molecular evolution variables of interest an increasingly important approach to addressing broad evolutionary questions. Two of the programs most widely used for this purpose are codeml and baseml, parts of the PAML (Phylogenetic Analysis by Maximum Likelihood) suite. A significant drawback of these programs is their lack of a graphical user interface, which can limit their user base and considerably reduce their efficiency. We have developed IDEA (Interactive Display for Evolutionary Analyses), an intuitive graphical input and output interface which interacts with PHYLIP for phylogeny reconstruction and with codeml and baseml for molecular evolution analyses. IDEA's graphical input and visualization interfaces eliminate the need to edit and parse text input and output files, reducing the likelihood of errors and improving processing time. Further, its interactive output display gives the user immediate access to results. Finally, IDEA can process data in parallel on a local machine or computing grid, allowing genome-wide analyses to be completed quickly. IDEA provides a graphical user interface that allows the user to follow a codeml or baseml analysis from parameter input through to the exploration of results. Novel options streamline the analysis process, and post-analysis visualization of phylogenies, evolutionary rates and selective constraint along protein sequences simplifies the interpretation of results. The integration of these functions into a single tool eliminates the need for lengthy data handling and parsing, significantly expediting access to global patterns in the data.

IDEA: Interactive Display for Evolutionary Analyses

PubMed Central

Egan, Amy; Mahurkar, Anup; Crabtree, Jonathan; Badger, Jonathan H; Carlton, Jane M; Silva, Joana C

2008-01-01

Background The availability of complete genomic sequences for hundreds of organisms promises to make obtaining genome-wide estimates of substitution rates, selective constraints and other molecular evolution variables of interest an increasingly important approach to addressing broad evolutionary questions. Two of the programs most widely used for this purpose are codeml and baseml, parts of the PAML (Phylogenetic Analysis by Maximum Likelihood) suite. A significant drawback of these programs is their lack of a graphical user interface, which can limit their user base and considerably reduce their efficiency. Results We have developed IDEA (Interactive Display for Evolutionary Analyses), an intuitive graphical input and output interface which interacts with PHYLIP for phylogeny reconstruction and with codeml and baseml for molecular evolution analyses. IDEA's graphical input and visualization interfaces eliminate the need to edit and parse text input and output files, reducing the likelihood of errors and improving processing time. Further, its interactive output display gives the user immediate access to results. Finally, IDEA can process data in parallel on a local machine or computing grid, allowing genome-wide analyses to be completed quickly. Conclusion IDEA provides a graphical user interface that allows the user to follow a codeml or baseml analysis from parameter input through to the exploration of results. Novel options streamline the analysis process, and post-analysis visualization of phylogenies, evolutionary rates and selective constraint along protein sequences simplifies the interpretation of results. The integration of these functions into a single tool eliminates the need for lengthy data handling and parsing, significantly expediting access to global patterns in the data. PMID:19061522

Online mentalising investigated with functional MRI.

PubMed

Kircher, Tilo; Blümel, Isabelle; Marjoram, Dominic; Lataster, Tineke; Krabbendam, Lydia; Weber, Jochen; van Os, Jim; Krach, Sören

2009-05-01

For successful interpersonal communication, inferring intentions, goals or desires of others is highly advantageous. Increasingly, humans also interact with computers or robots. In this study, we sought to determine to what degree an interactive task, which involves receiving feedback from social partners that can be used to infer intent, engaged the medial prefrontal cortex, a region previously associated with Theory of Mind processes among others. Participants were scanned using fMRI as they played an adapted version of the Prisoner's Dilemma Game with alleged human and computer partners who were outside the scanner. The medial frontal cortex was activated when both human and computer partner were played, while the direct contrast revealed significantly stronger signal change during the human-human interaction. The results suggest a link between activity in the medial prefrontal cortex and the partner played in a mentalising task. This signal change was also present for to the computers partner. Implying agency or a will to non-human actors might be an innate human resource that could lead to an evolutionary advantage.

Understanding Evolutionary Potential in Virtual CPU Instruction Set Architectures

PubMed Central

Bryson, David M.; Ofria, Charles

2013-01-01

We investigate fundamental decisions in the design of instruction set architectures for linear genetic programs that are used as both model systems in evolutionary biology and underlying solution representations in evolutionary computation. We subjected digital organisms with each tested architecture to seven different computational environments designed to present a range of evolutionary challenges. Our goal was to engineer a general purpose architecture that would be effective under a broad range of evolutionary conditions. We evaluated six different types of architectural features for the virtual CPUs: (1) genetic flexibility: we allowed digital organisms to more precisely modify the function of genetic instructions, (2) memory: we provided an increased number of registers in the virtual CPUs, (3) decoupled sensors and actuators: we separated input and output operations to enable greater control over data flow. We also tested a variety of methods to regulate expression: (4) explicit labels that allow programs to dynamically refer to specific genome positions, (5) position-relative search instructions, and (6) multiple new flow control instructions, including conditionals and jumps. Each of these features also adds complication to the instruction set and risks slowing evolution due to epistatic interactions. Two features (multiple argument specification and separated I/O) demonstrated substantial improvements in the majority of test environments, along with versions of each of the remaining architecture modifications that show significant improvements in multiple environments. However, some tested modifications were detrimental, though most exhibit no systematic effects on evolutionary potential, highlighting the robustness of digital evolution. Combined, these observations enhance our understanding of how instruction architecture impacts evolutionary potential, enabling the creation of architectures that support more rapid evolution of complex solutions to a broad range of challenges. PMID:24376669

A framework for evolutionary systems biology

PubMed Central

Loewe, Laurence

2009-01-01

Background 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. Results 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 in silico. 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. Conclusion 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. PMID:19239699

Life's attractors : understanding developmental systems through reverse engineering and in silico evolution.

PubMed

Jaeger, Johannes; Crombach, Anton

2012-01-01

We propose an approach to evolutionary systems biology which is based on reverse engineering of gene regulatory networks and in silico evolutionary simulations. We infer regulatory parameters for gene networks by fitting computational models to quantitative expression data. This allows us to characterize the regulatory structure and dynamical repertoire of evolving gene regulatory networks with a reasonable amount of experimental and computational effort. We use the resulting network models to identify those regulatory interactions that are conserved, and those that have diverged between different species. Moreover, we use the models obtained by data fitting as starting points for simulations of evolutionary transitions between species. These simulations enable us to investigate whether such transitions are random, or whether they show stereotypical series of regulatory changes which depend on the structure and dynamical repertoire of an evolving network. Finally, we present a case study-the gap gene network in dipterans (flies, midges, and mosquitoes)-to illustrate the practical application of the proposed methodology, and to highlight the kind of biological insights that can be gained by this approach.

The tangled bank of amino acids

PubMed Central

Pollock, David D.

2016-01-01

Abstract The use of amino acid substitution matrices to model protein evolution has yielded important insights into both the evolutionary process and the properties of specific protein families. In order to make these models tractable, standard substitution matrices represent the average results of the evolutionary process rather than the underlying molecular biophysics and population genetics, treating proteins as a set of independently evolving sites rather than as an integrated biomolecular entity. With advances in computing and the increasing availability of sequence data, we now have an opportunity to move beyond current substitution matrices to more interpretable mechanistic models with greater fidelity to the evolutionary process of mutation and selection and the holistic nature of the selective constraints. As part of this endeavour, we consider how epistatic interactions induce spatial and temporal rate heterogeneity, and demonstrate how these generally ignored factors can reconcile standard substitution rate matrices and the underlying biology, allowing us to better understand the meaning of these substitution rates. Using computational simulations of protein evolution, we can demonstrate the importance of both spatial and temporal heterogeneity in modelling protein evolution. PMID:27028523

Development of X-TOOLSS: Preliminary Design of Space Systems Using Evolutionary Computation

NASA Technical Reports Server (NTRS)

Schnell, Andrew R.; Hull, Patrick V.; Turner, Mike L.; Dozier, Gerry; Alverson, Lauren; Garrett, Aaron; Reneau, Jarred

2008-01-01

Evolutionary computational (EC) techniques such as genetic algorithms (GA) have been identified as promising methods to explore the design space of mechanical and electrical systems at the earliest stages of design. In this paper the authors summarize their research in the use of evolutionary computation to develop preliminary designs for various space systems. An evolutionary computational solver developed over the course of the research, X-TOOLSS (Exploration Toolset for the Optimization of Launch and Space Systems) is discussed. With the success of early, low-fidelity example problems, an outline of work involving more computationally complex models is discussed.

From evolutionary computation to the evolution of things.

PubMed

Eiben, Agoston E; Smith, Jim

2015-05-28

Evolution has provided a source of inspiration for algorithm designers since the birth of computers. The resulting field, evolutionary computation, has been successful in solving engineering tasks ranging in outlook from the molecular to the astronomical. Today, the field is entering a new phase as evolutionary algorithms that take place in hardware are developed, opening up new avenues towards autonomous machines that can adapt to their environment. We discuss how evolutionary computation compares with natural evolution and what its benefits are relative to other computing approaches, and we introduce the emerging area of artificial evolution in physical systems.

Protein-Protein Interactions in a Crowded Environment: An Analysis via Cross-Docking Simulations and Evolutionary Information

PubMed Central

Lopes, Anne; Sacquin-Mora, Sophie; Dimitrova, Viktoriya; Laine, Elodie; Ponty, Yann; Carbone, Alessandra

2013-01-01

Large-scale analyses of protein-protein interactions based on coarse-grain molecular docking simulations and binding site predictions resulting from evolutionary sequence analysis, are possible and realizable on hundreds of proteins with variate structures and interfaces. We demonstrated this on the 168 proteins of the Mintseris Benchmark 2.0. On the one hand, we evaluated the quality of the interaction signal and the contribution of docking information compared to evolutionary information showing that the combination of the two improves partner identification. On the other hand, since protein interactions usually occur in crowded environments with several competing partners, we realized a thorough analysis of the interactions of proteins with true partners but also with non-partners to evaluate whether proteins in the environment, competing with the true partner, affect its identification. We found three populations of proteins: strongly competing, never competing, and interacting with different levels of strength. Populations and levels of strength are numerically characterized and provide a signature for the behavior of a protein in the crowded environment. We showed that partner identification, to some extent, does not depend on the competing partners present in the environment, that certain biochemical classes of proteins are intrinsically easier to analyze than others, and that small proteins are not more promiscuous than large ones. Our approach brings to light that the knowledge of the binding site can be used to reduce the high computational cost of docking simulations with no consequence in the quality of the results, demonstrating the possibility to apply coarse-grain docking to datasets made of thousands of proteins. Comparison with all available large-scale analyses aimed to partner predictions is realized. We release the complete decoys set issued by coarse-grain docking simulations of both true and false interacting partners, and their evolutionary sequence analysis leading to binding site predictions. Download site: http://www.lgm.upmc.fr/CCDMintseris/ PMID:24339765

Effect of reference genome selection on the performance of computational methods for genome-wide protein-protein interaction prediction.

PubMed

Muley, Vijaykumar Yogesh; Ranjan, Akash

2012-01-01

Recent progress in computational methods for predicting physical and functional protein-protein interactions has provided new insights into the complexity of biological processes. Most of these methods assume that functionally interacting proteins are likely to have a shared evolutionary history. This history can be traced out for the protein pairs of a query genome by correlating different evolutionary aspects of their homologs in multiple genomes known as the reference genomes. These methods include phylogenetic profiling, gene neighborhood and co-occurrence of the orthologous protein coding genes in the same cluster or operon. These are collectively known as genomic context methods. On the other hand a method called mirrortree is based on the similarity of phylogenetic trees between two interacting proteins. Comprehensive performance analyses of these methods have been frequently reported in literature. However, very few studies provide insight into the effect of reference genome selection on detection of meaningful protein interactions. We analyzed the performance of four methods and their variants to understand the effect of reference genome selection on prediction efficacy. We used six sets of reference genomes, sampled in accordance with phylogenetic diversity and relationship between organisms from 565 bacteria. We used Escherichia coli as a model organism and the gold standard datasets of interacting proteins reported in DIP, EcoCyc and KEGG databases to compare the performance of the prediction methods. Higher performance for predicting protein-protein interactions was achievable even with 100-150 bacterial genomes out of 565 genomes. Inclusion of archaeal genomes in the reference genome set improves performance. We find that in order to obtain a good performance, it is better to sample few genomes of related genera of prokaryotes from the large number of available genomes. Moreover, such a sampling allows for selecting 50-100 genomes for comparable accuracy of predictions when computational resources are limited.

CSI computer system/remote interface unit acceptance test results

NASA Technical Reports Server (NTRS)

Sparks, Dean W., Jr.

1992-01-01

The validation tests conducted on the Control/Structures Interaction (CSI) Computer System (CCS)/Remote Interface Unit (RIU) is discussed. The CCS/RIU consists of a commercially available, Langley Research Center (LaRC) programmed, space flight qualified computer and a flight data acquisition and filtering computer, developed at LaRC. The tests were performed in the Space Structures Research Laboratory (SSRL) and included open loop excitation, closed loop control, safing, RIU digital filtering, and RIU stand alone testing with the CSI Evolutionary Model (CEM) Phase-0 testbed. The test results indicated that the CCS/RIU system is comparable to ground based systems in performing real-time control-structure experiments.

Practical advantages of evolutionary computation

NASA Astrophysics Data System (ADS)

Fogel, David B.

1997-10-01

Evolutionary computation is becoming a common technique for solving difficult, real-world problems in industry, medicine, and defense. This paper reviews some of the practical advantages to using evolutionary algorithms as compared with classic methods of optimization or artificial intelligence. Specific advantages include the flexibility of the procedures, as well as their ability to self-adapt the search for optimum solutions on the fly. As desktop computers increase in speed, the application of evolutionary algorithms will become routine.

RFDT: A Rotation Forest-based Predictor for Predicting Drug-Target Interactions Using Drug Structure and Protein Sequence Information.

PubMed

Wang, Lei; You, Zhu-Hong; Chen, Xing; Yan, Xin; Liu, Gang; Zhang, Wei

2018-01-01

Identification of interaction between drugs and target proteins plays an important role in discovering new drug candidates. However, through the experimental method to identify the drug-target interactions remain to be extremely time-consuming, expensive and challenging even nowadays. Therefore, it is urgent to develop new computational methods to predict potential drugtarget interactions (DTI). In this article, a novel computational model is developed for predicting potential drug-target interactions under the theory that each drug-target interaction pair can be represented by the structural properties from drugs and evolutionary information derived from proteins. Specifically, the protein sequences are encoded as Position-Specific Scoring Matrix (PSSM) descriptor which contains information of biological evolutionary and the drug molecules are encoded as fingerprint feature vector which represents the existence of certain functional groups or fragments. Four benchmark datasets involving enzymes, ion channels, GPCRs and nuclear receptors, are independently used for establishing predictive models with Rotation Forest (RF) model. The proposed method achieved the prediction accuracy of 91.3%, 89.1%, 84.1% and 71.1% for four datasets respectively. In order to make our method more persuasive, we compared our classifier with the state-of-theart Support Vector Machine (SVM) classifier. We also compared the proposed method with other excellent methods. Experimental results demonstrate that the proposed method is effective in the prediction of DTI, and can provide assistance for new drug research and development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Pharmacokinetics and Drug Interactions Determine Optimum Combination Strategies in Computational Models of Cancer Evolution.

PubMed

Chakrabarti, Shaon; Michor, Franziska

2017-07-15

The identification of optimal drug administration schedules to battle the emergence of resistance is a major challenge in cancer research. The existence of a multitude of resistance mechanisms necessitates administering drugs in combination, significantly complicating the endeavor of predicting the evolutionary dynamics of cancers and optimal intervention strategies. A thorough understanding of the important determinants of cancer evolution under combination therapies is therefore crucial for correctly predicting treatment outcomes. Here we developed the first computational strategy to explore pharmacokinetic and drug interaction effects in evolutionary models of cancer progression, a crucial step towards making clinically relevant predictions. We found that incorporating these phenomena into our multiscale stochastic modeling framework significantly changes the optimum drug administration schedules identified, often predicting nonintuitive strategies for combination therapies. We applied our approach to an ongoing phase Ib clinical trial (TATTON) administering AZD9291 and selumetinib to EGFR-mutant lung cancer patients. Our results suggest that the schedules used in the three trial arms have almost identical efficacies, but slight modifications in the dosing frequencies of the two drugs can significantly increase tumor cell eradication. Interestingly, we also predict that drug concentrations lower than the MTD are as efficacious, suggesting that lowering the total amount of drug administered could lower toxicities while not compromising on the effectiveness of the drugs. Our approach highlights the fact that quantitative knowledge of pharmacokinetic, drug interaction, and evolutionary processes is essential for identifying best intervention strategies. Our method is applicable to diverse cancer and treatment types and allows for a rational design of clinical trials. Cancer Res; 77(14); 3908-21. ©2017 AACR . ©2017 American Association for Cancer Research.

A computer lab exploring evolutionary aspects of chromatin structure and dynamics for an undergraduate chromatin course*.

PubMed

Eirín-López, José M

2013-01-01

The study of chromatin constitutes one of the most active research fields in life sciences, being subject to constant revisions that continuously redefine the state of the art in its knowledge. As every other rapidly changing field, chromatin biology requires clear and straightforward educational strategies able to efficiently translate such a vast body of knowledge to the classroom. With this aim, the present work describes a multidisciplinary computer lab designed to introduce undergraduate students to the dynamic nature of chromatin, within the context of the one semester course "Chromatin: Structure, Function and Evolution." This exercise is organized in three parts including (a) molecular evolutionary biology of histone families (using the H1 family as example), (b) histone structure and variation across different animal groups, and (c) effect of histone diversity on nucleosome structure and chromatin dynamics. By using freely available bioinformatic tools that can be run on common computers, the concept of chromatin dynamics is interactively illustrated from a comparative/evolutionary perspective. At the end of this computer lab, students are able to translate the bioinformatic information into a biochemical context in which the relevance of histone primary structure on chromatin dynamics is exposed. During the last 8 years this exercise has proven to be a powerful approach for teaching chromatin structure and dynamics, allowing students a higher degree of independence during the processes of learning and self-assessment. Copyright © 2013 International Union of Biochemistry and Molecular Biology, Inc.

The tangled bank of amino acids.

PubMed

Goldstein, Richard A; Pollock, David D

2016-07-01

The use of amino acid substitution matrices to model protein evolution has yielded important insights into both the evolutionary process and the properties of specific protein families. In order to make these models tractable, standard substitution matrices represent the average results of the evolutionary process rather than the underlying molecular biophysics and population genetics, treating proteins as a set of independently evolving sites rather than as an integrated biomolecular entity. With advances in computing and the increasing availability of sequence data, we now have an opportunity to move beyond current substitution matrices to more interpretable mechanistic models with greater fidelity to the evolutionary process of mutation and selection and the holistic nature of the selective constraints. As part of this endeavour, we consider how epistatic interactions induce spatial and temporal rate heterogeneity, and demonstrate how these generally ignored factors can reconcile standard substitution rate matrices and the underlying biology, allowing us to better understand the meaning of these substitution rates. Using computational simulations of protein evolution, we can demonstrate the importance of both spatial and temporal heterogeneity in modelling protein evolution. © 2016 The Authors Protein Science published by Wiley Periodicals, Inc. on behalf of The Protein Society.

Evolving Digital Ecological Networks

PubMed Central

Wagner, Aaron P.; Ofria, Charles

2013-01-01

“It is hard to realize that the living world as we know it is just one among many possibilities” [1]. Evolving digital ecological networks are webs of interacting, self-replicating, and evolving computer programs (i.e., digital organisms) that experience the same major ecological interactions as biological organisms (e.g., competition, predation, parasitism, and mutualism). Despite being computational, these programs evolve quickly in an open-ended way, and starting from only one or two ancestral organisms, the formation of ecological networks can be observed in real-time by tracking interactions between the constantly evolving organism phenotypes. These phenotypes may be defined by combinations of logical computations (hereafter tasks) that digital organisms perform and by expressed behaviors that have evolved. The types and outcomes of interactions between phenotypes are determined by task overlap for logic-defined phenotypes and by responses to encounters in the case of behavioral phenotypes. Biologists use these evolving networks to study active and fundamental topics within evolutionary ecology (e.g., the extent to which the architecture of multispecies networks shape coevolutionary outcomes, and the processes involved). PMID:23533370

Promoter-enhancer interactions identified from Hi-C data using probabilistic models and hierarchical topological domains.

PubMed

Ron, Gil; Globerson, Yuval; Moran, Dror; Kaplan, Tommy

2017-12-21

Proximity-ligation methods such as Hi-C allow us to map physical DNA-DNA interactions along the genome, and reveal its organization into topologically associating domains (TADs). As the Hi-C data accumulate, computational methods were developed for identifying domain borders in multiple cell types and organisms. Here, we present PSYCHIC, a computational approach for analyzing Hi-C data and identifying promoter-enhancer interactions. We use a unified probabilistic model to segment the genome into domains, which we then merge hierarchically and fit using a local background model, allowing us to identify over-represented DNA-DNA interactions across the genome. By analyzing the published Hi-C data sets in human and mouse, we identify hundreds of thousands of putative enhancers and their target genes, and compile an extensive genome-wide catalog of gene regulation in human and mouse. As we show, our predictions are highly enriched for ChIP-seq and DNA accessibility data, evolutionary conservation, eQTLs and other DNA-DNA interaction data.

Developmental and Evolutionary Lexicon Acquisition in Cognitive Agents/Robots with Grounding Principle: A Short Review.

PubMed

Rasheed, Nadia; Amin, Shamsudin H M

2016-01-01

Grounded language acquisition is an important issue, particularly to facilitate human-robot interactions in an intelligent and effective way. The evolutionary and developmental language acquisition are two innovative and important methodologies for the grounding of language in cognitive agents or robots, the aim of which is to address current limitations in robot design. This paper concentrates on these two main modelling methods with the grounding principle for the acquisition of linguistic ability in cognitive agents or robots. This review not only presents a survey of the methodologies and relevant computational cognitive agents or robotic models, but also highlights the advantages and progress of these approaches for the language grounding issue.

Developmental and Evolutionary Lexicon Acquisition in Cognitive Agents/Robots with Grounding Principle: A Short Review

PubMed Central

Rasheed, Nadia; Amin, Shamsudin H. M.

2016-01-01

Grounded language acquisition is an important issue, particularly to facilitate human-robot interactions in an intelligent and effective way. The evolutionary and developmental language acquisition are two innovative and important methodologies for the grounding of language in cognitive agents or robots, the aim of which is to address current limitations in robot design. This paper concentrates on these two main modelling methods with the grounding principle for the acquisition of linguistic ability in cognitive agents or robots. This review not only presents a survey of the methodologies and relevant computational cognitive agents or robotic models, but also highlights the advantages and progress of these approaches for the language grounding issue. PMID:27069470

Design Mining Interacting Wind Turbines.

PubMed

Preen, Richard J; Bull, Larry

2016-01-01

An initial study has recently been presented of surrogate-assisted evolutionary algorithms used to design vertical-axis wind turbines wherein candidate prototypes are evaluated under fan-generated wind conditions after being physically instantiated by a 3D printer. Unlike other approaches, such as computational fluid dynamics simulations, no mathematical formulations were used and no model assumptions were made. This paper extends that work by exploring alternative surrogate modelling and evolutionary techniques. The accuracy of various modelling algorithms used to estimate the fitness of evaluated individuals from the initial experiments is compared. The effect of temporally windowing surrogate model training samples is explored. A surrogate-assisted approach based on an enhanced local search is introduced; and alternative coevolution collaboration schemes are examined.

Computing the origin and evolution of the ribosome from its structure — Uncovering processes of macromolecular accretion benefiting synthetic biology

PubMed Central

Caetano-Anollés, Gustavo; Caetano-Anollés, Derek

2015-01-01

Accretion occurs pervasively in nature at widely different timeframes. The process also manifests in the evolution of macromolecules. Here we review recent computational and structural biology studies of evolutionary accretion that make use of the ideographic (historical, retrodictive) and nomothetic (universal, predictive) scientific frameworks. Computational studies uncover explicit timelines of accretion of structural parts in molecular repertoires and molecules. Phylogenetic trees of protein structural domains and proteomes and their molecular functions were built from a genomic census of millions of encoded proteins and associated terminal Gene Ontology terms. Trees reveal a ‘metabolic-first’ origin of proteins, the late development of translation, and a patchwork distribution of proteins in biological networks mediated by molecular recruitment. Similarly, the natural history of ancient RNA molecules inferred from trees of molecular substructures built from a census of molecular features shows patchwork-like accretion patterns. Ideographic analyses of ribosomal history uncover the early appearance of structures supporting mRNA decoding and tRNA translocation, the coevolution of ribosomal proteins and RNA, and a first evolutionary transition that brings ribosomal subunits together into a processive protein biosynthetic complex. Nomothetic structural biology studies of tertiary interactions and ancient insertions in rRNA complement these findings, once concentric layering assumptions are removed. Patterns of coaxial helical stacking reveal a frustrated dynamics of outward and inward ribosomal growth possibly mediated by structural grafting. The early rise of the ribosomal ‘turnstile’ suggests an evolutionary transition in natural biological computation. Results make explicit the need to understand processes of molecular growth and information transfer of macromolecules. PMID:27096056

Engaging Undergraduate Math Majors in Geoscience Research using Interactive Simulations and Computer Art

NASA Astrophysics Data System (ADS)

Matott, L. S.; Hymiak, B.; Reslink, C. F.; Baxter, C.; Aziz, S.

2012-12-01

As part of the NSF-sponsored 'URGE (Undergraduate Research Group Experiences) to Compute' program, Dr. Matott has been collaborating with talented Math majors to explore the design of cost-effective systems to safeguard groundwater supplies from contaminated sites. Such activity is aided by a combination of groundwater modeling, simulation-based optimization, and high-performance computing - disciplines largely unfamiliar to the students at the outset of the program. To help train and engage the students, a number of interactive and graphical software packages were utilized. Examples include: (1) a tutorial for exploring the behavior of evolutionary algorithms and other heuristic optimizers commonly used in simulation-based optimization; (2) an interactive groundwater modeling package for exploring alternative pump-and-treat containment scenarios at a contaminated site in Billings, Montana; (3) the R software package for visualizing various concepts related to subsurface hydrology; and (4) a job visualization tool for exploring the behavior of numerical experiments run on a large distributed computing cluster. Further engagement and excitement in the program was fostered by entering (and winning) a computer art competition run by the Coalition for Academic Scientific Computation (CASC). The winning submission visualizes an exhaustively mapped optimization cost surface and dramatically illustrates the phenomena of artificial minima - valley locations that correspond to designs whose costs are only partially optimal.

Analysis and application of opinion model with multiple topic interactions.

PubMed

Xiong, Fei; Liu, Yun; Wang, Liang; Wang, Ximeng

2017-08-01

To reveal heterogeneous behaviors of opinion evolution in different scenarios, we propose an opinion model with topic interactions. Individual opinions and topic features are represented by a multidimensional vector. We measure an agent's action towards a specific topic by the product of opinion and topic feature. When pairs of agents interact for a topic, their actions are introduced to opinion updates with bounded confidence. Simulation results show that a transition from a disordered state to a consensus state occurs at a critical point of the tolerance threshold, which depends on the opinion dimension. The critical point increases as the dimension of opinions increases. Multiple topics promote opinion interactions and lead to the formation of macroscopic opinion clusters. In addition, more topics accelerate the evolutionary process and weaken the effect of network topology. We use two sets of large-scale real data to evaluate the model, and the results prove its effectiveness in characterizing a real evolutionary process. Our model achieves high performance in individual action prediction and even outperforms state-of-the-art methods. Meanwhile, our model has much smaller computational complexity. This paper provides a demonstration for possible practical applications of theoretical opinion dynamics.

Upon Accounting for the Impact of Isoenzyme Loss, Gene Deletion Costs Anticorrelate with Their Evolutionary Rates.

PubMed

Jacobs, Christopher; Lambourne, Luke; Xia, Yu; Segrè, Daniel

2017-01-01

System-level metabolic network models enable the computation of growth and metabolic phenotypes from an organism's genome. In particular, flux balance approaches have been used to estimate the contribution of individual metabolic genes to organismal fitness, offering the opportunity to test whether such contributions carry information about the evolutionary pressure on the corresponding genes. Previous failure to identify the expected negative correlation between such computed gene-loss cost and sequence-derived evolutionary rates in Saccharomyces cerevisiae has been ascribed to a real biological gap between a gene's fitness contribution to an organism "here and now" and the same gene's historical importance as evidenced by its accumulated mutations over millions of years of evolution. Here we show that this negative correlation does exist, and can be exposed by revisiting a broadly employed assumption of flux balance models. In particular, we introduce a new metric that we call "function-loss cost", which estimates the cost of a gene loss event as the total potential functional impairment caused by that loss. This new metric displays significant negative correlation with evolutionary rate, across several thousand minimal environments. We demonstrate that the improvement gained using function-loss cost over gene-loss cost is explained by replacing the base assumption that isoenzymes provide unlimited capacity for backup with the assumption that isoenzymes are completely non-redundant. We further show that this change of the assumption regarding isoenzymes increases the recall of epistatic interactions predicted by the flux balance model at the cost of a reduction in the precision of the predictions. In addition to suggesting that the gene-to-reaction mapping in genome-scale flux balance models should be used with caution, our analysis provides new evidence that evolutionary gene importance captures much more than strict essentiality.

Chlorophyll-Derivative Modulation of Rhodopsin Signaling Properties through Evolutionarily Conserved Interaction Pathways

PubMed Central

Woods, Kristina N.; Pfeffer, Jürgen; Klein-Seetharaman, Judith

2017-01-01

Retinal is the light-absorbing chromophore that is responsible for the activation of visual pigments and light-driven ion pumps. Evolutionary changes in the intermolecular interactions of the retinal with specific amino acids allow for adaptation of the spectral characteristics, referred to as spectral tuning. However, it has been proposed that a specific species of dragon fish has bypassed the adaptive evolutionary process of spectral tuning and replaced it with a single evolutionary event: photosensitization of rhodopsin by chlorophyll derivatives. Here, by using a combination of experimental measurements and computational modeling to probe retinal-receptor interactions in rhodopsin, we show how the binding of the chlorophyll derivative, chlorin-e6 (Ce6) in the intracellular domain (ICD) of the receptor allosterically excites G-protein coupled receptor class A (GPCR-A) conserved long-range correlated fluctuations that connect distant parts of the receptor. These long-range correlated motions are associated with regulating the dynamics and intermolecular interactions of specific amino acids in the retinal ligand-binding pocket that have been associated with shifts in the absorbance peak maximum (λmax) and hence, spectral sensitivity of the visual system. Moreover, the binding of Ce6 affects the overall global properties of the receptor. Specifically, we find that Ce6-induced dynamics alter the thermal stability of rhodopsin by adjusting hydrogen-bonding interactions near the receptor active-site that consequently also influences the intrinsic conformational equilibrium of the receptor. Due to the conservation of the ICD residues amongst different receptors in this class and the fact that all GPCR-A receptors share a common mechanism of activation, it is possible that the allosteric associations excited in rhodopsin with Ce6 binding are a common feature in all class A GPCRs. PMID:29312953

Advances in computer simulation of genome evolution: toward more realistic evolutionary genomics analysis by approximate bayesian computation.

PubMed

Arenas, Miguel

2015-04-01

NGS technologies present a fast and cheap generation of genomic data. Nevertheless, ancestral genome inference is not so straightforward due to complex evolutionary processes acting on this material such as inversions, translocations, and other genome rearrangements that, in addition to their implicit complexity, can co-occur and confound ancestral inferences. Recently, models of genome evolution that accommodate such complex genomic events are emerging. This letter explores these novel evolutionary models and proposes their incorporation into robust statistical approaches based on computer simulations, such as approximate Bayesian computation, that may produce a more realistic evolutionary analysis of genomic data. Advantages and pitfalls in using these analytical methods are discussed. Potential applications of these ancestral genomic inferences are also pointed out.

Communication scheme based on evolutionary spatial 2×2 games

NASA Astrophysics Data System (ADS)

Ziaukas, Pranas; Ragulskis, Tautvydas; Ragulskis, Minvydas

2014-06-01

A visual communication scheme based on evolutionary spatial 2×2 games is proposed in this paper. Self-organizing patterns induced by complex interactions between competing individuals are exploited for hiding and transmitting secret visual information. Properties of the proposed communication scheme are discussed in details. It is shown that the hiding capacity of the system (the minimum size of the detectable primitives and the minimum distance between two primitives) is sufficient for the effective transmission of digital dichotomous images. Also, it is demonstrated that the proposed communication scheme is resilient to time backwards, plain image attacks and is highly sensitive to perturbations of private and public keys. Several computational experiments are used to demonstrate the effectiveness of the proposed communication scheme.

Covariant Evolutionary Event Analysis for Base Interaction Prediction Using a Relational Database Management System for RNA.

PubMed

Xu, Weijia; Ozer, Stuart; Gutell, Robin R

2009-01-01

With an increasingly large amount of sequences properly aligned, comparative sequence analysis can accurately identify not only common structures formed by standard base pairing but also new types of structural elements and constraints. However, traditional methods are too computationally expensive to perform well on large scale alignment and less effective with the sequences from diversified phylogenetic classifications. We propose a new approach that utilizes coevolutional rates among pairs of nucleotide positions using phylogenetic and evolutionary relationships of the organisms of aligned sequences. With a novel data schema to manage relevant information within a relational database, our method, implemented with a Microsoft SQL Server 2005, showed 90% sensitivity in identifying base pair interactions among 16S ribosomal RNA sequences from Bacteria, at a scale 40 times bigger and 50% better sensitivity than a previous study. The results also indicated covariation signals for a few sets of cross-strand base stacking pairs in secondary structure helices, and other subtle constraints in the RNA structure.

Covariant Evolutionary Event Analysis for Base Interaction Prediction Using a Relational Database Management System for RNA

PubMed Central

Xu, Weijia; Ozer, Stuart; Gutell, Robin R.

2010-01-01

With an increasingly large amount of sequences properly aligned, comparative sequence analysis can accurately identify not only common structures formed by standard base pairing but also new types of structural elements and constraints. However, traditional methods are too computationally expensive to perform well on large scale alignment and less effective with the sequences from diversified phylogenetic classifications. We propose a new approach that utilizes coevolutional rates among pairs of nucleotide positions using phylogenetic and evolutionary relationships of the organisms of aligned sequences. With a novel data schema to manage relevant information within a relational database, our method, implemented with a Microsoft SQL Server 2005, showed 90% sensitivity in identifying base pair interactions among 16S ribosomal RNA sequences from Bacteria, at a scale 40 times bigger and 50% better sensitivity than a previous study. The results also indicated covariation signals for a few sets of cross-strand base stacking pairs in secondary structure helices, and other subtle constraints in the RNA structure. PMID:20502534

Co-evolutionary Analysis of Domains in Interacting Proteins Reveals Insights into Domain–Domain Interactions Mediating Protein–Protein Interactions

PubMed Central

Jothi, Raja; Cherukuri, Praveen F.; Tasneem, Asba; Przytycka, Teresa M.

2006-01-01

Recent advances in functional genomics have helped generate large-scale high-throughput protein interaction data. Such networks, though extremely valuable towards molecular level understanding of cells, do not provide any direct information about the regions (domains) in the proteins that mediate the interaction. Here, we performed co-evolutionary analysis of domains in interacting proteins in order to understand the degree of co-evolution of interacting and non-interacting domains. Using a combination of sequence and structural analysis, we analyzed protein–protein interactions in F1-ATPase, Sec23p/Sec24p, DNA-directed RNA polymerase and nuclear pore complexes, and found that interacting domain pair(s) for a given interaction exhibits higher level of co-evolution than the noninteracting domain pairs. Motivated by this finding, we developed a computational method to test the generality of the observed trend, and to predict large-scale domain–domain interactions. Given a protein–protein interaction, the proposed method predicts the domain pair(s) that is most likely to mediate the protein interaction. We applied this method on the yeast interactome to predict domain–domain interactions, and used known domain–domain interactions found in PDB crystal structures to validate our predictions. Our results show that the prediction accuracy of the proposed method is statistically significant. Comparison of our prediction results with those from two other methods reveals that only a fraction of predictions are shared by all the three methods, indicating that the proposed method can detect known interactions missed by other methods. We believe that the proposed method can be used with other methods to help identify previously unrecognized domain–domain interactions on a genome scale, and could potentially help reduce the search space for identifying interaction sites. PMID:16949097

Evolutionary computation in zoology and ecology.

PubMed

Boone, Randall B

2017-12-01

Evolutionary computational methods have adopted attributes of natural selection and evolution to solve problems in computer science, engineering, and other fields. The method is growing in use in zoology and ecology. Evolutionary principles may be merged with an agent-based modeling perspective to have individual animals or other agents compete. Four main categories are discussed: genetic algorithms, evolutionary programming, genetic programming, and evolutionary strategies. In evolutionary computation, a population is represented in a way that allows for an objective function to be assessed that is relevant to the problem of interest. The poorest performing members are removed from the population, and remaining members reproduce and may be mutated. The fitness of the members is again assessed, and the cycle continues until a stopping condition is met. Case studies include optimizing: egg shape given different clutch sizes, mate selection, migration of wildebeest, birds, and elk, vulture foraging behavior, algal bloom prediction, and species richness given energy constraints. Other case studies simulate the evolution of species and a means to project shifts in species ranges in response to a changing climate that includes competition and phenotypic plasticity. This introduction concludes by citing other uses of evolutionary computation and a review of the flexibility of the methods. For example, representing species' niche spaces subject to selective pressure allows studies on cladistics, the taxon cycle, neutral versus niche paradigms, fundamental versus realized niches, community structure and order of colonization, invasiveness, and responses to a changing climate.

Evolutionary computation in zoology and ecology

PubMed Central

2017-01-01

Abstract Evolutionary computational methods have adopted attributes of natural selection and evolution to solve problems in computer science, engineering, and other fields. The method is growing in use in zoology and ecology. Evolutionary principles may be merged with an agent-based modeling perspective to have individual animals or other agents compete. Four main categories are discussed: genetic algorithms, evolutionary programming, genetic programming, and evolutionary strategies. In evolutionary computation, a population is represented in a way that allows for an objective function to be assessed that is relevant to the problem of interest. The poorest performing members are removed from the population, and remaining members reproduce and may be mutated. The fitness of the members is again assessed, and the cycle continues until a stopping condition is met. Case studies include optimizing: egg shape given different clutch sizes, mate selection, migration of wildebeest, birds, and elk, vulture foraging behavior, algal bloom prediction, and species richness given energy constraints. Other case studies simulate the evolution of species and a means to project shifts in species ranges in response to a changing climate that includes competition and phenotypic plasticity. This introduction concludes by citing other uses of evolutionary computation and a review of the flexibility of the methods. For example, representing species’ niche spaces subject to selective pressure allows studies on cladistics, the taxon cycle, neutral versus niche paradigms, fundamental versus realized niches, community structure and order of colonization, invasiveness, and responses to a changing climate. PMID:29492029

From computers to cultivation: reconceptualizing evolutionary psychology.

PubMed

Barrett, Louise; Pollet, Thomas V; Stulp, Gert

2014-01-01

Does evolutionary theorizing have a role in psychology? This is a more contentious issue than one might imagine, given that, as evolved creatures, the answer must surely be yes. The contested nature of evolutionary psychology lies not in our status as evolved beings, but in the extent to which evolutionary ideas add value to studies of human behavior, and the rigor with which these ideas are tested. This, in turn, is linked to the framework in which particular evolutionary ideas are situated. While the framing of the current research topic places the brain-as-computer metaphor in opposition to evolutionary psychology, the most prominent school of thought in this field (born out of cognitive psychology, and often known as the Santa Barbara school) is entirely wedded to the computational theory of mind as an explanatory framework. Its unique aspect is to argue that the mind consists of a large number of functionally specialized (i.e., domain-specific) computational mechanisms, or modules (the massive modularity hypothesis). Far from offering an alternative to, or an improvement on, the current perspective, we argue that evolutionary psychology is a mainstream computational theory, and that its arguments for domain-specificity often rest on shaky premises. We then go on to suggest that the various forms of e-cognition (i.e., embodied, embedded, enactive) represent a true alternative to standard computational approaches, with an emphasis on "cognitive integration" or the "extended mind hypothesis" in particular. We feel this offers the most promise for human psychology because it incorporates the social and historical processes that are crucial to human "mind-making" within an evolutionarily informed framework. In addition to linking to other research areas in psychology, this approach is more likely to form productive links to other disciplines within the social sciences, not least by encouraging a healthy pluralism in approach.

Evolutionary dynamics on graphs: Efficient method for weak selection

NASA Astrophysics Data System (ADS)

Fu, Feng; Wang, Long; Nowak, Martin A.; Hauert, Christoph

2009-04-01

Investigating the evolutionary dynamics of game theoretical interactions in populations where individuals are arranged on a graph can be challenging in terms of computation time. Here, we propose an efficient method to study any type of game on arbitrary graph structures for weak selection. In this limit, evolutionary game dynamics represents a first-order correction to neutral evolution. Spatial correlations can be empirically determined under neutral evolution and provide the basis for formulating the game dynamics as a discrete Markov process by incorporating a detailed description of the microscopic dynamics based on the neutral correlations. This framework is then applied to one of the most intriguing questions in evolutionary biology: the evolution of cooperation. We demonstrate that the degree heterogeneity of a graph impedes cooperation and that the success of tit for tat depends not only on the number of rounds but also on the degree of the graph. Moreover, considering the mutation-selection equilibrium shows that the symmetry of the stationary distribution of states under weak selection is skewed in favor of defectors for larger selection strengths. In particular, degree heterogeneity—a prominent feature of scale-free networks—generally results in a more pronounced increase in the critical benefit-to-cost ratio required for evolution to favor cooperation as compared to regular graphs. This conclusion is corroborated by an analysis of the effects of population structures on the fixation probabilities of strategies in general 2×2 games for different types of graphs. Computer simulations confirm the predictive power of our method and illustrate the improved accuracy as compared to previous studies.

Feature Selection in Classification of Eye Movements Using Electrooculography for Activity Recognition

PubMed Central

Mala, S.; Latha, K.

2014-01-01

Activity recognition is needed in different requisition, for example, reconnaissance system, patient monitoring, and human-computer interfaces. Feature selection plays an important role in activity recognition, data mining, and machine learning. In selecting subset of features, an efficient evolutionary algorithm Differential Evolution (DE), a very efficient optimizer, is used for finding informative features from eye movements using electrooculography (EOG). Many researchers use EOG signals in human-computer interactions with various computational intelligence methods to analyze eye movements. The proposed system involves analysis of EOG signals using clearness based features, minimum redundancy maximum relevance features, and Differential Evolution based features. This work concentrates more on the feature selection algorithm based on DE in order to improve the classification for faultless activity recognition. PMID:25574185

Feature selection in classification of eye movements using electrooculography for activity recognition.

PubMed

Mala, S; Latha, K

2014-01-01

Activity recognition is needed in different requisition, for example, reconnaissance system, patient monitoring, and human-computer interfaces. Feature selection plays an important role in activity recognition, data mining, and machine learning. In selecting subset of features, an efficient evolutionary algorithm Differential Evolution (DE), a very efficient optimizer, is used for finding informative features from eye movements using electrooculography (EOG). Many researchers use EOG signals in human-computer interactions with various computational intelligence methods to analyze eye movements. The proposed system involves analysis of EOG signals using clearness based features, minimum redundancy maximum relevance features, and Differential Evolution based features. This work concentrates more on the feature selection algorithm based on DE in order to improve the classification for faultless activity recognition.

Evolution of dispersal and life history interact to drive accelerating spread of an invasive species.

PubMed

Perkins, T Alex; Phillips, Benjamin L; Baskett, Marissa L; Hastings, Alan

2013-08-01

Populations on the edge of an expanding range are subject to unique evolutionary pressures acting on their life-history and dispersal traits. Empirical evidence and theory suggest that traits there can evolve rapidly enough to interact with ecological dynamics, potentially giving rise to accelerating spread. Nevertheless, which of several evolutionary mechanisms drive this interaction between evolution and spread remains an open question. We propose an integrated theoretical framework for partitioning the contributions of different evolutionary mechanisms to accelerating spread, and we apply this model to invasive cane toads in northern Australia. In doing so, we identify a previously unrecognised evolutionary process that involves an interaction between life-history and dispersal evolution during range shift. In roughly equal parts, life-history evolution, dispersal evolution and their interaction led to a doubling of distance spread by cane toads in our model, highlighting the potential importance of multiple evolutionary processes in the dynamics of range expansion. © 2013 John Wiley & Sons Ltd/CNRS.

On the preservation of cooperation in two-strategy games with nonlocal interactions.

PubMed

Aydogmus, Ozgur; Zhou, Wen; Kang, Yun

2017-03-01

Nonlocal interactions such as spatial interaction are ubiquitous in nature and may alter the equilibrium in evolutionary dynamics. Models including nonlocal spatial interactions can provide a further understanding on the preservation and emergence of cooperation in evolutionary dynamics. In this paper, we consider a variety of two-strategy evolutionary spatial games with nonlocal interactions based on an integro-differential replicator equation. By defining the invasion speed and minimal traveling wave speed for the derived model, we study the effects of the payoffs, the selection pressure and the spatial parameter on the preservation of cooperation. One of our most interesting findings is that, for the Prisoners Dilemma games in which the defection is the only evolutionary stable strategy for unstructured populations, analyses on its asymptotic speed of propagation suggest that, in contrast with spatially homogeneous games, the cooperators can invade the habitat under proper conditions. Other two-strategy evolutionary spatial games are also explored. Both our theoretical and numerical studies show that the nonlocal spatial interaction favors diversity in strategies in a population and is able to preserve cooperation in a competing environment. A real data application in a virus mutation study echoes our theoretical observations. In addition, we compare the results of our model to the partial differential equation approach to demonstrate the importance of including non-local interaction component in evolutionary game models. Copyright © 2016 Elsevier Inc. All rights reserved.

Evolutionary dynamics of group interactions on structured populations: a review

PubMed Central

Perc, Matjaž; Gómez-Gardeñes, Jesús; Szolnoki, Attila; Floría, Luis M.; Moreno, Yamir

2013-01-01

Interactions among living organisms, from bacteria colonies to human societies, are inherently more complex than interactions among particles and non-living matter. Group interactions are a particularly important and widespread class, representative of which is the public goods game. In addition, methods of statistical physics have proved valuable for studying pattern formation, equilibrium selection and self-organization in evolutionary games. Here, we review recent advances in the study of evolutionary dynamics of group interactions on top of structured populations, including lattices, complex networks and coevolutionary models. We also compare these results with those obtained on well-mixed populations. The review particularly highlights that the study of the dynamics of group interactions, like several other important equilibrium and non-equilibrium dynamical processes in biological, economical and social sciences, benefits from the synergy between statistical physics, network science and evolutionary game theory. PMID:23303223

Datamonkey 2.0: a modern web application for characterizing selective and other evolutionary processes.

PubMed

Weaver, Steven; Shank, Stephen D; Spielman, Stephanie J; Li, Michael; Muse, Spencer V; Kosakovsky Pond, Sergei L

2018-01-02

Inference of how evolutionary forces have shaped extant genetic diversity is a cornerstone of modern comparative sequence analysis. Advances in sequence generation and increased statistical sophistication of relevant methods now allow researchers to extract ever more evolutionary signal from the data, albeit at an increased computational cost. Here, we announce the release of Datamonkey 2.0, a completely re-engineered version of the Datamonkey web-server for analyzing evolutionary signatures in sequence data. For this endeavor, we leveraged recent developments in open-source libraries that facilitate interactive, robust, and scalable web application development. Datamonkey 2.0 provides a carefully curated collection of methods for interrogating coding-sequence alignments for imprints of natural selection, packaged as a responsive (i.e. can be viewed on tablet and mobile devices), fully interactive, and API-enabled web application. To complement Datamonkey 2.0, we additionally release HyPhy Vision, an accompanying JavaScript application for visualizing analysis results. HyPhy Vision can also be used separately from Datamonkey 2.0 to visualize locally-executed HyPhy analyses. Together, Datamonkey 2.0 and HyPhy Vision showcase how scientific software development can benefit from general-purpose open-source frameworks. Datamonkey 2.0 is freely and publicly available at http://www.datamonkey. org, and the underlying codebase is available from https://github.com/veg/datamonkey-js. © The Author 2018. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

A conservation and biophysics guided stochastic approach to refining docked multimeric proteins.

PubMed

Akbal-Delibas, Bahar; Haspel, Nurit

2013-01-01

We introduce a protein docking refinement method that accepts complexes consisting of any number of monomeric units. The method uses a scoring function based on a tight coupling between evolutionary conservation, geometry and physico-chemical interactions. Understanding the role of protein complexes in the basic biology of organisms heavily relies on the detection of protein complexes and their structures. Different computational docking methods are developed for this purpose, however, these methods are often not accurate and their results need to be further refined to improve the geometry and the energy of the resulting complexes. Also, despite the fact that complexes in nature often have more than two monomers, most docking methods focus on dimers since the computational complexity increases exponentially due to the addition of monomeric units. Our results show that the refinement scheme can efficiently handle complexes with more than two monomers by biasing the results towards complexes with native interactions, filtering out false positive results. Our refined complexes have better IRMSDs with respect to the known complexes and lower energies than those initial docked structures. Evolutionary conservation information allows us to bias our results towards possible functional interfaces, and the probabilistic selection scheme helps us to escape local energy minima. We aim to incorporate our refinement method in a larger framework which also enables docking of multimeric complexes given only monomeric structures.

The beta-diversity of species interactions: Untangling the drivers of geographic variation in plant-pollinator diversity and function across scales.

PubMed

Burkle, Laura A; Myers, Jonathan A; Belote, R Travis

2016-01-01

Geographic patterns of biodiversity have long inspired interest in processes that shape the assembly, diversity, and dynamics of communities at different spatial scales. To study mechanisms of community assembly, ecologists often compare spatial variation in community composition (beta-diversity) across environmental and spatial gradients. These same patterns inspired evolutionary biologists to investigate how micro- and macro-evolutionary processes create gradients in biodiversity. Central to these perspectives are species interactions, which contribute to community assembly and geographic variation in evolutionary processes. However, studies of beta-diversity have predominantly focused on single trophic levels, resulting in gaps in our understanding of variation in species-interaction networks (interaction beta-diversity), especially at scales most relevant to evolutionary studies of geographic variation. We outline two challenges and their consequences in scaling-up studies of interaction beta-diversity from local to biogeographic scales using plant-pollinator interactions as a model system in ecology, evolution, and conservation. First, we highlight how variation in regional species pools may contribute to variation in interaction beta-diversity among biogeographic regions with dissimilar evolutionary history. Second, we highlight how pollinator behavior (host-switching) links ecological networks to geographic patterns of plant-pollinator interactions and evolutionary processes. Third, we outline key unanswered questions regarding the role of geographic variation in plant-pollinator interactions for conservation and ecosystem services (pollination) in changing environments. We conclude that the largest advances in the burgeoning field of interaction beta-diversity will come from studies that integrate frameworks in ecology, evolution, and conservation to understand the causes and consequences of interaction beta-diversity across scales. © 2016 Botanical Society of America.

Evolutionary Games of Multiplayer Cooperation on Graphs

PubMed Central

Arranz, Jordi; Traulsen, Arne

2016-01-01

There has been much interest in studying evolutionary games in structured populations, often modeled as graphs. However, most analytical results so far have only been obtained for two-player or linear games, while the study of more complex multiplayer games has been usually tackled by computer simulations. Here we investigate evolutionary multiplayer games on graphs updated with a Moran death-Birth process. For cycles, we obtain an exact analytical condition for cooperation to be favored by natural selection, given in terms of the payoffs of the game and a set of structure coefficients. For regular graphs of degree three and larger, we estimate this condition using a combination of pair approximation and diffusion approximation. For a large class of cooperation games, our approximations suggest that graph-structured populations are stronger promoters of cooperation than populations lacking spatial structure. Computer simulations validate our analytical approximations for random regular graphs and cycles, but show systematic differences for graphs with many loops such as lattices. In particular, our simulation results show that these kinds of graphs can even lead to more stringent conditions for the evolution of cooperation than well-mixed populations. Overall, we provide evidence suggesting that the complexity arising from many-player interactions and spatial structure can be captured by pair approximation in the case of random graphs, but that it need to be handled with care for graphs with high clustering. PMID:27513946

From computers to cultivation: reconceptualizing evolutionary psychology

PubMed Central

Barrett, Louise; Pollet, Thomas V.; Stulp, Gert

2014-01-01

Does evolutionary theorizing have a role in psychology? This is a more contentious issue than one might imagine, given that, as evolved creatures, the answer must surely be yes. The contested nature of evolutionary psychology lies not in our status as evolved beings, but in the extent to which evolutionary ideas add value to studies of human behavior, and the rigor with which these ideas are tested. This, in turn, is linked to the framework in which particular evolutionary ideas are situated. While the framing of the current research topic places the brain-as-computer metaphor in opposition to evolutionary psychology, the most prominent school of thought in this field (born out of cognitive psychology, and often known as the Santa Barbara school) is entirely wedded to the computational theory of mind as an explanatory framework. Its unique aspect is to argue that the mind consists of a large number of functionally specialized (i.e., domain-specific) computational mechanisms, or modules (the massive modularity hypothesis). Far from offering an alternative to, or an improvement on, the current perspective, we argue that evolutionary psychology is a mainstream computational theory, and that its arguments for domain-specificity often rest on shaky premises. We then go on to suggest that the various forms of e-cognition (i.e., embodied, embedded, enactive) represent a true alternative to standard computational approaches, with an emphasis on “cognitive integration” or the “extended mind hypothesis” in particular. We feel this offers the most promise for human psychology because it incorporates the social and historical processes that are crucial to human “mind-making” within an evolutionarily informed framework. In addition to linking to other research areas in psychology, this approach is more likely to form productive links to other disciplines within the social sciences, not least by encouraging a healthy pluralism in approach. PMID:25161633

Keys and the crisis in taxonomy: extinction or reinvention?

PubMed

Walter, David Evans; Winterton, Shaun

2007-01-01

Dichotomous keys that follow a single pathway of character state choices to an end point have been the primary tools for the identification of unknown organisms for more than two centuries. However, a revolution in computer diagnostics is now under way that may result in the replacement of traditional keys by matrix-based computer interactive keys that have many paths to a correct identification and make extensive use of hypertext to link to images, glossaries, and other support material. Progress is also being made on replacing keys entirely by optical matching of specimens to digital databases and DNA sequences. These new tools may go some way toward alleviating the taxonomic impediment to biodiversity studies and other ecological and evolutionary research, especially with better coordination between those who produce keys and those who use them and by integrating interactive keys into larger biological Web sites.

Interactive Computer-Enhanced Remote Viewing System (ICERVS): Final report, November 1994--September 1996

DOE Office of Scientific and Technical Information (OSTI.GOV)

NONE

1997-05-01

The Interactive Computer-Enhanced Remote Viewing System (ICERVS) is a software tool for complex three-dimensional (3-D) visualization and modeling. Its primary purpose is to facilitate the use of robotic and telerobotic systems in remote and/or hazardous environments, where spatial information is provided by 3-D mapping sensors. ICERVS provides a robust, interactive system for viewing sensor data in 3-D and combines this with interactive geometric modeling capabilities that allow an operator to construct CAD models to match the remote environment. Part I of this report traces the development of ICERVS through three evolutionary phases: (1) development of first-generation software to render orthogonalmore » view displays and wireframe models; (2) expansion of this software to include interactive viewpoint control, surface-shaded graphics, material (scalar and nonscalar) property data, cut/slice planes, color and visibility mapping, and generalized object models; (3) demonstration of ICERVS as a tool for the remediation of underground storage tanks (USTs) and the dismantlement of contaminated processing facilities. Part II of this report details the software design of ICERVS, with particular emphasis on its object-oriented architecture and user interface.« less

A sense of life: computational and experimental investigations with models of biochemical and evolutionary processes.

PubMed

Mishra, Bud; Daruwala, Raoul-Sam; Zhou, Yi; Ugel, Nadia; Policriti, Alberto; Antoniotti, Marco; Paxia, Salvatore; Rejali, Marc; Rudra, Archisman; Cherepinsky, Vera; Silver, Naomi; Casey, William; Piazza, Carla; Simeoni, Marta; Barbano, Paolo; Spivak, Marina; Feng, Jiawu; Gill, Ofer; Venkatesh, Mysore; Cheng, Fang; Sun, Bing; Ioniata, Iuliana; Anantharaman, Thomas; Hubbard, E Jane Albert; Pnueli, Amir; Harel, David; Chandru, Vijay; Hariharan, Ramesh; Wigler, Michael; Park, Frank; Lin, Shih-Chieh; Lazebnik, Yuri; Winkler, Franz; Cantor, Charles R; Carbone, Alessandra; Gromov, Mikhael

2003-01-01

We collaborate in a research program aimed at creating a rigorous framework, experimental infrastructure, and computational environment for understanding, experimenting with, manipulating, and modifying a diverse set of fundamental biological processes at multiple scales and spatio-temporal modes. The novelty of our research is based on an approach that (i) requires coevolution of experimental science and theoretical techniques and (ii) exploits a certain universality in biology guided by a parsimonious model of evolutionary mechanisms operating at the genomic level and manifesting at the proteomic, transcriptomic, phylogenic, and other higher levels. Our current program in "systems biology" endeavors to marry large-scale biological experiments with the tools to ponder and reason about large, complex, and subtle natural systems. To achieve this ambitious goal, ideas and concepts are combined from many different fields: biological experimentation, applied mathematical modeling, computational reasoning schemes, and large-scale numerical and symbolic simulations. From a biological viewpoint, the basic issues are many: (i) understanding common and shared structural motifs among biological processes; (ii) modeling biological noise due to interactions among a small number of key molecules or loss of synchrony; (iii) explaining the robustness of these systems in spite of such noise; and (iv) cataloging multistatic behavior and adaptation exhibited by many biological processes.

Kernel Method Based Human Model for Enhancing Interactive Evolutionary Optimization

PubMed Central

Zhao, Qiangfu; Liu, Yong

2015-01-01

A fitness landscape presents the relationship between individual and its reproductive success in evolutionary computation (EC). However, discrete and approximate landscape in an original search space may not support enough and accurate information for EC search, especially in interactive EC (IEC). The fitness landscape of human subjective evaluation in IEC is very difficult and impossible to model, even with a hypothesis of what its definition might be. In this paper, we propose a method to establish a human model in projected high dimensional search space by kernel classification for enhancing IEC search. Because bivalent logic is a simplest perceptual paradigm, the human model is established by considering this paradigm principle. In feature space, we design a linear classifier as a human model to obtain user preference knowledge, which cannot be supported linearly in original discrete search space. The human model is established by this method for predicting potential perceptual knowledge of human. With the human model, we design an evolution control method to enhance IEC search. From experimental evaluation results with a pseudo-IEC user, our proposed model and method can enhance IEC search significantly. PMID:25879050

Upon accounting for the impact of isoenzyme loss, gene deletion costs anticorrelate with their evolutionary rates

DOE PAGES

Jacobs, Christopher; Lambourne, Luke; Xia, Yu; ...

2017-01-20

Here, system-level metabolic network models enable the computation of growth and metabolic phenotypes from an organism's genome. In particular, flux balance approaches have been used to estimate the contribution of individual metabolic genes to organismal fitness, offering the opportunity to test whether such contributions carry information about the evolutionary pressure on the corresponding genes. Previous failure to identify the expected negative correlation between such computed gene-loss cost and sequence-derived evolutionary rates in Saccharomyces cerevisiae has been ascribed to a real biological gap between a gene's fitness contribution to an organism "here and now"º and the same gene's historical importance asmore » evidenced by its accumulated mutations over millions of years of evolution. Here we show that this negative correlation does exist, and can be exposed by revisiting a broadly employed assumption of flux balance models. In particular, we introduce a new metric that we call "function-loss cost", which estimates the cost of a gene loss event as the total potential functional impairment caused by that loss. This new metric displays significant negative correlation with evolutionary rate, across several thousand minimal environments. We demonstrate that the improvement gained using function-loss cost over gene-loss cost is explained by replacing the base assumption that isoenzymes provide unlimited capacity for backup with the assumption that isoenzymes are completely non-redundant. We further show that this change of the assumption regarding isoenzymes increases the recall of epistatic interactions predicted by the flux balance model at the cost of a reduction in the precision of the predictions. In addition to suggesting that the gene-to-reaction mapping in genome-scale flux balance models should be used with caution, our analysis provides new evidence that evolutionary gene importance captures much more than strict essentiality.« less

Protein 3D Structure Computed from Evolutionary Sequence Variation

PubMed Central

Sheridan, Robert; Hopf, Thomas A.; Pagnani, Andrea; Zecchina, Riccardo; Sander, Chris

2011-01-01

The evolutionary trajectory of a protein through sequence space is constrained by its function. Collections of sequence homologs record the outcomes of millions of evolutionary experiments in which the protein evolves according to these constraints. Deciphering the evolutionary record held in these sequences and exploiting it for predictive and engineering purposes presents a formidable challenge. The potential benefit of solving this challenge is amplified by the advent of inexpensive high-throughput genomic sequencing. In this paper we ask whether we can infer evolutionary constraints from a set of sequence homologs of a protein. The challenge is to distinguish true co-evolution couplings from the noisy set of observed correlations. We address this challenge using a maximum entropy model of the protein sequence, constrained by the statistics of the multiple sequence alignment, to infer residue pair couplings. Surprisingly, we find that the strength of these inferred couplings is an excellent predictor of residue-residue proximity in folded structures. Indeed, the top-scoring residue couplings are sufficiently accurate and well-distributed to define the 3D protein fold with remarkable accuracy. We quantify this observation by computing, from sequence alone, all-atom 3D structures of fifteen test proteins from different fold classes, ranging in size from 50 to 260 residues., including a G-protein coupled receptor. These blinded inferences are de novo, i.e., they do not use homology modeling or sequence-similar fragments from known structures. The co-evolution signals provide sufficient information to determine accurate 3D protein structure to 2.7–4.8 Å Cα-RMSD error relative to the observed structure, over at least two-thirds of the protein (method called EVfold, details at http://EVfold.org). This discovery provides insight into essential interactions constraining protein evolution and will facilitate a comprehensive survey of the universe of protein structures, new strategies in protein and drug design, and the identification of functional genetic variants in normal and disease genomes. PMID:22163331

Upon accounting for the impact of isoenzyme loss, gene deletion costs anticorrelate with their evolutionary rates

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jacobs, Christopher; Lambourne, Luke; Xia, Yu

Here, system-level metabolic network models enable the computation of growth and metabolic phenotypes from an organism's genome. In particular, flux balance approaches have been used to estimate the contribution of individual metabolic genes to organismal fitness, offering the opportunity to test whether such contributions carry information about the evolutionary pressure on the corresponding genes. Previous failure to identify the expected negative correlation between such computed gene-loss cost and sequence-derived evolutionary rates in Saccharomyces cerevisiae has been ascribed to a real biological gap between a gene's fitness contribution to an organism "here and now"º and the same gene's historical importance asmore » evidenced by its accumulated mutations over millions of years of evolution. Here we show that this negative correlation does exist, and can be exposed by revisiting a broadly employed assumption of flux balance models. In particular, we introduce a new metric that we call "function-loss cost", which estimates the cost of a gene loss event as the total potential functional impairment caused by that loss. This new metric displays significant negative correlation with evolutionary rate, across several thousand minimal environments. We demonstrate that the improvement gained using function-loss cost over gene-loss cost is explained by replacing the base assumption that isoenzymes provide unlimited capacity for backup with the assumption that isoenzymes are completely non-redundant. We further show that this change of the assumption regarding isoenzymes increases the recall of epistatic interactions predicted by the flux balance model at the cost of a reduction in the precision of the predictions. In addition to suggesting that the gene-to-reaction mapping in genome-scale flux balance models should be used with caution, our analysis provides new evidence that evolutionary gene importance captures much more than strict essentiality.« less

Indirect evolutionary rescue: prey adapts, predator avoids extinction

PubMed Central

Yamamichi, Masato; Miner, Brooks E

2015-01-01

Recent studies have increasingly recognized evolutionary rescue (adaptive evolution that prevents extinction following environmental change) as an important process in evolutionary biology and conservation science. Researchers have concentrated on single species living in isolation, but populations in nature exist within communities of interacting species, so evolutionary rescue should also be investigated in a multispecies context. We argue that the persistence or extinction of a focal species can be determined solely by evolutionary change in an interacting species. We demonstrate that prey adaptive evolution can prevent predator extinction in two-species predator–prey models, and we derive the conditions under which this indirect evolutionary interaction is essential to prevent extinction following environmental change. A nonevolving predator can be rescued from extinction by adaptive evolution of its prey due to a trade-off for the prey between defense against predation and population growth rate. As prey typically have larger populations and shorter generations than their predators, prey evolution can be rapid and have profound effects on predator population dynamics. We suggest that this process, which we term ‘indirect evolutionary rescue’, has the potential to be critically important to the ecological and evolutionary responses of populations and communities to dramatic environmental change. PMID:26366196

AN ANGIOTENSIN II TYPE 1 RECEPTOR ACTIVATION SWITCH PATCH REVEALED THROUGH EVOLUTIONARY TRACE ANALYSIS

PubMed Central

Bonde, Marie Mi; Yao, Rong; Ma, Jian-Nong; Madabushi, Srinivasan; Haunsø, Stig; Burstein, Ethan S.; Whistler, Jennifer L.; Sheikh, Søren P.; Lichtarge, Olivier; Hansen, Jakob Lerche

2010-01-01

Seven transmembrane (7TM) or G protein-coupled receptors constitute a large superfamily of cell surface receptors sharing a structural motif of seven transmembrane spanning alpha helices. Their activation mechanism most likely involves concerted movements of the transmembrane helices, but remains to be completely resolved. Evolutionary Trace (ET) analysis is a computational method, which identifies clusters of functionally important residues by integrating information on evolutionary important residue variations with receptor structure. Combined with known mutational data, ET predicted a patch of residues in the cytoplasmic parts of TM2, TM3, and TM6 to form an activation switch that is common to all family A 7TM receptors. We tested this hypothesis in the rat Angiotensin II (Ang II) type 1 (AT1) receptor. The receptor has important roles in the cardiovascular system, but has also frequently been applied as a model for 7TM receptor activation and signaling. Six mutations: F66A, L67R, L70R, L119R, D125A, and I245F were targeted to the putative switch and assayed for changes in activation state by their ligand binding, signaling, and trafficking properties. All but one receptor mutant (that was not expressed well) displayed phenotypes associated with changed activation state, such as increased agonist affinity or basal activity, promiscuous activation, or constitutive internalization highlighting the importance of testing different signaling pathways. We conclude that this evolutionary important patch mediates interactions important for maintaining the inactive state. More broadly, these observations in the AT1 receptor are consistent with computational predictions of a generic role for this patch in 7TM receptor activation. PMID:20227396

3D RNA and functional interactions from evolutionary couplings

PubMed Central

Weinreb, Caleb; Riesselman, Adam; Ingraham, John B.; Gross, Torsten; Sander, Chris; Marks, Debora S.

2016-01-01

Summary Non-coding RNAs are ubiquitous, but the discovery of new RNA gene sequences far outpaces research on their structure and functional interactions. We mine the evolutionary sequence record to derive precise information about function and structure of RNAs and RNA-protein complexes. As in protein structure prediction, we use maximum entropy global probability models of sequence co-variation to infer evolutionarily constrained nucleotide-nucleotide interactions within RNA molecules, and nucleotide-amino acid interactions in RNA-protein complexes. The predicted contacts allow all-atom blinded 3D structure prediction at good accuracy for several known RNA structures and RNA-protein complexes. For unknown structures, we predict contacts in 160 non-coding RNA families. Beyond 3D structure prediction, evolutionary couplings help identify important functional interactions, e.g., at switch points in riboswitches and at a complex nucleation site in HIV. Aided by accelerating sequence accumulation, evolutionary coupling analysis can accelerate the discovery of functional interactions and 3D structures involving RNA. PMID:27087444

Life history determines genetic structure and evolutionary potential of host–parasite interactions

PubMed Central

Barrett, Luke G.; Thrall, Peter H.; Burdon, Jeremy J.; Linde, Celeste C.

2009-01-01

Measures of population genetic structure and diversity of disease-causing organisms are commonly used to draw inferences regarding their evolutionary history and potential to generate new variation in traits that determine interactions with their hosts. Parasite species exhibit a range of population structures and life-history strategies, including different transmission modes, life-cycle complexity, off-host survival mechanisms and dispersal ability. These are important determinants of the frequency and predictability of interactions with host species. Yet the complex causal relationships between spatial structure, life history and the evolutionary dynamics of parasite populations are not well understood. We demonstrate that a clear picture of the evolutionary potential of parasitic organisms and their demographic and evolutionary histories can only come from understanding the role of life history and spatial structure in influencing population dynamics and epidemiological patterns. PMID:18947899

Life history determines genetic structure and evolutionary potential of host-parasite interactions.

PubMed

Barrett, Luke G; Thrall, Peter H; Burdon, Jeremy J; Linde, Celeste C

2008-12-01

Measures of population genetic structure and diversity of disease-causing organisms are commonly used to draw inferences regarding their evolutionary history and potential to generate new variation in traits that determine interactions with their hosts. Parasite species exhibit a range of population structures and life-history strategies, including different transmission modes, life-cycle complexity, off-host survival mechanisms and dispersal ability. These are important determinants of the frequency and predictability of interactions with host species. Yet the complex causal relationships between spatial structure, life history and the evolutionary dynamics of parasite populations are not well understood. We demonstrate that a clear picture of the evolutionary potential of parasitic organisms and their demographic and evolutionary histories can only come from understanding the role of life history and spatial structure in influencing population dynamics and epidemiological patterns.

[The history of development of evolutionary methods in St. Petersburg school of computer simulation in biology].

PubMed

Menshutkin, V V; Kazanskiĭ, A B; Levchenko, V F

2010-01-01

The history of rise and development of evolutionary methods in Saint Petersburg school of biological modelling is traced and analyzed. Some pioneering works in simulation of ecological and evolutionary processes, performed in St.-Petersburg school became an exemplary ones for many followers in Russia and abroad. The individual-based approach became the crucial point in the history of the school as an adequate instrument for construction of models of biological evolution. This approach is natural for simulation of the evolution of life-history parameters and adaptive processes in populations and communities. In some cases simulated evolutionary process was used for solving a reverse problem, i. e., for estimation of uncertain life-history parameters of population. Evolutionary computations is one more aspect of this approach application in great many fields. The problems and vistas of ecological and evolutionary modelling in general are discussed.

Evolutionary dynamics of fluctuating populations with strong mutualism

NASA Astrophysics Data System (ADS)

Chotibut, Thiparat; Nelson, David

2013-03-01

Evolutionary game theory with finite interacting populations is receiving increased attention, including subtle phenomena associated with number fluctuations, i.e., ``genetic drift.'' Models of cooperation and competition often utilize a simplified Moran model, with a strictly fixed total population size. We explore a more general evolutionary model with independent fluctuations in the numbers of two distinct species, in a regime characterized by ``strong mutualism.'' The model has two absorbing states, each corresponding to fixation of one of the two species, and allows exploration of the interplay between growth, competition, and mutualism. When mutualism is favored, number fluctuations eventually drive the system away from a stable fixed point, characterized by cooperation, to one of the absorbing states. Well-mixed populations will thus be taken over by a single species in a finite time, despite the bias towards cooperation. We calculate both the fixation probability and the mean fixation time as a function of the initial conditions and carrying capacities in the strong mutualism regime, using the method of matched asymptotic expansions. Our results are compared to computer simulations.

Knowledge Guided Evolutionary Algorithms in Financial Investing

ERIC Educational Resources Information Center

Wimmer, Hayden

2013-01-01

A large body of literature exists on evolutionary computing, genetic algorithms, decision trees, codified knowledge, and knowledge management systems; however, the intersection of these computing topics has not been widely researched. Moving through the set of all possible solutions--or traversing the search space--at random exhibits no control…

Automated design of spacecraft systems power subsystems

NASA Technical Reports Server (NTRS)

Terrile, Richard J.; Kordon, Mark; Mandutianu, Dan; Salcedo, Jose; Wood, Eric; Hashemi, Mona

2006-01-01

This paper discusses the application of evolutionary computing to a dynamic space vehicle power subsystem resource and performance simulation in a parallel processing environment. Our objective is to demonstrate the feasibility, application and advantage of using evolutionary computation techniques for the early design search and optimization of space systems.

Testing co-evolutionary hypotheses over geological timescales: interactions between Mesozoic non-avian dinosaurs and cycads.

PubMed

Butler, Richard J; Barrett, Paul M; Kenrick, Paul; Penn, Malcolm G

2009-02-01

The significance of co-evolution over ecological timescales is well established, yet it remains unclear to what extent co-evolutionary processes contribute to driving large-scale evolutionary and ecological changes over geological timescales. Some of the most intriguing and pervasive long-term co-evolutionary hypotheses relate to proposed interactions between herbivorous non-avian dinosaurs and Mesozoic plants, including cycads. Dinosaurs have been proposed as key dispersers of cycad seeds during the Mesozoic, and temporal variation in cycad diversity and abundance has been linked to dinosaur faunal changes. Here we assess the evidence for proposed hypotheses of trophic and evolutionary interactions between these two groups using diversity analyses, a new database of Cretaceous dinosaur and plant co-occurrence data, and a geographical information system (GIS) as a visualisation tool. Phylogenetic evidence suggests that the origins of several key biological properties of cycads (e.g. toxins, bright-coloured seeds) likely predated the origin of dinosaurs. Direct evidence of dinosaur-cycad interactions is lacking, but evidence from extant ecosystems suggests that dinosaurs may plausibly have acted as seed dispersers for cycads, although it is likely that other vertebrate groups (e.g. birds, early mammals) also played a role. Although the Late Triassic radiations of dinosaurs and cycads appear to have been approximately contemporaneous, few significant changes in dinosaur faunas coincide with the late Early Cretaceous cycad decline. No significant spatiotemporal associations between particular dinosaur groups and cycads can be identified - GIS visualisation reveals disparities between the spatiotemporal distributions of some dinosaur groups (e.g. sauropodomorphs) and cycads that are inconsistent with co-evolutionary hypotheses. The available data provide no unequivocal support for any of the proposed co-evolutionary interactions between cycads and herbivorous dinosaurs - diffuse co-evolutionary scenarios that are proposed to operate over geological timescales are plausible, but such hypotheses need to be firmly grounded on direct evidence of interaction and may be difficult to support given the patchiness of the fossil record.

A Coevolutionary Arms Race: Understanding Plant-Herbivore Interactions

ERIC Educational Resources Information Center

Becklin, Katie M.

2008-01-01

Plants and insects share a long evolutionary history characterized by relationships that affect individual, population, and community dynamics. Plant-herbivore interactions are a prominent feature of this evolutionary history; it is by plant-herbivore interactions that energy is transferred from primary producers to the rest of the food web. Not…

Using modified fruit fly optimisation algorithm to perform the function test and case studies

NASA Astrophysics Data System (ADS)

Pan, Wen-Tsao

2013-06-01

Evolutionary computation is a computing mode established by practically simulating natural evolutionary processes based on the concept of Darwinian Theory, and it is a common research method. The main contribution of this paper was to reinforce the function of searching for the optimised solution using the fruit fly optimization algorithm (FOA), in order to avoid the acquisition of local extremum solutions. The evolutionary computation has grown to include the concepts of animal foraging behaviour and group behaviour. This study discussed three common evolutionary computation methods and compared them with the modified fruit fly optimization algorithm (MFOA). It further investigated the ability of the three mathematical functions in computing extreme values, as well as the algorithm execution speed and the forecast ability of the forecasting model built using the optimised general regression neural network (GRNN) parameters. The findings indicated that there was no obvious difference between particle swarm optimization and the MFOA in regards to the ability to compute extreme values; however, they were both better than the artificial fish swarm algorithm and FOA. In addition, the MFOA performed better than the particle swarm optimization in regards to the algorithm execution speed, and the forecast ability of the forecasting model built using the MFOA's GRNN parameters was better than that of the other three forecasting models.

Prediction and redesign of protein–protein interactions

PubMed Central

Lua, Rhonald C.; Marciano, David C.; Katsonis, Panagiotis; Adikesavan, Anbu K.; Wilkins, Angela D.; Lichtarge, Olivier

2014-01-01

Understanding the molecular basis of protein function remains a central goal of biology, with the hope to elucidate the role of human genes in health and in disease, and to rationally design therapies through targeted molecular perturbations. We review here some of the computational techniques and resources available for characterizing a critical aspect of protein function – those mediated by protein–protein interactions (PPI). We describe several applications and recent successes of the Evolutionary Trace (ET) in identifying molecular events and shapes that underlie protein function and specificity in both eukaryotes and prokaryotes. ET is a part of analytical approaches based on the successes and failures of evolution that enable the rational control of PPI. PMID:24878423

Social Media: Menagerie of Metrics

DTIC Science & Technology

2010-01-27

intelligence, an evolutionary algorithm (EA) is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm . An EA...Cloning - 22 Animals were cloned to date; genetic algorithms can help prediction (e.g. “elitism” - attempts to ensure selection by including performers...28, 2010 Evolutionary Algorithm • Evolutionary algorithm From Wikipedia, the free encyclopedia Artificial intelligence portal In artificial

Another cat and mouse game: Deciphering the evolution of the SCGB superfamily and exploring the molecular similarity of major cat allergen Fel d 1 and mouse ABP using computational approaches

PubMed Central

Pageat, Patrick; Bienboire-Frosini, Cécile

2018-01-01

The mammalian secretoglobin (SCGB) superfamily contains functionally diverse members, among which the major cat allergen Fel d 1 and mouse salivary androgen-binding protein (ABP) display similar subunits. We searched for molecular similarities between Fel d 1 and ABP to examine the possibility that they play similar roles. We aimed to i) cluster the evolutionary relationships of the SCGB superfamily; ii) identify divergence patterns, structural overlap, and protein-protein docking between Fel d 1 and ABP dimers; and iii) explore the residual interaction between ABP dimers and steroid binding in chemical communication using computational approaches. We also report that the evolutionary tree of the SCGB superfamily comprises seven unique palm-like clusters, showing the evolutionary pattern and divergence time tree of Fel d 1 with 28 ABP paralogs. Three ABP subunits (A27, BG27, and BG26) share phylogenetic relationships with Fel d 1 chains. The Fel d 1 and ABP subunits show similarities in terms of sequence conservation, identical motifs and binding site clefts. Topologically equivalent positions were visualized through superimposition of ABP A27:BG27 (AB) and ABP A27:BG26 (AG) dimers on a heterodimeric Fel d 1 model. In docking, Fel d 1-ABP dimers exhibit the maximum surface binding ability of AG compared with that of AB dimers and the several polar interactions between ABP dimers with steroids. Hence, cat Fel d 1 is an ABP-like molecule in which monomeric chains 1 and 2 are the equivalent of the ABPA and ABPBG monomers, respectively. These findings suggest that the biological and molecular function of Fel d 1 is similar to that of ABP in chemical communication, possibly via pheromone and/or steroid binding. PMID:29771985

Another cat and mouse game: Deciphering the evolution of the SCGB superfamily and exploring the molecular similarity of major cat allergen Fel d 1 and mouse ABP using computational approaches.

PubMed

Durairaj, Rajesh; Pageat, Patrick; Bienboire-Frosini, Cécile

2018-01-01

The mammalian secretoglobin (SCGB) superfamily contains functionally diverse members, among which the major cat allergen Fel d 1 and mouse salivary androgen-binding protein (ABP) display similar subunits. We searched for molecular similarities between Fel d 1 and ABP to examine the possibility that they play similar roles. We aimed to i) cluster the evolutionary relationships of the SCGB superfamily; ii) identify divergence patterns, structural overlap, and protein-protein docking between Fel d 1 and ABP dimers; and iii) explore the residual interaction between ABP dimers and steroid binding in chemical communication using computational approaches. We also report that the evolutionary tree of the SCGB superfamily comprises seven unique palm-like clusters, showing the evolutionary pattern and divergence time tree of Fel d 1 with 28 ABP paralogs. Three ABP subunits (A27, BG27, and BG26) share phylogenetic relationships with Fel d 1 chains. The Fel d 1 and ABP subunits show similarities in terms of sequence conservation, identical motifs and binding site clefts. Topologically equivalent positions were visualized through superimposition of ABP A27:BG27 (AB) and ABP A27:BG26 (AG) dimers on a heterodimeric Fel d 1 model. In docking, Fel d 1-ABP dimers exhibit the maximum surface binding ability of AG compared with that of AB dimers and the several polar interactions between ABP dimers with steroids. Hence, cat Fel d 1 is an ABP-like molecule in which monomeric chains 1 and 2 are the equivalent of the ABPA and ABPBG monomers, respectively. These findings suggest that the biological and molecular function of Fel d 1 is similar to that of ABP in chemical communication, possibly via pheromone and/or steroid binding.

Evolutionary computing for the design search and optimization of space vehicle power subsystems

NASA Technical Reports Server (NTRS)

Kordon, M.; Klimeck, G.; Hanks, D.

2004-01-01

Evolutionary computing has proven to be a straightforward and robust approach for optimizing a wide range of difficult analysis and design problems. This paper discusses the application of these techniques to an existing space vehicle power subsystem resource and performance analysis simulation in a parallel processing environment.

Asymmetric Evolutionary Games.

PubMed

McAvoy, Alex; Hauert, Christoph

2015-08-01

Evolutionary game theory is a powerful framework for studying evolution in populations of interacting individuals. A common assumption in evolutionary game theory is that interactions are symmetric, which means that the players are distinguished by only their strategies. In nature, however, the microscopic interactions between players are nearly always asymmetric due to environmental effects, differing baseline characteristics, and other possible sources of heterogeneity. To model these phenomena, we introduce into evolutionary game theory two broad classes of asymmetric interactions: ecological and genotypic. Ecological asymmetry results from variation in the environments of the players, while genotypic asymmetry is a consequence of the players having differing baseline genotypes. We develop a theory of these forms of asymmetry for games in structured populations and use the classical social dilemmas, the Prisoner's Dilemma and the Snowdrift Game, for illustrations. Interestingly, asymmetric games reveal essential differences between models of genetic evolution based on reproduction and models of cultural evolution based on imitation that are not apparent in symmetric games.

Tree-Structured Digital Organisms Model

NASA Astrophysics Data System (ADS)

Suzuki, Teruhiko; Nobesawa, Shiho; Tahara, Ikuo

Tierra and Avida are well-known models of digital organisms. They describe a life process as a sequence of computation codes. A linear sequence model may not be the only way to describe a digital organism, though it is very simple for a computer-based model. Thus we propose a new digital organism model based on a tree structure, which is rather similar to the generic programming. With our model, a life process is a combination of various functions, as if life in the real world is. This implies that our model can easily describe the hierarchical structure of life, and it can simulate evolutionary computation through mutual interaction of functions. We verified our model by simulations that our model can be regarded as a digital organism model according to its definitions. Our model even succeeded in creating species such as viruses and parasites.

Reconstructing evolutionary trees in parallel for massive sequences.

PubMed

Zou, Quan; Wan, Shixiang; Zeng, Xiangxiang; Ma, Zhanshan Sam

2017-12-14

Building the evolutionary trees for massive unaligned DNA sequences is challenging and crucial. However, reconstructing evolutionary tree for ultra-large sequences is hard. Massive multiple sequence alignment is also challenging and time/space consuming. Hadoop and Spark are developed recently, which bring spring light for the classical computational biology problems. In this paper, we tried to solve the multiple sequence alignment and evolutionary reconstruction in parallel. HPTree, which is developed in this paper, can deal with big DNA sequence files quickly. It works well on the >1GB files, and gets better performance than other evolutionary reconstruction tools. Users could use HPTree for reonstructing evolutioanry trees on the computer clusters or cloud platform (eg. Amazon Cloud). HPTree could help on population evolution research and metagenomics analysis. In this paper, we employ the Hadoop and Spark platform and design an evolutionary tree reconstruction software tool for unaligned massive DNA sequences. Clustering and multiple sequence alignment are done in parallel. Neighbour-joining model was employed for the evolutionary tree building. We opened our software together with source codes via http://lab.malab.cn/soft/HPtree/ .

Practical aspects of protein co-evolution.

PubMed

Ochoa, David; Pazos, Florencio

2014-01-01

Co-evolution is a fundamental aspect of Evolutionary Theory. At the molecular level, co-evolutionary linkages between protein families have been used as indicators of protein interactions and functional relationships from long ago. Due to the complexity of the problem and the amount of genomic data required for these approaches to achieve good performances, it took a relatively long time from the appearance of the first ideas and concepts to the quotidian application of these approaches and their incorporation to the standard toolboxes of bioinformaticians and molecular biologists. Today, these methodologies are mature (both in terms of performance and usability/implementation), and the genomic information that feeds them large enough to allow their general application. This review tries to summarize the current landscape of co-evolution-based methodologies, with a strong emphasis on describing interesting cases where their application to important biological systems, alone or in combination with other computational and experimental approaches, allowed getting new insight into these.

Practical aspects of protein co-evolution

PubMed Central

Ochoa, David; Pazos, Florencio

2014-01-01

Co-evolution is a fundamental aspect of Evolutionary Theory. At the molecular level, co-evolutionary linkages between protein families have been used as indicators of protein interactions and functional relationships from long ago. Due to the complexity of the problem and the amount of genomic data required for these approaches to achieve good performances, it took a relatively long time from the appearance of the first ideas and concepts to the quotidian application of these approaches and their incorporation to the standard toolboxes of bioinformaticians and molecular biologists. Today, these methodologies are mature (both in terms of performance and usability/implementation), and the genomic information that feeds them large enough to allow their general application. This review tries to summarize the current landscape of co-evolution-based methodologies, with a strong emphasis on describing interesting cases where their application to important biological systems, alone or in combination with other computational and experimental approaches, allowed getting new insight into these. PMID:25364721

Evolutionary fate of memory-one strategies in repeated prisoner's dilemma game in structured populations

NASA Astrophysics Data System (ADS)

Liu, Xu-Sheng; Wu, Zhi-Xi; Chen, Michael Z. Q.; Guan, Jian-Yue

2017-07-01

We study evolutionary spatial prisoner's dilemma game involving a one-step memory mechanism of the individuals whenever making strategy updating. In particular, during the process of strategy updating, each individual keeps in mind all the outcome of the action pairs adopted by himself and each of his neighbors in the last interaction, and according to which the individuals decide what actions they will take in the next round. Computer simulation results imply that win-stay-lose-shift like strategy win out of the memory-one strategy set in the stationary state. This result is robust in a large range of the payoff parameter, and does not depend on the initial state of the system. Furthermore, theoretical analysis with mean field and quasi-static approximation predict the same result. Thus, our studies suggest that win-stay-lose-shift like strategy is a stable dominant strategy in repeated prisoner's dilemma game in homogeneous structured populations.

MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms.

PubMed

Kumar, Sudhir; Stecher, Glen; Li, Michael; Knyaz, Christina; Tamura, Koichiro

2018-06-01

The Molecular Evolutionary Genetics Analysis (Mega) software implements many analytical methods and tools for phylogenomics and phylomedicine. Here, we report a transformation of Mega to enable cross-platform use on Microsoft Windows and Linux operating systems. Mega X does not require virtualization or emulation software and provides a uniform user experience across platforms. Mega X has additionally been upgraded to use multiple computing cores for many molecular evolutionary analyses. Mega X is available in two interfaces (graphical and command line) and can be downloaded from www.megasoftware.net free of charge.

Evolutionary responses to climate change in parasitic systems.

PubMed

Chaianunporn, Thotsapol; Hovestadt, Thomas

2015-08-01

Species may respond to climate change in many ecological and evolutionary ways. In this simulation study, we focus on the concurrent evolution of three traits in response to climate change, namely dispersal probability, temperature tolerance (or niche width), and temperature preference (optimal habitat). More specifically, we consider evolutionary responses in host species involved in different types of interaction, that is parasitism or commensalism, and for low or high costs of a temperature tolerance-fertility trade-off (cost of generalization). We find that host species potentially evolve all three traits simultaneously in response to increasing temperature but that the evolutionary response interacts and may be compensatory depending on the conditions. The evolutionary adjustment of temperature preference is slower in the parasitism than in commensalism scenario. Parasitism, in turn, selects for higher temperature tolerance and increased dispersal. High costs for temperature tolerance (i.e. generalization) restrict evolution of tolerance and thus lead to a faster response in temperature preference than that observed under low costs. These results emphasize the possible role of biotic interactions and the importance of 'multidimensional' evolutionary responses to climate change. © 2015 John Wiley & Sons Ltd.

Eco-Evo PVAs: Incorporating Eco-Evolutionary Processes into Population Viability Models

EPA Science Inventory

We synthesize how advances in computational methods and population genomics can be combined within an Ecological-Evolutionary (Eco-Evo) PVA model. Eco-Evo PVA models are powerful new tools for understanding the influence of evolutionary processes on plant and animal population pe...

Lessons from (co-)evolution in the docking of proteins and peptides for CAPRI Rounds 28-35.

PubMed

Yu, Jinchao; Andreani, Jessica; Ochsenbein, Françoise; Guerois, Raphaël

2017-03-01

Computational protein-protein docking is of great importance for understanding protein interactions at the structural level. Critical assessment of prediction of interactions (CAPRI) experiments provide the protein docking community with a unique opportunity to blindly test methods based on real-life cases and help accelerate methodology development. For CAPRI Rounds 28-35, we used an automatic docking pipeline integrating the coarse-grained co-evolution-based potential InterEvScore. This score was developed to exploit the information contained in the multiple sequence alignments of binding partners and selectively recognize co-evolved interfaces. Together with Zdock/Frodock for rigid-body docking, SOAP-PP for atomic potential and Rosetta applications for structural refinement, this pipeline reached high performance on a majority of targets. For protein-peptide docking and interfacial water position predictions, we also explored different means of taking evolutionary information into account. Overall, our group ranked 1 st by correctly predicting 10 targets, composed of 1 High, 7 Medium and 2 Acceptable predictions. Excellent and Outstanding levels of accuracy were reached for each of the two water prediction targets, respectively. Altogether, in 15 out of 18 targets in total, evolutionary information, either through co-evolution or conservation analyses, could provide key constraints to guide modeling towards the most likely assemblies. These results open promising perspectives regarding the way evolutionary information can be valuable to improve docking prediction accuracy. Proteins 2017; 85:378-390. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Learning, epigenetics, and computation: An extension on Fitch's proposal. Comment on “Toward a computational framework for cognitive biology: Unifying approaches from cognitive neuroscience and comparative cognition” by W. Tecumseh Fitch

NASA Astrophysics Data System (ADS)

Okanoya, Kazuo

2014-09-01

The comparative computational approach of Fitch [1] attempts to renew the classical David Marr paradigm of computation, algorithm, and implementation, by introducing evolutionary view of the relationship between neural architecture and cognition. This comparative evolutionary view provides constraints useful in narrowing down the problem space for both cognition and neural mechanisms. I will provide two examples from our own studies that reinforce and extend Fitch's proposal.

Unperturbed Schelling Segregation in Two or Three Dimensions

NASA Astrophysics Data System (ADS)

Barmpalias, George; Elwes, Richard; Lewis-Pye, Andrew

2016-09-01

Schelling's models of segregation, first described in 1969 (Am Econ Rev 59:488-493, 1969) are among the best known models of self-organising behaviour. Their original purpose was to identify mechanisms of urban racial segregation. But his models form part of a family which arises in statistical mechanics, neural networks, social science, and beyond, where populations of agents interact on networks. Despite extensive study, unperturbed Schelling models have largely resisted rigorous analysis, prior results generally focusing on variants in which noise is introduced into the dynamics, the resulting system being amenable to standard techniques from statistical mechanics or stochastic evolutionary game theory (Young in Individual strategy and social structure: an evolutionary theory of institutions, Princeton University Press, Princeton, 1998). A series of recent papers (Brandt et al. in: Proceedings of the 44th annual ACM symposium on theory of computing (STOC 2012), 2012); Barmpalias et al. in: 55th annual IEEE symposium on foundations of computer science, Philadelphia, 2014, J Stat Phys 158:806-852, 2015), has seen the first rigorous analyses of 1-dimensional unperturbed Schelling models, in an asymptotic framework largely unknown in statistical mechanics. Here we provide the first such analysis of 2- and 3-dimensional unperturbed models, establishing most of the phase diagram, and answering a challenge from Brandt et al. in: Proceedings of the 44th annual ACM symposium on theory of computing (STOC 2012), 2012).

Learning Evolution and the Nature of Science Using Evolutionary Computing and Artificial Life

ERIC Educational Resources Information Center

Pennock, Robert T.

2007-01-01

Because evolution in natural systems happens so slowly, it is difficult to design inquiry-based labs where students can experiment and observe evolution in the way they can when studying other phenomena. New research in evolutionary computation and artificial life provides a solution to this problem. This paper describes a new A-Life software…

Evolutionary Paths to Corrupt Societies of Artificial Agents

NASA Astrophysics Data System (ADS)

Nasrallah, Walid

Virtual corrupt societies can be defined as groups of interacting computer-generated agents who predominantly choose behavior that gives short term personal gain at the expense of a higher aggregate cost to others. This paper focuses on corrupt societies that, unlike published models in which cooperation must evolve in order for the society to continue to survive, do not naturally die out as the corrupt class siphons off the resources. For example, a very computationally simple strategy of avoiding confrontation can allow a majority of "unethical" individuals to survive off the efforts of an "ethical" but productive minority. Analogies are drawn to actual human societies in which similar conditions gave rise to behavior traditionally defined as economic or political corruption.

Gender Inequality in Interaction--An Evolutionary Account

ERIC Educational Resources Information Center

Hopcroft, Rosemary L.

2009-01-01

In this article I argue that evolutionary theorizing can help sociologists and feminists better understand gender inequality. Evolutionary theory explains why control of the sexuality of young women is a priority across most human societies both past and present. Evolutionary psychology has extended our understanding of male violence against…

A Novel Handwritten Letter Recognizer Using Enhanced Evolutionary Neural Network

NASA Astrophysics Data System (ADS)

Mahmoudi, Fariborz; Mirzashaeri, Mohsen; Shahamatnia, Ehsan; Faridnia, Saed

This paper introduces a novel design for handwritten letter recognition by employing a hybrid back-propagation neural network with an enhanced evolutionary algorithm. Feeding the neural network consists of a new approach which is invariant to translation, rotation, and scaling of input letters. Evolutionary algorithm is used for the global search of the search space and the back-propagation algorithm is used for the local search. The results have been computed by implementing this approach for recognizing 26 English capital letters in the handwritings of different people. The computational results show that the neural network reaches very satisfying results with relatively scarce input data and a promising performance improvement in convergence of the hybrid evolutionary back-propagation algorithms is exhibited.

Using concepts from biology to improve problem-solving methods

NASA Astrophysics Data System (ADS)

Goodman, Erik D.; Rothwell, Edward J.; Averill, Ronald C.

2011-06-01

Observing nature has been a cornerstone of engineering design. Today, engineers look not only at finished products, but imitate the evolutionary process by which highly optimized artifacts have appeared in nature. Evolutionary computation began by capturing only the simplest ideas of evolution, but today, researchers study natural evolution and incorporate an increasing number of concepts in order to evolve solutions to complex engineering problems. At the new BEACON Center for the Study of Evolution in Action, studies in the lab and field and in silico are laying the groundwork for new tools for evolutionary engineering design. This paper, which accompanies a keynote address, describes various steps in development and application of evolutionary computation, particularly as regards sensor design, and sets the stage for future advances.

Evolution of spatially structured host-parasite interactions.

PubMed

Lion, S; Gandon, S

2015-01-01

Spatial structure has dramatic effects on the demography and the evolution of species. A large variety of theoretical models have attempted to understand how local dispersal may shape the coevolution of interacting species such as host-parasite interactions. The lack of a unifying framework is a serious impediment for anyone willing to understand current theory. Here, we review previous theoretical studies in the light of a single epidemiological model that allows us to explore the effects of both host and parasite migration rates on the evolution and coevolution of various life-history traits. We discuss the impact of local dispersal on parasite virulence, various host defence strategies and local adaptation. Our analysis shows that evolutionary and coevolutionary outcomes crucially depend on the details of the host-parasite life cycle and on which life-history trait is involved in the interaction. We also discuss experimental studies that support the effects of spatial structure on the evolution of host-parasite interactions. This review highlights major similarities between some theoretical results, but it also reveals an important gap between evolutionary and coevolutionary models. We discuss possible ways to bridge this gap within a more unified framework that would reconcile spatial epidemiology, evolution and coevolution. © 2014 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2014 European Society For Evolutionary Biology.

Evolutionary Computation for the Identification of Emergent Behavior in Autonomous Systems

NASA Technical Reports Server (NTRS)

Terrile, Richard J.; Guillaume, Alexandre

2009-01-01

Over the past several years the Center for Evolutionary Computation and Automated Design at the Jet Propulsion Laboratory has developed a technique based on Evolutionary Computational Methods (ECM) that allows for the automated optimization of complex computationally modeled systems. An important application of this technique is for the identification of emergent behaviors in autonomous systems. Mobility platforms such as rovers or airborne vehicles are now being designed with autonomous mission controllers that can find trajectories over a solution space that is larger than can reasonably be tested. It is critical to identify control behaviors that are not predicted and can have surprising results (both good and bad). These emergent behaviors need to be identified, characterized and either incorporated into or isolated from the acceptable range of control characteristics. We use cluster analysis of automatically retrieved solutions to identify isolated populations of solutions with divergent behaviors.

Exploring Evolutionary Patterns in Genetic Sequence: A Computer Exercise

ERIC Educational Resources Information Center

Shumate, Alice M.; Windsor, Aaron J.

2010-01-01

The increase in publications presenting molecular evolutionary analyses and the availability of comparative sequence data through resources such as NCBI's GenBank underscore the necessity of providing undergraduates with hands-on sequence analysis skills in an evolutionary context. This need is particularly acute given that students have been…

Direct Calculation of Protein Fitness Landscapes through Computational Protein Design

PubMed Central

Au, Loretta; Green, David F.

2016-01-01

Naturally selected amino-acid sequences or experimentally derived ones are often the basis for understanding how protein three-dimensional conformation and function are determined by primary structure. Such sequences for a protein family comprise only a small fraction of all possible variants, however, representing the fitness landscape with limited scope. Explicitly sampling and characterizing alternative, unexplored protein sequences would directly identify fundamental reasons for sequence robustness (or variability), and we demonstrate that computational methods offer an efficient mechanism toward this end, on a large scale. The dead-end elimination and A∗ search algorithms were used here to find all low-energy single mutant variants, and corresponding structures of a G-protein heterotrimer, to measure changes in structural stability and binding interactions to define a protein fitness landscape. We established consistency between these algorithms with known biophysical and evolutionary trends for amino-acid substitutions, and could thus recapitulate known protein side-chain interactions and predict novel ones. PMID:26745411

Energetics and solvation structure of a dihalogen dopant (I2) in (4)He clusters.

PubMed

Pérez de Tudela, Ricardo; Barragán, Patricia; Valdés, Álvaro; Prosmiti, Rita

2014-08-21

The energetics and structure of small HeNI2 clusters are analyzed as the size of the system changes, with N up to 38. The full interaction between the I2 molecule and the He atoms is based on analytical ab initio He-I2 potentials plus the He-He interaction, obtained from first-principle calculations. The most stable structures, as a function of the number of solvent He atoms, are obtained by employing an evolutionary algorithm and compared with CCSD(T) and MP2 ab initio computations. Further, the classical description is completed by explicitly including thermal corrections and quantum features, such as zero-point-energy values and spatial delocalization. From quantum PIMC calculations, the binding energies and radial/angular probability density distributions of the thermal equilibrium state for selected-size clusters are computed at a low temperature. The sequential formation of regular shell structures is analyzed and discussed for both classical and quantum treatments.

Evolutionary Game Theory Analysis of Tumor Progression

NASA Astrophysics Data System (ADS)

Wu, Amy; Liao, David; Sturm, James; Austin, Robert

2014-03-01

Evolutionary game theory applied to two interacting cell populations can yield quantitative prediction of the future densities of the two cell populations based on the initial interaction terms. We will discuss how in a complex ecology that evolutionary game theory successfully predicts the future densities of strains of stromal and cancer cells (multiple myeloma), and discuss the possible clinical use of such analysis for predicting cancer progression. Supported by the National Science Foundation and the National Cancer Institute.

Evolutionary inevitability of sexual antagonism.

PubMed

Connallon, Tim; Clark, Andrew G

2014-02-07

Sexual antagonism, whereby mutations are favourable in one sex and disfavourable in the other, is common in natural populations, yet the root causes of sexual antagonism are rarely considered in evolutionary theories of adaptation. Here, we explore the evolutionary consequences of sex-differential selection and genotype-by-sex interactions for adaptation in species with separate sexes. We show that sexual antagonism emerges naturally from sex differences in the direction of selection on phenotypes expressed by both sexes or from sex-by-genotype interactions affecting the expression of such phenotypes. Moreover, modest sex differences in selection or genotype-by-sex effects profoundly influence the long-term evolutionary trajectories of populations with separate sexes, as these conditions trigger the evolution of strong sexual antagonism as a by-product of adaptively driven evolutionary change. The theory demonstrates that sexual antagonism is an inescapable by-product of adaptation in species with separate sexes, whether or not selection favours evolutionary divergence between males and females.

The effects of different styles of interaction on the learning of evolutionary theories

NASA Astrophysics Data System (ADS)

Sugimoto, Akiko

This study investigated the effects of different styles of social interaction on the learning of advanced biological knowledge. Recent research has increasingly acknowledged the importance of social interaction for promoting learning and cognitive development. However, there has been a controversy about the optimal style of interaction. Some studies have showed the beneficial effects of symmetrical interactions such as an argument between peers, whereas other studies have found the superiority of asymmetrical interactions in which a novice learn with the guidance of an expert. The reason for the contradictory results may be that different styles of interaction enhance different kinds of learning. The present study focused on the three styles of interaction; (1) Conflicting style, in which two novice students with scientifically wrong but conflicting views argue with one another, (2) Guiding style, in which a novice student is led by a more expert student to an understanding of scientifically appropriate knowledge, (3) Mutual Constructive style, in which an expert student and a novice student jointly solve a scientific problem on an equal footing. Sixty college students with non-biology-majors and 30 students with a biology major participated in this experiment to discuss an evolutionary problem in these three styles of interaction, with the former serving as novices and the latter as experts. Analyses of the Pre- and the Posttest performance and discussion processes in the Interaction session revealed the following. First, the Guiding style and the Mutual Constructive style enhanced the acquisition of the scientific evolutionary conceptual framework more effectively than the Conflicting style. However, some students in the Conflicting style also grasped the scientific evolutionary framework, and many students reconstructed their theories of evolution through discussion, even if the frameworks remained scientifically inappropriate. Second, the students who discussed evolution in the Conflicting style and the Mutual Constructive style tended to become more reflective and flexible than the students in the Guiding style, when solving a new evolutionary problem. Third, analyses of epistemological beliefs and critiques of evolutionary explanations suggested that the Mutual Constructive style and the Conflicting style facilitated the development of critical thinking more than the Guiding style.

Cooperation in microbial communities and their biotechnological applications

PubMed Central

Cavaliere, Matteo; Feng, Song; Soyer, Orkun S.

2017-01-01

Summary Microbial communities are increasingly utilized in biotechnology. Efficiency and productivity in many of these applications depends on the presence of cooperative interactions between members of the community. Two key processes underlying these interactions are the production of public goods and metabolic cross‐feeding, which can be understood in the general framework of ecological and evolutionary (eco‐evo) dynamics. In this review, we illustrate the relevance of cooperative interactions in microbial biotechnological processes, discuss their mechanistic origins and analyse their evolutionary resilience. Cooperative behaviours can be damaged by the emergence of ‘cheating’ cells that benefit from the cooperative interactions but do not contribute to them. Despite this, cooperative interactions can be stabilized by spatial segregation, by the presence of feedbacks between the evolutionary dynamics and the ecology of the community, by the role of regulatory systems coupled to the environmental conditions and by the action of horizontal gene transfer. Cooperative interactions enrich microbial communities with a higher degree of robustness against environmental stress and can facilitate the evolution of more complex traits. Therefore, the evolutionary resilience of microbial communities and their ability to constraint detrimental mutants should be considered to design robust biotechnological applications. PMID:28447371

Inference of Evolutionary Jumps in Large Phylogenies using Lévy Processes

PubMed Central

Duchen, Pablo; Leuenberger, Christoph; Szilágyi, Sándor M.; Harmon, Luke; Eastman, Jonathan; Schweizer, Manuel

2017-01-01

Abstract Although it is now widely accepted that the rate of phenotypic evolution may not necessarily be constant across large phylogenies, the frequency and phylogenetic position of periods of rapid evolution remain unclear. In his highly influential view of evolution, G. G. Simpson supposed that such evolutionary jumps occur when organisms transition into so-called new adaptive zones, for instance after dispersal into a new geographic area, after rapid climatic changes, or following the appearance of an evolutionary novelty. Only recently, large, accurate and well calibrated phylogenies have become available that allow testing this hypothesis directly, yet inferring evolutionary jumps remains computationally very challenging. Here, we develop a computationally highly efficient algorithm to accurately infer the rate and strength of evolutionary jumps as well as their phylogenetic location. Following previous work we model evolutionary jumps as a compound process, but introduce a novel approach to sample jump configurations that does not require matrix inversions and thus naturally scales to large trees. We then make use of this development to infer evolutionary jumps in Anolis lizards and Loriinii parrots where we find strong signal for such jumps at the basis of clades that transitioned into new adaptive zones, just as postulated by Simpson’s hypothesis. [evolutionary jump; Lévy process; phenotypic evolution; punctuated equilibrium; quantitative traits. PMID:28204787

Asymmetric Evolutionary Games

PubMed Central

McAvoy, Alex; Hauert, Christoph

2015-01-01

Evolutionary game theory is a powerful framework for studying evolution in populations of interacting individuals. A common assumption in evolutionary game theory is that interactions are symmetric, which means that the players are distinguished by only their strategies. In nature, however, the microscopic interactions between players are nearly always asymmetric due to environmental effects, differing baseline characteristics, and other possible sources of heterogeneity. To model these phenomena, we introduce into evolutionary game theory two broad classes of asymmetric interactions: ecological and genotypic. Ecological asymmetry results from variation in the environments of the players, while genotypic asymmetry is a consequence of the players having differing baseline genotypes. We develop a theory of these forms of asymmetry for games in structured populations and use the classical social dilemmas, the Prisoner’s Dilemma and the Snowdrift Game, for illustrations. Interestingly, asymmetric games reveal essential differences between models of genetic evolution based on reproduction and models of cultural evolution based on imitation that are not apparent in symmetric games. PMID:26308326

Protein-protein interactions in paralogues: Electrostatics modulates specificity on a conserved steric scaffold

PubMed Central

Huber, Roland G.; Bond, Peter J.

2017-01-01

An improved knowledge of protein-protein interactions is essential for better understanding of metabolic and signaling networks, and cellular function. Progress tends to be based on structure determination and predictions using known structures, along with computational methods based on evolutionary information or detailed atomistic descriptions. We hypothesized that for the case of interactions across a common interface, between proteins from a pair of paralogue families or within a family of paralogues, a relatively simple interface description could distinguish between binding and non-binding pairs. Using binding data for several systems, and large-scale comparative modeling based on known template complex structures, it is found that charge-charge interactions (for groups bearing net charge) are generally a better discriminant than buried non-polar surface. This is particularly the case for paralogue families that are less divergent, with more reliable comparative modeling. We suggest that electrostatic interactions are major determinants of specificity in such systems, an observation that could be used to predict binding partners. PMID:29016650

Protein-protein interactions in paralogues: Electrostatics modulates specificity on a conserved steric scaffold.

PubMed

Ivanov, Stefan M; Cawley, Andrew; Huber, Roland G; Bond, Peter J; Warwicker, Jim

2017-01-01

An improved knowledge of protein-protein interactions is essential for better understanding of metabolic and signaling networks, and cellular function. Progress tends to be based on structure determination and predictions using known structures, along with computational methods based on evolutionary information or detailed atomistic descriptions. We hypothesized that for the case of interactions across a common interface, between proteins from a pair of paralogue families or within a family of paralogues, a relatively simple interface description could distinguish between binding and non-binding pairs. Using binding data for several systems, and large-scale comparative modeling based on known template complex structures, it is found that charge-charge interactions (for groups bearing net charge) are generally a better discriminant than buried non-polar surface. This is particularly the case for paralogue families that are less divergent, with more reliable comparative modeling. We suggest that electrostatic interactions are major determinants of specificity in such systems, an observation that could be used to predict binding partners.

Open Reading Frame Phylogenetic Analysis on the Cloud

PubMed Central

2013-01-01

Phylogenetic analysis has become essential in researching the evolutionary relationships between viruses. These relationships are depicted on phylogenetic trees, in which viruses are grouped based on sequence similarity. Viral evolutionary relationships are identified from open reading frames rather than from complete sequences. Recently, cloud computing has become popular for developing internet-based bioinformatics tools. Biocloud is an efficient, scalable, and robust bioinformatics computing service. In this paper, we propose a cloud-based open reading frame phylogenetic analysis service. The proposed service integrates the Hadoop framework, virtualization technology, and phylogenetic analysis methods to provide a high-availability, large-scale bioservice. In a case study, we analyze the phylogenetic relationships among Norovirus. Evolutionary relationships are elucidated by aligning different open reading frame sequences. The proposed platform correctly identifies the evolutionary relationships between members of Norovirus. PMID:23671843

Evolutionary Technologies: Fundamentals and Applications to Information/Communication Systems and Manufacturing/Logistics Systems

NASA Astrophysics Data System (ADS)

Gen, Mitsuo; Kawakami, Hiroshi; Tsujimura, Yasuhiro; Handa, Hisashi; Lin, Lin; Okamoto, Azuma

As efficient utilization of computational resources is increasing, evolutionary technology based on the Genetic Algorithm (GA), Genetic Programming (GP), Evolution Strategy (ES) and other Evolutionary Computations (ECs) is making rapid progress, and its social recognition and the need as applied technology are increasing. This is explained by the facts that EC offers higher robustness for knowledge information processing systems, intelligent production and logistics systems, most advanced production scheduling and other various real-world problems compared to the approaches based on conventional theories, and EC ensures flexible applicability and usefulness for any unknown system environment even in a case where accurate mathematical modeling fails in the formulation. In this paper, we provide a comprehensive survey of the current state-of-the-art in the fundamentals and applications of evolutionary technologies.

PhyloBot: A Web Portal for Automated Phylogenetics, Ancestral Sequence Reconstruction, and Exploration of Mutational Trajectories.

PubMed

Hanson-Smith, Victor; Johnson, Alexander

2016-07-01

The method of phylogenetic ancestral sequence reconstruction is a powerful approach for studying evolutionary relationships among protein sequence, structure, and function. In particular, this approach allows investigators to (1) reconstruct and "resurrect" (that is, synthesize in vivo or in vitro) extinct proteins to study how they differ from modern proteins, (2) identify key amino acid changes that, over evolutionary timescales, have altered the function of the protein, and (3) order historical events in the evolution of protein function. Widespread use of this approach has been slow among molecular biologists, in part because the methods require significant computational expertise. Here we present PhyloBot, a web-based software tool that makes ancestral sequence reconstruction easy. Designed for non-experts, it integrates all the necessary software into a single user interface. Additionally, PhyloBot provides interactive tools to explore evolutionary trajectories between ancestors, enabling the rapid generation of hypotheses that can be tested using genetic or biochemical approaches. Early versions of this software were used in previous studies to discover genetic mechanisms underlying the functions of diverse protein families, including V-ATPase ion pumps, DNA-binding transcription regulators, and serine/threonine protein kinases. PhyloBot runs in a web browser, and is available at the following URL: http://www.phylobot.com. The software is implemented in Python using the Django web framework, and runs on elastic cloud computing resources from Amazon Web Services. Users can create and submit jobs on our free server (at the URL listed above), or use our open-source code to launch their own PhyloBot server.

PhyloBot: A Web Portal for Automated Phylogenetics, Ancestral Sequence Reconstruction, and Exploration of Mutational Trajectories

PubMed Central

Hanson-Smith, Victor; Johnson, Alexander

2016-01-01

The method of phylogenetic ancestral sequence reconstruction is a powerful approach for studying evolutionary relationships among protein sequence, structure, and function. In particular, this approach allows investigators to (1) reconstruct and “resurrect” (that is, synthesize in vivo or in vitro) extinct proteins to study how they differ from modern proteins, (2) identify key amino acid changes that, over evolutionary timescales, have altered the function of the protein, and (3) order historical events in the evolution of protein function. Widespread use of this approach has been slow among molecular biologists, in part because the methods require significant computational expertise. Here we present PhyloBot, a web-based software tool that makes ancestral sequence reconstruction easy. Designed for non-experts, it integrates all the necessary software into a single user interface. Additionally, PhyloBot provides interactive tools to explore evolutionary trajectories between ancestors, enabling the rapid generation of hypotheses that can be tested using genetic or biochemical approaches. Early versions of this software were used in previous studies to discover genetic mechanisms underlying the functions of diverse protein families, including V-ATPase ion pumps, DNA-binding transcription regulators, and serine/threonine protein kinases. PhyloBot runs in a web browser, and is available at the following URL: http://www.phylobot.com. The software is implemented in Python using the Django web framework, and runs on elastic cloud computing resources from Amazon Web Services. Users can create and submit jobs on our free server (at the URL listed above), or use our open-source code to launch their own PhyloBot server. PMID:27472806

Computationally mapping sequence space to understand evolutionary protein engineering.

PubMed

Armstrong, Kathryn A; Tidor, Bruce

2008-01-01

Evolutionary protein engineering has been dramatically successful, producing a wide variety of new proteins with altered stability, binding affinity, and enzymatic activity. However, the success of such procedures is often unreliable, and the impact of the choice of protein, engineering goal, and evolutionary procedure is not well understood. We have created a framework for understanding aspects of the protein engineering process by computationally mapping regions of feasible sequence space for three small proteins using structure-based design protocols. We then tested the ability of different evolutionary search strategies to explore these sequence spaces. The results point to a non-intuitive relationship between the error-prone PCR mutation rate and the number of rounds of replication. The evolutionary relationships among feasible sequences reveal hub-like sequences that serve as particularly fruitful starting sequences for evolutionary search. Moreover, genetic recombination procedures were examined, and tradeoffs relating sequence diversity and search efficiency were identified. This framework allows us to consider the impact of protein structure on the allowed sequence space and therefore on the challenges that each protein presents to error-prone PCR and genetic recombination procedures.

Form of an evolutionary tradeoff affects eco-evolutionary dynamics in a predator-prey system.

PubMed

Kasada, Minoru; Yamamichi, Masato; Yoshida, Takehito

2014-11-11

Evolution on a time scale similar to ecological dynamics has been increasingly recognized for the last three decades. Selection mediated by ecological interactions can change heritable phenotypic variation (i.e., evolution), and evolution of traits, in turn, can affect ecological interactions. Hence, ecological and evolutionary dynamics can be tightly linked and important to predict future dynamics, but our understanding of eco-evolutionary dynamics is still in its infancy and there is a significant gap between theoretical predictions and empirical tests. Empirical studies have demonstrated that the presence of genetic variation can dramatically change ecological dynamics, whereas theoretical studies predict that eco-evolutionary dynamics depend on the details of the genetic variation, such as the form of a tradeoff among genotypes, which can be more important than the presence or absence of the genetic variation. Using a predator-prey (rotifer-algal) experimental system in laboratory microcosms, we studied how different forms of a tradeoff between prey defense and growth affect eco-evolutionary dynamics. Our experimental results show for the first time to our knowledge that different forms of the tradeoff produce remarkably divergent eco-evolutionary dynamics, including near fixation, near extinction, and coexistence of algal genotypes, with quantitatively different population dynamics. A mathematical model, parameterized from completely independent experiments, explains the observed dynamics. The results suggest that knowing the details of heritable trait variation and covariation within a population is essential for understanding how evolution and ecology will interact and what form of eco-evolutionary dynamics will result.

PRMT7 Interacts with ASS1 and Citrullinemia Mutations Disrupt the Interaction.

PubMed

Verma, Mamta; Charles, Ramya Chandar M; Chakrapani, Baskar; Coumar, Mohane Selvaraj; Govindaraju, Gayathri; Rajavelu, Arumugam; Chavali, Sreenivas; Dhayalan, Arunkumar

2017-07-21

Protein arginine methyltransferase 7 (PRMT7) catalyzes the introduction of monomethylation marks at the arginine residues of substrate proteins. PRMT7 plays important roles in the regulation of gene expression, splicing, DNA damage, paternal imprinting, cancer and metastasis. However, little is known about the interaction partners of PRMT7. To address this, we performed yeast two-hybrid screening of PRMT7 and identified argininosuccinate synthetase (ASS1) as a potential interaction partner of PRMT7. We confirmed that PRMT7 directly interacts with ASS1 using pull-down studies. ASS1 catalyzes the rate-limiting step of arginine synthesis in urea cycle and citrulline-nitric oxide cycle. We mapped the interface of PRMT7-ASS1 complex through computational approaches and validated the predicted interface in vivo by site-directed mutagenesis. Evolutionary analysis revealed that the ASS1 residues important for PRMT7-ASS1 interaction have co-evolved with PRMT7. We showed that ASS1 mutations linked to type I citrullinemia disrupt the ASS1-PRMT7 interaction, which might explain the molecular pathogenesis of the disease. Copyright © 2017 Elsevier Ltd. All rights reserved.

Sequence co-evolution gives 3D contacts and structures of protein complexes

PubMed Central

Hopf, Thomas A; Schärfe, Charlotta P I; Rodrigues, João P G L M; Green, Anna G; Kohlbacher, Oliver; Sander, Chris; Bonvin, Alexandre M J J; Marks, Debora S

2014-01-01

Protein–protein interactions are fundamental to many biological processes. Experimental screens have identified tens of thousands of interactions, and structural biology has provided detailed functional insight for select 3D protein complexes. An alternative rich source of information about protein interactions is the evolutionary sequence record. Building on earlier work, we show that analysis of correlated evolutionary sequence changes across proteins identifies residues that are close in space with sufficient accuracy to determine the three-dimensional structure of the protein complexes. We evaluate prediction performance in blinded tests on 76 complexes of known 3D structure, predict protein–protein contacts in 32 complexes of unknown structure, and demonstrate how evolutionary couplings can be used to distinguish between interacting and non-interacting protein pairs in a large complex. With the current growth of sequences, we expect that the method can be generalized to genome-wide elucidation of protein–protein interaction networks and used for interaction predictions at residue resolution. DOI: http://dx.doi.org/10.7554/eLife.03430.001 PMID:25255213

InterEvDock2: an expanded server for protein docking using evolutionary and biological information from homology models and multimeric inputs.

PubMed

Quignot, Chloé; Rey, Julien; Yu, Jinchao; Tufféry, Pierre; Guerois, Raphaël; Andreani, Jessica

2018-05-08

Computational protein docking is a powerful strategy to predict structures of protein-protein interactions and provides crucial insights for the functional characterization of macromolecular cross-talks. We previously developed InterEvDock, a server for ab initio protein docking based on rigid-body sampling followed by consensus scoring using physics-based and statistical potentials, including the InterEvScore function specifically developed to incorporate co-evolutionary information in docking. InterEvDock2 is a major evolution of InterEvDock which allows users to submit input sequences - not only structures - and multimeric inputs and to specify constraints for the pairwise docking process based on previous knowledge about the interaction. For this purpose, we added modules in InterEvDock2 for automatic template search and comparative modeling of the input proteins. The InterEvDock2 pipeline was benchmarked on 812 complexes for which unbound homology models of the two partners and co-evolutionary information are available in the PPI4DOCK database. InterEvDock2 identified a correct model among the top 10 consensus in 29% of these cases (compared to 15-24% for individual scoring functions) and at least one correct interface residue among 10 predicted in 91% of these cases. InterEvDock2 is thus a unique protein docking server, designed to be useful for the experimental biology community. The InterEvDock2 web interface is available at http://bioserv.rpbs.univ-paris-diderot.fr/services/InterEvDock2/.

Buried treasure: evolutionary perspectives on microbial iron piracy

PubMed Central

Barber, Matthew F.; Elde, Nels C.

2015-01-01

Host-pathogen interactions provide valuable systems for the study of evolutionary genetics and natural selection. The sequestration of essential iron has emerged as a critical innate defense system termed nutritional immunity, leading pathogens to evolve mechanisms of `iron piracy' to scavenge this metal from host proteins. This battle for iron carries numerous consequences not only for host-pathogen evolution, but also microbial community interactions. Here we highlight recent and potential future areas of investigation on the evolutionary implications of microbial iron piracy in relation to molecular arms races, host range, competition, and virulence. Applying evolutionary genetic approaches to the study of microbial iron acquisition could also provide new inroads for understanding and combating infectious disease. PMID:26431675

Relative importance of evolutionary dynamics depends on the composition of microbial predator-prey community.

PubMed

Friman, Ville-Petri; Dupont, Alessandra; Bass, David; Murrell, David J; Bell, Thomas

2016-06-01

Community dynamics are often studied in subsets of pairwise interactions. Scaling pairwise interactions back to the community level is, however, problematic because one given interaction might not reflect ecological and evolutionary outcomes of other functionally similar species interactions or capture the emergent eco-evolutionary dynamics arising only in more complex communities. Here we studied this experimentally by exposing Pseudomonas fluorescens SBW25 prey bacterium to four different protist predators (Tetrahymena pyriformis, Tetrahymena vorax, Chilomonas paramecium and Acanthamoeba polyphaga) in all possible single-predator, two-predator and four-predator communities for hundreds of prey generations covering both ecological and evolutionary timescales. We found that only T. pyriformis selected for prey defence in single-predator communities. Although T. pyriformis selection was constrained in the presence of the intraguild predator, T. vorax, T. pyriformis selection led to evolution of specialised prey defence strategies in the presence of C. paramecium or A. polyphaga. At the ecological level, adapted prey populations were phenotypically more diverse, less stable and less productive compared with non-adapted prey populations. These results suggest that predator community composition affects the relative importance of ecological and evolutionary processes and can crucially determine when rapid evolution has the potential to change ecological properties of microbial communities.

Relative importance of evolutionary dynamics depends on the composition of microbial predator–prey community

PubMed Central

Friman, Ville-Petri; Dupont, Alessandra; Bass, David; Murrell, David J; Bell, Thomas

2016-01-01

Community dynamics are often studied in subsets of pairwise interactions. Scaling pairwise interactions back to the community level is, however, problematic because one given interaction might not reflect ecological and evolutionary outcomes of other functionally similar species interactions or capture the emergent eco-evolutionary dynamics arising only in more complex communities. Here we studied this experimentally by exposing Pseudomonas fluorescens SBW25 prey bacterium to four different protist predators (Tetrahymena pyriformis, Tetrahymena vorax, Chilomonas paramecium and Acanthamoeba polyphaga) in all possible single-predator, two-predator and four-predator communities for hundreds of prey generations covering both ecological and evolutionary timescales. We found that only T. pyriformis selected for prey defence in single-predator communities. Although T. pyriformis selection was constrained in the presence of the intraguild predator, T. vorax, T. pyriformis selection led to evolution of specialised prey defence strategies in the presence of C. paramecium or A. polyphaga. At the ecological level, adapted prey populations were phenotypically more diverse, less stable and less productive compared with non-adapted prey populations. These results suggest that predator community composition affects the relative importance of ecological and evolutionary processes and can crucially determine when rapid evolution has the potential to change ecological properties of microbial communities. PMID:26684728

A Strategic Interaction Model of Punishment Favoring Contagion of Honest Behavior

PubMed Central

Cremene, Marcel; Dumitrescu, D.; Cremene, Ligia

2014-01-01

The punishment effect on social behavior is analyzed within the strategic interaction framework of Cellular Automata and computational Evolutionary Game Theory. A new game, called Social Honesty (SH), is proposed. The SH game is analyzed in spatial configurations. Probabilistic punishment is used as a dishonesty deterrence mechanism. In order to capture the intrinsic uncertainty of social environments, payoffs are described as random variables. New dynamics, with a new relation between punishment probability and punishment severity, are revealed. Punishment probability proves to be more important than punishment severity in guiding convergence towards honesty as predominant behavior. This result is confirmed by empirical evidence and reported experiments. Critical values and transition intervals for punishment probability and severity are identified and analyzed. Clusters of honest or dishonest players emerge spontaneously from the very first rounds of interaction and are determinant for the future dynamics and outcomes. PMID:24489917

Stochastic Evolutionary Algorithms for Planning Robot Paths

NASA Technical Reports Server (NTRS)

Fink, Wolfgang; Aghazarian, Hrand; Huntsberger, Terrance; Terrile, Richard

2006-01-01

A computer program implements stochastic evolutionary algorithms for planning and optimizing collision-free paths for robots and their jointed limbs. Stochastic evolutionary algorithms can be made to produce acceptably close approximations to exact, optimal solutions for path-planning problems while often demanding much less computation than do exhaustive-search and deterministic inverse-kinematics algorithms that have been used previously for this purpose. Hence, the present software is better suited for application aboard robots having limited computing capabilities (see figure). The stochastic aspect lies in the use of simulated annealing to (1) prevent trapping of an optimization algorithm in local minima of an energy-like error measure by which the fitness of a trial solution is evaluated while (2) ensuring that the entire multidimensional configuration and parameter space of the path-planning problem is sampled efficiently with respect to both robot joint angles and computation time. Simulated annealing is an established technique for avoiding local minima in multidimensional optimization problems, but has not, until now, been applied to planning collision-free robot paths by use of low-power computers.

Biomimetic design processes in architecture: morphogenetic and evolutionary computational design.

PubMed

Menges, Achim

2012-03-01

Design computation has profound impact on architectural design methods. This paper explains how computational design enables the development of biomimetic design processes specific to architecture, and how they need to be significantly different from established biomimetic processes in engineering disciplines. The paper first explains the fundamental difference between computer-aided and computational design in architecture, as the understanding of this distinction is of critical importance for the research presented. Thereafter, the conceptual relation and possible transfer of principles from natural morphogenesis to design computation are introduced and the related developments of generative, feature-based, constraint-based, process-based and feedback-based computational design methods are presented. This morphogenetic design research is then related to exploratory evolutionary computation, followed by the presentation of two case studies focusing on the exemplary development of spatial envelope morphologies and urban block morphologies.

Inference of Evolutionary Jumps in Large Phylogenies using Lévy Processes.

PubMed

Duchen, Pablo; Leuenberger, Christoph; Szilágyi, Sándor M; Harmon, Luke; Eastman, Jonathan; Schweizer, Manuel; Wegmann, Daniel

2017-11-01

Although it is now widely accepted that the rate of phenotypic evolution may not necessarily be constant across large phylogenies, the frequency and phylogenetic position of periods of rapid evolution remain unclear. In his highly influential view of evolution, G. G. Simpson supposed that such evolutionary jumps occur when organisms transition into so-called new adaptive zones, for instance after dispersal into a new geographic area, after rapid climatic changes, or following the appearance of an evolutionary novelty. Only recently, large, accurate and well calibrated phylogenies have become available that allow testing this hypothesis directly, yet inferring evolutionary jumps remains computationally very challenging. Here, we develop a computationally highly efficient algorithm to accurately infer the rate and strength of evolutionary jumps as well as their phylogenetic location. Following previous work we model evolutionary jumps as a compound process, but introduce a novel approach to sample jump configurations that does not require matrix inversions and thus naturally scales to large trees. We then make use of this development to infer evolutionary jumps in Anolis lizards and Loriinii parrots where we find strong signal for such jumps at the basis of clades that transitioned into new adaptive zones, just as postulated by Simpson's hypothesis. [evolutionary jump; Lévy process; phenotypic evolution; punctuated equilibrium; quantitative traits. The Author(s) 2017. Published by Oxford University Press, on behalf of the Society of Systematic Biologists.

Big cat phylogenies, consensus trees, and computational thinking.

PubMed

Sul, Seung-Jin; Williams, Tiffani L

2011-07-01

Phylogenetics seeks to deduce the pattern of relatedness between organisms by using a phylogeny or evolutionary tree. For a given set of organisms or taxa, there may be many evolutionary trees depicting how these organisms evolved from a common ancestor. As a result, consensus trees are a popular approach for summarizing the shared evolutionary relationships in a group of trees. We examine these consensus techniques by studying how the pantherine lineage of cats (clouded leopard, jaguar, leopard, lion, snow leopard, and tiger) evolved, which is hotly debated. While there are many phylogenetic resources that describe consensus trees, there is very little information, written for biologists, regarding the underlying computational techniques for building them. The pantherine cats provide us with a small, relevant example to explore the computational techniques (such as sorting numbers, hashing functions, and traversing trees) for constructing consensus trees. Our hope is that life scientists enjoy peeking under the computational hood of consensus tree construction and share their positive experiences with others in their community.

Accounting for epistatic interactions improves the functional analysis of protein structures.

PubMed

Wilkins, Angela D; Venner, Eric; Marciano, David C; Erdin, Serkan; Atri, Benu; Lua, Rhonald C; Lichtarge, Olivier

2013-11-01

The constraints under which sequence, structure and function coevolve are not fully understood. Bringing this mutual relationship to light can reveal the molecular basis of binding, catalysis and allostery, thereby identifying function and rationally guiding protein redesign. Underlying these relationships are the epistatic interactions that occur when the consequences of a mutation to a protein are determined by the genetic background in which it occurs. Based on prior data, we hypothesize that epistatic forces operate most strongly between residues nearby in the structure, resulting in smooth evolutionary importance across the structure. We find that when residue scores of evolutionary importance are distributed smoothly between nearby residues, functional site prediction accuracy improves. Accordingly, we designed a novel measure of evolutionary importance that focuses on the interaction between pairs of structurally neighboring residues. This measure that we term pair-interaction Evolutionary Trace yields greater functional site overlap and better structure-based proteome-wide functional predictions. Our data show that the structural smoothness of evolutionary importance is a fundamental feature of the coevolution of sequence, structure and function. Mutations operate on individual residues, but selective pressure depends in part on the extent to which a mutation perturbs interactions with neighboring residues. In practice, this principle led us to redefine the importance of a residue in terms of the importance of its epistatic interactions with neighbors, yielding better annotation of functional residues, motivating experimental validation of a novel functional site in LexA and refining protein function prediction. lichtarge@bcm.edu. Supplementary data are available at Bioinformatics online.

Accounting for epistatic interactions improves the functional analysis of protein structures

PubMed Central

Wilkins, Angela D.; Venner, Eric; Marciano, David C.; Erdin, Serkan; Atri, Benu; Lua, Rhonald C.; Lichtarge, Olivier

2013-01-01

Motivation: The constraints under which sequence, structure and function coevolve are not fully understood. Bringing this mutual relationship to light can reveal the molecular basis of binding, catalysis and allostery, thereby identifying function and rationally guiding protein redesign. Underlying these relationships are the epistatic interactions that occur when the consequences of a mutation to a protein are determined by the genetic background in which it occurs. Based on prior data, we hypothesize that epistatic forces operate most strongly between residues nearby in the structure, resulting in smooth evolutionary importance across the structure. Methods and Results: We find that when residue scores of evolutionary importance are distributed smoothly between nearby residues, functional site prediction accuracy improves. Accordingly, we designed a novel measure of evolutionary importance that focuses on the interaction between pairs of structurally neighboring residues. This measure that we term pair-interaction Evolutionary Trace yields greater functional site overlap and better structure-based proteome-wide functional predictions. Conclusions: Our data show that the structural smoothness of evolutionary importance is a fundamental feature of the coevolution of sequence, structure and function. Mutations operate on individual residues, but selective pressure depends in part on the extent to which a mutation perturbs interactions with neighboring residues. In practice, this principle led us to redefine the importance of a residue in terms of the importance of its epistatic interactions with neighbors, yielding better annotation of functional residues, motivating experimental validation of a novel functional site in LexA and refining protein function prediction. Contact: lichtarge@bcm.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24021383

Cophylogenetic signal is detectable in pollination interactions across ecological scales.

PubMed

Hutchinson, Matthew C; Cagua, Edgar Fernando; Stouffer, Daniel B

2017-10-01

That evolutionary history can influence the way that species interact is a basic tenet of evolutionary ecology. However, when the role of evolution in determining ecological interactions is investigated, focus typically centers on just one side of the interaction. A cophylogenetic signal, the congruence of evolutionary history across both sides of an ecological interaction, extends these previous explorations and provides a more complete picture of how evolutionary patterns influence the way species interact. To date, cophylogenetic signal has most typically been studied in interactions that occur between fine taxonomic clades that show high intimacy. In this study, we took an alternative approach and made an exhaustive assessment of cophylogeny in pollination interactions. To do so, we assessed the strength of cophylogenetic signal at four distinct scales of pollination interaction: (1) across plant-pollinator associations globally, (2) in local pollination communities, (3) within the modular structure of those communities, and (4) in individual modules. We did so using a globally distributed dataset comprised of 54 pollination networks, over 4000 species, and over 12,000 interactions. Within these data, we detected cophylogenetic signal at all four scales. Cophylogenetic signal was found at the level of plant-pollinator interactions on a global scale and in the majority of pollination communities. At the scale defined by the modular structure within those communities, however, we observed a much weaker cophylogenetic signal. Cophylogenetic signal was detectable in a significant proportion of individual modules and most typically when within-module phylogenetic diversity was low. In sum, the detection of cophylogenetic signal in pollination interactions across scales provides a new dimension to the story of how past evolution shapes extant pollinator-angiosperm interactions. © 2017 by the Ecological Society of America.

MEGA5: Molecular Evolutionary Genetics Analysis Using Maximum Likelihood, Evolutionary Distance, and Maximum Parsimony Methods

PubMed Central

Tamura, Koichiro; Peterson, Daniel; Peterson, Nicholas; Stecher, Glen; Nei, Masatoshi; Kumar, Sudhir

2011-01-01

Comparative analysis of molecular sequence data is essential for reconstructing the evolutionary histories of species and inferring the nature and extent of selective forces shaping the evolution of genes and species. Here, we announce the release of Molecular Evolutionary Genetics Analysis version 5 (MEGA5), which is a user-friendly software for mining online databases, building sequence alignments and phylogenetic trees, and using methods of evolutionary bioinformatics in basic biology, biomedicine, and evolution. The newest addition in MEGA5 is a collection of maximum likelihood (ML) analyses for inferring evolutionary trees, selecting best-fit substitution models (nucleotide or amino acid), inferring ancestral states and sequences (along with probabilities), and estimating evolutionary rates site-by-site. In computer simulation analyses, ML tree inference algorithms in MEGA5 compared favorably with other software packages in terms of computational efficiency and the accuracy of the estimates of phylogenetic trees, substitution parameters, and rate variation among sites. The MEGA user interface has now been enhanced to be activity driven to make it easier for the use of both beginners and experienced scientists. This version of MEGA is intended for the Windows platform, and it has been configured for effective use on Mac OS X and Linux desktops. It is available free of charge from http://www.megasoftware.net. PMID:21546353

A platform for evolving intelligently interactive adversaries.

PubMed

Fogel, David B; Hays, Timothy J; Johnson, Douglas R

2006-07-01

Entertainment software developers face significant challenges in designing games with broad appeal. One of the challenges concerns creating nonplayer (computer-controlled) characters that can adapt their behavior in light of the current and prospective situation, possibly emulating human behaviors. This adaptation should be inherently novel, unrepeatable, yet within the bounds of realism. Evolutionary algorithms provide a suitable method for generating such behaviors. This paper provides background on the entertainment software industry, and details a prior and current effort to create a platform for evolving nonplayer characters with genetic and behavioral traits within a World War I combat flight simulator.

Approaches to understanding the impact of life-history features on plant-pathogen co-evolutionary dynamics

Treesearch

Jeremy J. Burdon; Peter H. Thrall; Adnane Nemri

2012-01-01

Natural plant-pathogen associations are complex interactions in which the interplay of environment, host, and pathogen factors results in spatially heterogeneous ecological and epidemiological dynamics. The evolutionary patterns that result from the interaction of these factors are still relatively poorly understood. Recently, integration of the appropriate spatial and...

Predicting domain-domain interaction based on domain profiles with feature selection and support vector machines

PubMed Central

2010-01-01

Background Protein-protein interaction (PPI) plays essential roles in cellular functions. The cost, time and other limitations associated with the current experimental methods have motivated the development of computational methods for predicting PPIs. As protein interactions generally occur via domains instead of the whole molecules, predicting domain-domain interaction (DDI) is an important step toward PPI prediction. Computational methods developed so far have utilized information from various sources at different levels, from primary sequences, to molecular structures, to evolutionary profiles. Results In this paper, we propose a computational method to predict DDI using support vector machines (SVMs), based on domains represented as interaction profile hidden Markov models (ipHMM) where interacting residues in domains are explicitly modeled according to the three dimensional structural information available at the Protein Data Bank (PDB). Features about the domains are extracted first as the Fisher scores derived from the ipHMM and then selected using singular value decomposition (SVD). Domain pairs are represented by concatenating their selected feature vectors, and classified by a support vector machine trained on these feature vectors. The method is tested by leave-one-out cross validation experiments with a set of interacting protein pairs adopted from the 3DID database. The prediction accuracy has shown significant improvement as compared to InterPreTS (Interaction Prediction through Tertiary Structure), an existing method for PPI prediction that also uses the sequences and complexes of known 3D structure. Conclusions We show that domain-domain interaction prediction can be significantly enhanced by exploiting information inherent in the domain profiles via feature selection based on Fisher scores, singular value decomposition and supervised learning based on support vector machines. Datasets and source code are freely available on the web at http://liao.cis.udel.edu/pub/svdsvm. Implemented in Matlab and supported on Linux and MS Windows. PMID:21034480

Eco-evolutionary feedbacks drive species interactions

PubMed Central

Andrade-Domínguez, Andrés; Salazar, Emmanuel; del Carmen Vargas-Lagunas, María; Kolter, Roberto; Encarnación, Sergio

2014-01-01

In the biosphere, many species live in close proximity and can thus interact in many different ways. Such interactions are dynamic and fall along a continuum between antagonism and cooperation. Because interspecies interactions are the key to understanding biological communities, it is important to know how species interactions arise and evolve. Here, we show that the feedback between ecological and evolutionary processes has a fundamental role in the emergence and dynamics of species interaction. Using a two-species artificial community, we demonstrate that ecological processes and rapid evolution interact to influence the dynamics of the symbiosis between a eukaryote (Saccharomyces cerevisiae) and a bacterium (Rhizobium etli). The simplicity of our experimental design enables an explicit statement of causality. The niche-constructing activities of the fungus were the key ecological process: it allowed the establishment of a commensal relationship that switched to ammensalism and provided the selective conditions necessary for the adaptive evolution of the bacteria. In this latter state, the bacterial population radiates into more than five genotypes that vary with respect to nutrient transport, metabolic strategies and global regulation. Evolutionary diversification of the bacterial populations has strong effects on the community; the nature of interaction subsequently switches from ammensalism to antagonism where bacteria promote yeast extinction. Our results demonstrate the importance of the evolution-to-ecology pathway in the persistence of interactions and the stability of communities. Thus, eco-evolutionary dynamics have the potential to transform the structure and functioning of ecosystems. Our results suggest that these dynamics should be considered to improve our understanding of beneficial and detrimental host–microbe interactions. PMID:24304674

The protein-protein interface evolution acts in a similar way to antibody affinity maturation.

PubMed

Li, Bohua; Zhao, Lei; Wang, Chong; Guo, Huaizu; Wu, Lan; Zhang, Xunming; Qian, Weizhu; Wang, Hao; Guo, Yajun

2010-02-05

Understanding the evolutionary mechanism that acts at the interfaces of protein-protein complexes is a fundamental issue with high interest for delineating the macromolecular complexes and networks responsible for regulation and complexity in biological systems. To investigate whether the evolution of protein-protein interface acts in a similar way as antibody affinity maturation, we incorporated evolutionary information derived from antibody affinity maturation with common simulation techniques to evaluate prediction success rates of the computational method in affinity improvement in four different systems: antibody-receptor, antibody-peptide, receptor-membrane ligand, and receptor-soluble ligand. It was interesting to find that the same evolutionary information could improve the prediction success rates in all the four protein-protein complexes with an exceptional high accuracy (>57%). One of the most striking findings in our present study is that not only in the antibody-combining site but in other protein-protein interfaces almost all of the affinity-enhancing mutations are located at the germline hotspot sequences (RGYW or WA), indicating that DNA hot spot mechanisms may be widely used in the evolution of protein-protein interfaces. Our data suggest that the evolution of distinct protein-protein interfaces may use the same basic strategy under selection pressure to maintain interactions. Additionally, our data indicate that classical simulation techniques incorporating the evolutionary information derived from in vivo antibody affinity maturation can be utilized as a powerful tool to improve the binding affinity of protein-protein complex with a high accuracy.

Insights into the origin and distribution of biodiversity in the Brazilian Atlantic forest hot spot: a statistical phylogeographic study using a low-dispersal organism.

PubMed

Álvarez-Presas, M; Sánchez-Gracia, A; Carbayo, F; Rozas, J; Riutort, M

2014-06-01

The relative importance of the processes that generate and maintain biodiversity is a major and controversial topic in evolutionary biology with large implications for conservation management. The Atlantic Forest of Brazil, one of the world's richest biodiversity hot spots, is severely damaged by human activities. To formulate an efficient conservation policy, a good understanding of spatial and temporal biodiversity patterns and their underlying evolutionary mechanisms is required. With this aim, we performed a comprehensive phylogeographic study using a low-dispersal organism, the land planarian species Cephaloflexa bergi (Platyhelminthes, Tricladida). Analysing multi-locus DNA sequence variation under the Approximate Bayesian Computation framework, we evaluated two scenarios proposed to explain the diversity of Southern Atlantic Forest (SAF) region. We found that most sampled localities harbour high levels of genetic diversity, with lineages sharing common ancestors that predate the Pleistocene. Remarkably, we detected the molecular hallmark of the isolation-by-distance effect and little evidence of a recent colonization of SAF localities; nevertheless, some populations might result from very recent secondary contacts. We conclude that extant SAF biodiversity originated and has been shaped by complex interactions between ancient geological events and more recent evolutionary processes, whereas Pleistocene climate changes had a minor influence in generating present-day diversity. We also demonstrate that land planarians are an advantageous biological model for making phylogeographic and, particularly, fine-scale evolutionary inferences, and propose appropriate conservation policies.

The evolution of distributed sensing and collective computation in animal populations

PubMed Central

Hein, Andrew M; Rosenthal, Sara Brin; Hagstrom, George I; Berdahl, Andrew; Torney, Colin J; Couzin, Iain D

2015-01-01

Many animal groups exhibit rapid, coordinated collective motion. Yet, the evolutionary forces that cause such collective responses to evolve are poorly understood. Here, we develop analytical methods and evolutionary simulations based on experimental data from schooling fish. We use these methods to investigate how populations evolve within unpredictable, time-varying resource environments. We show that populations evolve toward a distinctive regime in behavioral phenotype space, where small responses of individuals to local environmental cues cause spontaneous changes in the collective state of groups. These changes resemble phase transitions in physical systems. Through these transitions, individuals evolve the emergent capacity to sense and respond to resource gradients (i.e. individuals perceive gradients via social interactions, rather than sensing gradients directly), and to allocate themselves among distinct, distant resource patches. Our results yield new insight into how natural selection, acting on selfish individuals, results in the highly effective collective responses evident in nature. DOI: http://dx.doi.org/10.7554/eLife.10955.001 PMID:26652003

Toward Optimal Transport Networks

NASA Technical Reports Server (NTRS)

Alexandrov, Natalia; Kincaid, Rex K.; Vargo, Erik P.

2008-01-01

Strictly evolutionary approaches to improving the air transport system a highly complex network of interacting systems no longer suffice in the face of demand that is projected to double or triple in the near future. Thus evolutionary approaches should be augmented with active design methods. The ability to actively design, optimize and control a system presupposes the existence of predictive modeling and reasonably well-defined functional dependences between the controllable variables of the system and objective and constraint functions for optimization. Following recent advances in the studies of the effects of network topology structure on dynamics, we investigate the performance of dynamic processes on transport networks as a function of the first nontrivial eigenvalue of the network's Laplacian, which, in turn, is a function of the network s connectivity and modularity. The last two characteristics can be controlled and tuned via optimization. We consider design optimization problem formulations. We have developed a flexible simulation of network topology coupled with flows on the network for use as a platform for computational experiments.

Regulatory RNA design through evolutionary computation and strand displacement.

PubMed

Rostain, William; Landrain, Thomas E; Rodrigo, Guillermo; Jaramillo, Alfonso

2015-01-01

The discovery and study of a vast number of regulatory RNAs in all kingdoms of life over the past decades has allowed the design of new synthetic RNAs that can regulate gene expression in vivo. Riboregulators, in particular, have been used to activate or repress gene expression. However, to accelerate and scale up the design process, synthetic biologists require computer-assisted design tools, without which riboregulator engineering will remain a case-by-case design process requiring expert attention. Recently, the design of RNA circuits by evolutionary computation and adapting strand displacement techniques from nanotechnology has proven to be suited to the automated generation of DNA sequences implementing regulatory RNA systems in bacteria. Herein, we present our method to carry out such evolutionary design and how to use it to create various types of riboregulators, allowing the systematic de novo design of genetic control systems in synthetic biology.

Genomicus 2018: karyotype evolutionary trees and on-the-fly synteny computing

PubMed Central

Nguyen, Nga Thi Thuy; Vincens, Pierre

2018-01-01

Abstract Since 2010, the Genomicus web server is available online at http://genomicus.biologie.ens.fr/genomicus. This graphical browser provides access to comparative genomic analyses in four different phyla (Vertebrate, Plants, Fungi, and non vertebrate Metazoans). Users can analyse genomic information from extant species, as well as ancestral gene content and gene order for vertebrates and flowering plants, in an integrated evolutionary context. New analyses and visualization tools have recently been implemented in Genomicus Vertebrate. Karyotype structures from several genomes can now be compared along an evolutionary pathway (Multi-KaryotypeView), and synteny blocks can be computed and visualized between any two genomes (PhylDiagView). PMID:29087490

Two-Drug Antimicrobial Chemotherapy: A Mathematical Model and Experiments with Mycobacterium marinum

PubMed Central

Ankomah, Peter; Levin, Bruce R.

2012-01-01

Multi-drug therapy is the standard-of-care treatment for tuberculosis. Despite this, virtually all studies of the pharmacodynamics (PD) of mycobacterial drugs employed for the design of treatment protocols are restricted to single agents. In this report, mathematical models and in vitro experiments with Mycobacterium marinum and five antimycobacterial drugs are used to quantitatively evaluate the pharmaco-, population and evolutionary dynamics of two-drug antimicrobial chemotherapy regimes. Time kill experiments with single and pairs of antibiotics are used to estimate the parameters and evaluate the fit of Hill-function-based PD models. While Hill functions provide excellent fits for the PD of each single antibiotic studied, rifampin, amikacin, clarithromycin, streptomycin and moxifloxacin, two-drug Hill functions with a unique interaction parameter cannot account for the PD of any of the 10 pairs of these drugs. If we assume two antibiotic-concentration dependent functions for the interaction parameter, one for sub-MIC and one for supra-MIC drug concentrations, the modified biphasic Hill function provides a reasonably good fit for the PD of all 10 pairs of antibiotics studied. Monte Carlo simulations of antibiotic treatment based on the experimentally-determined PD functions are used to evaluate the potential microbiological efficacy (rate of clearance) and evolutionary consequences (likelihood of generating multi-drug resistance) of these different drug combinations as well as their sensitivity to different forms of non-adherence to therapy. These two-drug treatment simulations predict varying outcomes for the different pairs of antibiotics with respect to the aforementioned measures of efficacy. In summary, Hill functions with biphasic drug-drug interaction terms provide accurate analogs for the PD of pairs of antibiotics and M. marinum. The models, experimental protocols and computer simulations used in this study can be applied to evaluate the potential microbiological and evolutionary efficacy of two-drug therapy for any bactericidal antibiotics and bacteria that can be cultured in vitro. PMID:22253599

Non-Evolutionary Algorithms for Scheduling Dependent Tasks in Distributed Heterogeneous Computing Environments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wayne F. Boyer; Gurdeep S. Hura

2005-09-01

The Problem of obtaining an optimal matching and scheduling of interdependent tasks in distributed heterogeneous computing (DHC) environments is well known to be an NP-hard problem. In a DHC system, task execution time is dependent on the machine to which it is assigned and task precedence constraints are represented by a directed acyclic graph. Recent research in evolutionary techniques has shown that genetic algorithms usually obtain more efficient schedules that other known algorithms. We propose a non-evolutionary random scheduling (RS) algorithm for efficient matching and scheduling of inter-dependent tasks in a DHC system. RS is a succession of randomized taskmore » orderings and a heuristic mapping from task order to schedule. Randomized task ordering is effectively a topological sort where the outcome may be any possible task order for which the task precedent constraints are maintained. A detailed comparison to existing evolutionary techniques (GA and PSGA) shows the proposed algorithm is less complex than evolutionary techniques, computes schedules in less time, requires less memory and fewer tuning parameters. Simulation results show that the average schedules produced by RS are approximately as efficient as PSGA schedules for all cases studied and clearly more efficient than PSGA for certain cases. The standard formulation for the scheduling problem addressed in this paper is Rm|prec|Cmax.,« less

The relative importance of rapid evolution for plant-microbe interactions depends on ecological context.

PubMed

Terhorst, Casey P; Lennon, Jay T; Lau, Jennifer A

2014-06-22

Evolution can occur on ecological time-scales, affecting community and ecosystem processes. However, the importance of evolutionary change relative to ecological processes remains largely unknown. Here, we analyse data from a long-term experiment in which we allowed plant populations to evolve for three generations in dry or wet soils and used a reciprocal transplant to compare the ecological effect of drought and the effect of plant evolutionary responses to drought on soil microbial communities and nutrient availability. Plants that evolved under drought tended to support higher bacterial and fungal richness, and increased fungal : bacterial ratios in the soil. Overall, the magnitudes of ecological and evolutionary effects on microbial communities were similar; however, the strength and direction of these effects depended on the context in which they were measured. For example, plants that evolved in dry environments increased bacterial abundance in dry contemporary environments, but decreased bacterial abundance in wet contemporary environments. Our results suggest that interactions between recent evolutionary history and ecological context affect both the direction and magnitude of plant effects on soil microbes. Consequently, an eco-evolutionary perspective is required to fully understand plant-microbe interactions.

A single determinant dominates the rate of yeast protein evolution.

PubMed

Drummond, D Allan; Raval, Alpan; Wilke, Claus O

2006-02-01

A gene's rate of sequence evolution is among the most fundamental evolutionary quantities in common use, but what determines evolutionary rates has remained unclear. Here, we carry out the first combined analysis of seven predictors (gene expression level, dispensability, protein abundance, codon adaptation index, gene length, number of protein-protein interactions, and the gene's centrality in the interaction network) previously reported to have independent influences on protein evolutionary rates. Strikingly, our analysis reveals a single dominant variable linked to the number of translation events which explains 40-fold more variation in evolutionary rate than any other, suggesting that protein evolutionary rate has a single major determinant among the seven predictors. The dominant variable explains nearly half the variation in the rate of synonymous and protein evolution. We show that the two most commonly used methods to disentangle the determinants of evolutionary rate, partial correlation analysis and ordinary multivariate regression, produce misleading or spurious results when applied to noisy biological data. We overcome these difficulties by employing principal component regression, a multivariate regression of evolutionary rate against the principal components of the predictor variables. Our results support the hypothesis that translational selection governs the rate of synonymous and protein sequence evolution in yeast.

Female behaviour and the interaction of male and female genital traits mediate sperm transfer during mating.

PubMed

Friesen, C R; Uhrig, E J; Mason, R T; Brennan, P L R

2016-05-01

Natural selection and post-copulatory sexual selection, including sexual conflict, contribute to genital diversification. Fundamental first steps in understanding how these processes shape the evolution of specific genital traits are to determine their function experimentally and to understand the interactions between female and male genitalia during copulation. Our experimental manipulations of male and female genitalia in red-sided garter snakes (Thamnophis sirtalis parietalis) reveal that copulation duration and copulatory plug deposition, as well as total and oviductal/vaginal sperm counts, are influenced by the interaction between male and female genital traits and female behaviour during copulation. By mating females with anesthetized cloacae to males with spine-ablated hemipenes using a fully factorial design, we identified significant female-male copulatory trait interactions and found that females prevent sperm from entering their oviducts by contracting their vaginal pouch. Furthermore, these muscular contractions limit copulatory plug size, whereas the basal spine of the male hemipene aids in sperm and plug transfer. Our results are consistent with a role of sexual conflict in mating interactions and highlight the evolutionary importance of female resistance to reproductive outcomes. © 2016 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2016 European Society For Evolutionary Biology.

Optimality and stability of symmetric evolutionary games with applications in genetic selection.

PubMed

Huang, Yuanyuan; Hao, Yiping; Wang, Min; Zhou, Wen; Wu, Zhijun

2015-06-01

Symmetric evolutionary games, i.e., evolutionary games with symmetric fitness matrices, have important applications in population genetics, where they can be used to model for example the selection and evolution of the genotypes of a given population. In this paper, we review the theory for obtaining optimal and stable strategies for symmetric evolutionary games, and provide some new proofs and computational methods. In particular, we review the relationship between the symmetric evolutionary game and the generalized knapsack problem, and discuss the first and second order necessary and sufficient conditions that can be derived from this relationship for testing the optimality and stability of the strategies. Some of the conditions are given in different forms from those in previous work and can be verified more efficiently. We also derive more efficient computational methods for the evaluation of the conditions than conventional approaches. We demonstrate how these conditions can be applied to justifying the strategies and their stabilities for a special class of genetic selection games including some in the study of genetic disorders.

A Systematic Prediction of Drug-Target Interactions Using Molecular Fingerprints and Protein Sequences.

PubMed

Huang, Yu-An; You, Zhu-Hong; Chen, Xing

2018-01-01

Drug-Target Interactions (DTI) play a crucial role in discovering new drug candidates and finding new proteins to target for drug development. Although the number of detected DTI obtained by high-throughput techniques has been increasing, the number of known DTI is still limited. On the other hand, the experimental methods for detecting the interactions among drugs and proteins are costly and inefficient. Therefore, computational approaches for predicting DTI are drawing increasing attention in recent years. In this paper, we report a novel computational model for predicting the DTI using extremely randomized trees model and protein amino acids information. More specifically, the protein sequence is represented as a Pseudo Substitution Matrix Representation (Pseudo-SMR) descriptor in which the influence of biological evolutionary information is retained. For the representation of drug molecules, a novel fingerprint feature vector is utilized to describe its substructure information. Then the DTI pair is characterized by concatenating the two vector spaces of protein sequence and drug substructure. Finally, the proposed method is explored for predicting the DTI on four benchmark datasets: Enzyme, Ion Channel, GPCRs and Nuclear Receptor. The experimental results demonstrate that this method achieves promising prediction accuracies of 89.85%, 87.87%, 82.99% and 81.67%, respectively. For further evaluation, we compared the performance of Extremely Randomized Trees model with that of the state-of-the-art Support Vector Machine classifier. And we also compared the proposed model with existing computational models, and confirmed 15 potential drug-target interactions by looking for existing databases. The experiment results show that the proposed method is feasible and promising for predicting drug-target interactions for new drug candidate screening based on sizeable features. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Contemporary and historical evolutionary processes interact to shape patterns of within-lake phenotypic divergences in polyphenic pumpkinseed sunfish, Lepomis gibbosus.

PubMed

Weese, Dylan J; Ferguson, Moira M; Robinson, Beren W

2012-03-01

Historical and contemporary evolutionary processes can both contribute to patterns of phenotypic variation among populations of a species. Recent studies are revealing how interactions between historical and contemporary processes better explain observed patterns of phenotypic divergence than either process alone. Here, we investigate the roles of evolutionary history and adaptation to current environmental conditions in structuring phenotypic variation among polyphenic populations of sunfish inhabiting 12 postglacial lakes in eastern North America. The pumpkinseed sunfish polyphenism includes sympatric ecomorphs specialized for littoral or pelagic lake habitats. First, we use population genetic methods to test the evolutionary independence of within-lake phenotypic divergences of ecomorphs and to describe patterns of genetic structure among lake populations that clustered into three geographical groupings. We then used multivariate analysis of covariance (MANCOVA) to partition body shape variation (quantified with geometric morphometrics) among the effects of evolutionary history (reflecting phenotypic variation among genetic clusters), the shared phenotypic response of all populations to alternate habitats within lakes (reflecting adaptation to contemporary conditions), and unique phenotypic responses to habitats within lakes nested within genetic clusters. All effects had a significant influence on body form, but the effects of history and the interaction between history and contemporary habitat were larger than contemporary processes in structuring phenotypic variation. This highlights how divergence can be better understood against a known backdrop of evolutionary history.

Antibiotic resistance in the wild: an eco-evolutionary perspective.

PubMed

Hiltunen, Teppo; Virta, Marko; Laine, Anna-Liisa

2017-01-19

The legacy of the use and misuse of antibiotics in recent decades has left us with a global public health crisis: antibiotic-resistant bacteria are on the rise, making it harder to treat infections. At the same time, evolution of antibiotic resistance is probably the best-documented case of contemporary evolution. To date, research on antibiotic resistance has largely ignored the complexity of interactions that bacteria engage in. However, in natural populations, bacteria interact with other species; for example, competition and grazing are import interactions influencing bacterial population dynamics. Furthermore, antibiotic leakage to natural environments can radically alter bacterial communities. Overall, we argue that eco-evolutionary feedback loops in microbial communities can be modified by residual antibiotics and evolution of antibiotic resistance. The aim of this review is to connect some of the well-established key concepts in evolutionary biology and recent advances in the study of eco-evolutionary dynamics to research on antibiotic resistance. We also identify some key knowledge gaps related to eco-evolutionary dynamics of antibiotic resistance, and review some of the recent technical advantages in molecular microbiology that offer new opportunities for tackling these questions. Finally, we argue that using the full potential of evolutionary theory and active communication across the different fields is needed for solving this global crisis more efficiently.This article is part of the themed issue 'Human influences on evolution, and the ecological and societal consequences'. © 2016 The Authors.

Antibiotic resistance in the wild: an eco-evolutionary perspective

PubMed Central

Virta, Marko

2017-01-01

The legacy of the use and misuse of antibiotics in recent decades has left us with a global public health crisis: antibiotic-resistant bacteria are on the rise, making it harder to treat infections. At the same time, evolution of antibiotic resistance is probably the best-documented case of contemporary evolution. To date, research on antibiotic resistance has largely ignored the complexity of interactions that bacteria engage in. However, in natural populations, bacteria interact with other species; for example, competition and grazing are import interactions influencing bacterial population dynamics. Furthermore, antibiotic leakage to natural environments can radically alter bacterial communities. Overall, we argue that eco-evolutionary feedback loops in microbial communities can be modified by residual antibiotics and evolution of antibiotic resistance. The aim of this review is to connect some of the well-established key concepts in evolutionary biology and recent advances in the study of eco-evolutionary dynamics to research on antibiotic resistance. We also identify some key knowledge gaps related to eco-evolutionary dynamics of antibiotic resistance, and review some of the recent technical advantages in molecular microbiology that offer new opportunities for tackling these questions. Finally, we argue that using the full potential of evolutionary theory and active communication across the different fields is needed for solving this global crisis more efficiently. This article is part of the themed issue ‘Human influences on evolution, and the ecological and societal consequences'. PMID:27920384

Evolutionary inference via the Poisson Indel Process.

PubMed

Bouchard-Côté, Alexandre; Jordan, Michael I

2013-01-22

We address the problem of the joint statistical inference of phylogenetic trees and multiple sequence alignments from unaligned molecular sequences. This problem is generally formulated in terms of string-valued evolutionary processes along the branches of a phylogenetic tree. The classic evolutionary process, the TKF91 model [Thorne JL, Kishino H, Felsenstein J (1991) J Mol Evol 33(2):114-124] is a continuous-time Markov chain model composed of insertion, deletion, and substitution events. Unfortunately, this model gives rise to an intractable computational problem: The computation of the marginal likelihood under the TKF91 model is exponential in the number of taxa. In this work, we present a stochastic process, the Poisson Indel Process (PIP), in which the complexity of this computation is reduced to linear. The Poisson Indel Process is closely related to the TKF91 model, differing only in its treatment of insertions, but it has a global characterization as a Poisson process on the phylogeny. Standard results for Poisson processes allow key computations to be decoupled, which yields the favorable computational profile of inference under the PIP model. We present illustrative experiments in which Bayesian inference under the PIP model is compared with separate inference of phylogenies and alignments.

Evolutionary inference via the Poisson Indel Process

PubMed Central

Bouchard-Côté, Alexandre; Jordan, Michael I.

2013-01-01

We address the problem of the joint statistical inference of phylogenetic trees and multiple sequence alignments from unaligned molecular sequences. This problem is generally formulated in terms of string-valued evolutionary processes along the branches of a phylogenetic tree. The classic evolutionary process, the TKF91 model [Thorne JL, Kishino H, Felsenstein J (1991) J Mol Evol 33(2):114–124] is a continuous-time Markov chain model composed of insertion, deletion, and substitution events. Unfortunately, this model gives rise to an intractable computational problem: The computation of the marginal likelihood under the TKF91 model is exponential in the number of taxa. In this work, we present a stochastic process, the Poisson Indel Process (PIP), in which the complexity of this computation is reduced to linear. The Poisson Indel Process is closely related to the TKF91 model, differing only in its treatment of insertions, but it has a global characterization as a Poisson process on the phylogeny. Standard results for Poisson processes allow key computations to be decoupled, which yields the favorable computational profile of inference under the PIP model. We present illustrative experiments in which Bayesian inference under the PIP model is compared with separate inference of phylogenies and alignments. PMID:23275296

VizieR Online Data Catalog: Comparison of evolutionary tracks (Martins+, 2013)

NASA Astrophysics Data System (ADS)

Martins, F.; Palacios, A.

2013-11-01

Tables of evolutionary models for massive stars. The files m*_stol.dat correspond to models computed with the code STAREVOL. The files m*_mesa.dat correspond to models computed with the code MESA. For each code, models with initial masses equal to 7, 9, 15, 20, 25, 40 and 60M⊙ are provided. No rotation is included. The overshooting parameter f is equal to 0.01. The metallicity is solar. (14 data files).

Evolutionary, structural and biochemical evidence for a new interaction site of the leptin obesity protein

NASA Technical Reports Server (NTRS)

Gaucher, Eric A.; Miyamoto, Michael M.; Benner, Steven A.

2003-01-01

The Leptin protein is central to the regulation of energy metabolism in mammals. By integrating evolutionary, structural, and biochemical information, a surface segment, outside of its known receptor contacts, is predicted as a second interaction site that may help to further define its roles in energy balance and its functional differences between humans and other mammals.

Accurate prediction of protein-protein interactions by integrating potential evolutionary information embedded in PSSM profile and discriminative vector machine classifier.

PubMed

Li, Zheng-Wei; You, Zhu-Hong; Chen, Xing; Li, Li-Ping; Huang, De-Shuang; Yan, Gui-Ying; Nie, Ru; Huang, Yu-An

2017-04-04

Identification of protein-protein interactions (PPIs) is of critical importance for deciphering the underlying mechanisms of almost all biological processes of cell and providing great insight into the study of human disease. Although much effort has been devoted to identifying PPIs from various organisms, existing high-throughput biological techniques are time-consuming, expensive, and have high false positive and negative results. Thus it is highly urgent to develop in silico methods to predict PPIs efficiently and accurately in this post genomic era. In this article, we report a novel computational model combining our newly developed discriminative vector machine classifier (DVM) and an improved Weber local descriptor (IWLD) for the prediction of PPIs. Two components, differential excitation and orientation, are exploited to build evolutionary features for each protein sequence. The main characteristics of the proposed method lies in introducing an effective feature descriptor IWLD which can capture highly discriminative evolutionary information from position-specific scoring matrixes (PSSM) of protein data, and employing the powerful and robust DVM classifier. When applying the proposed method to Yeast and H. pylori data sets, we obtained excellent prediction accuracies as high as 96.52% and 91.80%, respectively, which are significantly better than the previous methods. Extensive experiments were then performed for predicting cross-species PPIs and the predictive results were also pretty promising. To further validate the performance of the proposed method, we compared it with the state-of-the-art support vector machine (SVM) classifier on Human data set. The experimental results obtained indicate that our method is highly effective for PPIs prediction and can be taken as a supplementary tool for future proteomics research.

Inference of Transmission Network Structure from HIV Phylogenetic Trees

DOE PAGES

Giardina, Federica; Romero-Severson, Ethan Obie; Albert, Jan; ...

2017-01-13

Phylogenetic inference is an attractive means to reconstruct transmission histories and epidemics. However, there is not a perfect correspondence between transmission history and virus phylogeny. Both node height and topological differences may occur, depending on the interaction between within-host evolutionary dynamics and between-host transmission patterns. To investigate these interactions, we added a within-host evolutionary model in epidemiological simulations and examined if the resulting phylogeny could recover different types of contact networks. To further improve realism, we also introduced patient-specific differences in infectivity across disease stages, and on the epidemic level we considered incomplete sampling and the age of the epidemic.more » Second, we implemented an inference method based on approximate Bayesian computation (ABC) to discriminate among three well-studied network models and jointly estimate both network parameters and key epidemiological quantities such as the infection rate. Our ABC framework used both topological and distance-based tree statistics for comparison between simulated and observed trees. Overall, our simulations showed that a virus time-scaled phylogeny (genealogy) may be substantially different from the between-host transmission tree. This has important implications for the interpretation of what a phylogeny reveals about the underlying epidemic contact network. In particular, we found that while the within-host evolutionary process obscures the transmission tree, the diversification process and infectivity dynamics also add discriminatory power to differentiate between different types of contact networks. We also found that the possibility to differentiate contact networks depends on how far an epidemic has progressed, where distance-based tree statistics have more power early in an epidemic. Finally, we applied our ABC inference on two different outbreaks from the Swedish HIV-1 epidemic.« less

Inference of Transmission Network Structure from HIV Phylogenetic Trees

DOE Office of Scientific and Technical Information (OSTI.GOV)

Giardina, Federica; Romero-Severson, Ethan Obie; Albert, Jan

Phylogenetic inference is an attractive means to reconstruct transmission histories and epidemics. However, there is not a perfect correspondence between transmission history and virus phylogeny. Both node height and topological differences may occur, depending on the interaction between within-host evolutionary dynamics and between-host transmission patterns. To investigate these interactions, we added a within-host evolutionary model in epidemiological simulations and examined if the resulting phylogeny could recover different types of contact networks. To further improve realism, we also introduced patient-specific differences in infectivity across disease stages, and on the epidemic level we considered incomplete sampling and the age of the epidemic.more » Second, we implemented an inference method based on approximate Bayesian computation (ABC) to discriminate among three well-studied network models and jointly estimate both network parameters and key epidemiological quantities such as the infection rate. Our ABC framework used both topological and distance-based tree statistics for comparison between simulated and observed trees. Overall, our simulations showed that a virus time-scaled phylogeny (genealogy) may be substantially different from the between-host transmission tree. This has important implications for the interpretation of what a phylogeny reveals about the underlying epidemic contact network. In particular, we found that while the within-host evolutionary process obscures the transmission tree, the diversification process and infectivity dynamics also add discriminatory power to differentiate between different types of contact networks. We also found that the possibility to differentiate contact networks depends on how far an epidemic has progressed, where distance-based tree statistics have more power early in an epidemic. Finally, we applied our ABC inference on two different outbreaks from the Swedish HIV-1 epidemic.« less

NexGen PVAs: Incorporating Eco-Evolutionary Processes into Population Viability Models

EPA Science Inventory

We examine how the integration of evolutionary and ecological processes in population dynamics – an emerging framework in ecology – could be incorporated into population viability analysis (PVA). Driven by parallel, complementary advances in population genomics and computational ...

Biophysics and systems biology.

PubMed

Noble, Denis

2010-03-13

Biophysics at the systems level, as distinct from molecular biophysics, acquired its most famous paradigm in the work of Hodgkin and Huxley, who integrated their equations for the nerve impulse in 1952. Their approach has since been extended to other organs of the body, notably including the heart. The modern field of computational biology has expanded rapidly during the first decade of the twenty-first century and, through its contribution to what is now called systems biology, it is set to revise many of the fundamental principles of biology, including the relations between genotypes and phenotypes. Evolutionary theory, in particular, will require re-assessment. To succeed in this, computational and systems biology will need to develop the theoretical framework required to deal with multilevel interactions. While computational power is necessary, and is forthcoming, it is not sufficient. We will also require mathematical insight, perhaps of a nature we have not yet identified. This article is therefore also a challenge to mathematicians to develop such insights.

Biophysics and systems biology

PubMed Central

Noble, Denis

2010-01-01

Biophysics at the systems level, as distinct from molecular biophysics, acquired its most famous paradigm in the work of Hodgkin and Huxley, who integrated their equations for the nerve impulse in 1952. Their approach has since been extended to other organs of the body, notably including the heart. The modern field of computational biology has expanded rapidly during the first decade of the twenty-first century and, through its contribution to what is now called systems biology, it is set to revise many of the fundamental principles of biology, including the relations between genotypes and phenotypes. Evolutionary theory, in particular, will require re-assessment. To succeed in this, computational and systems biology will need to develop the theoretical framework required to deal with multilevel interactions. While computational power is necessary, and is forthcoming, it is not sufficient. We will also require mathematical insight, perhaps of a nature we have not yet identified. This article is therefore also a challenge to mathematicians to develop such insights. PMID:20123750

Digital Poetry: A Narrow Relation between Poetics and the Codes of the Computational Logic

NASA Astrophysics Data System (ADS)

Laurentiz, Silvia

The project "Percorrendo Escrituras" (Walking Through Writings Project) has been developed at ECA-USP Fine Arts Department. Summarizing, it intends to study different structures of digital information that share the same universe and are generators of a new aesthetics condition. The aim is to search which are the expressive possibilities of the computer among the algorithm functions and other of its specific properties. It is a practical, theoretical and interdisciplinary project where the study of programming evolutionary language, logic and mathematics take us to poetic experimentations. The focus of this research is the digital poetry, and it comes from poetics of permutation combinations and culminates with dynamic and complex systems, autonomous, multi-user and interactive, through agents generation derivations, filtration and emergent standards. This lecture will present artworks that use some mechanisms introduced by cybernetics and the notion of system in digital poetry that demonstrate the narrow relationship between poetics and the codes of computational logic.

Computer optimization techniques for NASA Langley's CSI evolutionary model's real-time control system

NASA Technical Reports Server (NTRS)

Elliott, Kenny B.; Ugoletti, Roberto; Sulla, Jeff

1992-01-01

The evolution and optimization of a real-time digital control system is presented. The control system is part of a testbed used to perform focused technology research on the interactions of spacecraft platform and instrument controllers with the flexible-body dynamics of the platform and platform appendages. The control system consists of Computer Automated Measurement and Control (CAMAC) standard data acquisition equipment interfaced to a workstation computer. The goal of this work is to optimize the control system's performance to support controls research using controllers with up to 50 states and frame rates above 200 Hz. The original system could support a 16-state controller operating at a rate of 150 Hz. By using simple yet effective software improvements, Input/Output (I/O) latencies and contention problems are reduced or eliminated in the control system. The final configuration can support a 16-state controller operating at 475 Hz. Effectively the control system's performance was increased by a factor of 3.

RegulonDB version 9.0: high-level integration of gene regulation, coexpression, motif clustering and beyond

PubMed Central

Gama-Castro, Socorro; Salgado, Heladia; Santos-Zavaleta, Alberto; Ledezma-Tejeida, Daniela; Muñiz-Rascado, Luis; García-Sotelo, Jair Santiago; Alquicira-Hernández, Kevin; Martínez-Flores, Irma; Pannier, Lucia; Castro-Mondragón, Jaime Abraham; Medina-Rivera, Alejandra; Solano-Lira, Hilda; Bonavides-Martínez, César; Pérez-Rueda, Ernesto; Alquicira-Hernández, Shirley; Porrón-Sotelo, Liliana; López-Fuentes, Alejandra; Hernández-Koutoucheva, Anastasia; Moral-Chávez, Víctor Del; Rinaldi, Fabio; Collado-Vides, Julio

2016-01-01

RegulonDB (http://regulondb.ccg.unam.mx) is one of the most useful and important resources on bacterial gene regulation,as it integrates the scattered scientific knowledge of the best-characterized organism, Escherichia coli K-12, in a database that organizes large amounts of data. Its electronic format enables researchers to compare their results with the legacy of previous knowledge and supports bioinformatics tools and model building. Here, we summarize our progress with RegulonDB since our last Nucleic Acids Research publication describing RegulonDB, in 2013. In addition to maintaining curation up-to-date, we report a collection of 232 interactions with small RNAs affecting 192 genes, and the complete repertoire of 189 Elementary Genetic Sensory-Response units (GENSOR units), integrating the signal, regulatory interactions, and metabolic pathways they govern. These additions represent major progress to a higher level of understanding of regulated processes. We have updated the computationally predicted transcription factors, which total 304 (184 with experimental evidence and 120 from computational predictions); we updated our position-weight matrices and have included tools for clustering them in evolutionary families. We describe our semiautomatic strategy to accelerate curation, including datasets from high-throughput experiments, a novel coexpression distance to search for ‘neighborhood’ genes to known operons and regulons, and computational developments. PMID:26527724

Merging molecular mechanism and evolution: theory and computation at the interface of biophysics and evolutionary population genetics

PubMed Central

Serohijos, Adrian W.R.; Shakhnovich, Eugene I.

2014-01-01

The variation among sequences and structures in nature is both determined by physical laws and by evolutionary history. However, these two factors are traditionally investigated by disciplines with different emphasis and philosophy—molecular biophysics on one hand and evolutionary population genetics in another. Here, we review recent theoretical and computational approaches that address the critical need to integrate these two disciplines. We first articulate the elements of these integrated approaches. Then, we survey their contribution to our mechanistic understanding of molecular evolution, the polymorphisms in coding region, the distribution of fitness effects (DFE) of mutations, the observed folding stability of proteins in nature, and the distribution of protein folds in genomes. PMID:24952216

Merging molecular mechanism and evolution: theory and computation at the interface of biophysics and evolutionary population genetics.

PubMed

Serohijos, Adrian W R; Shakhnovich, Eugene I

2014-06-01

The variation among sequences and structures in nature is both determined by physical laws and by evolutionary history. However, these two factors are traditionally investigated by disciplines with different emphasis and philosophy-molecular biophysics on one hand and evolutionary population genetics in another. Here, we review recent theoretical and computational approaches that address the crucial need to integrate these two disciplines. We first articulate the elements of these approaches. Then, we survey their contribution to our mechanistic understanding of molecular evolution, the polymorphisms in coding region, the distribution of fitness effects (DFE) of mutations, the observed folding stability of proteins in nature, and the distribution of protein folds in genomes. Copyright © 2014 Elsevier Ltd. All rights reserved.

Genomicus 2018: karyotype evolutionary trees and on-the-fly synteny computing.

PubMed

Nguyen, Nga Thi Thuy; Vincens, Pierre; Roest Crollius, Hugues; Louis, Alexandra

2018-01-04

Since 2010, the Genomicus web server is available online at http://genomicus.biologie.ens.fr/genomicus. This graphical browser provides access to comparative genomic analyses in four different phyla (Vertebrate, Plants, Fungi, and non vertebrate Metazoans). Users can analyse genomic information from extant species, as well as ancestral gene content and gene order for vertebrates and flowering plants, in an integrated evolutionary context. New analyses and visualization tools have recently been implemented in Genomicus Vertebrate. Karyotype structures from several genomes can now be compared along an evolutionary pathway (Multi-KaryotypeView), and synteny blocks can be computed and visualized between any two genomes (PhylDiagView). © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Phylogenetic tree and community structure from a Tangled Nature model.

PubMed

Canko, Osman; Taşkın, Ferhat; Argın, Kamil

2015-10-07

In evolutionary biology, the taxonomy and origination of species are widely studied subjects. An estimation of the evolutionary tree can be done via available DNA sequence data. The calculation of the tree is made by well-known and frequently used methods such as maximum likelihood and neighbor-joining. In order to examine the results of these methods, an evolutionary tree is pursued computationally by a mathematical model, called Tangled Nature. A relatively small genome space is investigated due to computational burden and it is found that the actual and predicted trees are in reasonably good agreement in terms of shape. Moreover, the speciation and the resulting community structure of the food-web are investigated by modularity. Copyright © 2015 Elsevier Ltd. All rights reserved.

Natural language processing, pragmatics, and verbal behavior

PubMed Central

Cherpas, Chris

1992-01-01

Natural Language Processing (NLP) is that part of Artificial Intelligence (AI) concerned with endowing computers with verbal and listener repertoires, so that people can interact with them more easily. Most attention has been given to accurately parsing and generating syntactic structures, although NLP researchers are finding ways of handling the semantic content of language as well. It is increasingly apparent that understanding the pragmatic (contextual and consequential) dimension of natural language is critical for producing effective NLP systems. While there are some techniques for applying pragmatics in computer systems, they are piecemeal, crude, and lack an integrated theoretical foundation. Unfortunately, there is little awareness that Skinner's (1957) Verbal Behavior provides an extensive, principled pragmatic analysis of language. The implications of Skinner's functional analysis for NLP and for verbal aspects of epistemology lead to a proposal for a “user expert”—a computer system whose area of expertise is the long-term computer user. The evolutionary nature of behavior suggests an AI technology known as genetic algorithms/programming for implementing such a system. ImagesFig. 1 PMID:22477052

PrePhyloPro: phylogenetic profile-based prediction of whole proteome linkages

PubMed Central

Niu, Yulong; Liu, Chengcheng; Moghimyfiroozabad, Shayan; Yang, Yi

2017-01-01

Direct and indirect functional links between proteins as well as their interactions as part of larger protein complexes or common signaling pathways may be predicted by analyzing the correlation of their evolutionary patterns. Based on phylogenetic profiling, here we present a highly scalable and time-efficient computational framework for predicting linkages within the whole human proteome. We have validated this method through analysis of 3,697 human pathways and molecular complexes and a comparison of our results with the prediction outcomes of previously published co-occurrency model-based and normalization methods. Here we also introduce PrePhyloPro, a web-based software that uses our method for accurately predicting proteome-wide linkages. We present data on interactions of human mitochondrial proteins, verifying the performance of this software. PrePhyloPro is freely available at http://prephylopro.org/phyloprofile/. PMID:28875072

A new concept: Epigenetic game theory. Comment on: ;Epigenetic game theory: How to compute the epigenetic control of maternal-to-zygotic transition; by Qian Wang et al.

NASA Astrophysics Data System (ADS)

Zheng, Xiu-Deng; Tao, Yi

2017-03-01

The evolutionary significance of the interaction between paternal and maternal genomes in fertilized zygotes is a very interesting and challenging question. Wang et al. developed the concept of epigenetic game theory, and they try to use this concept to explain the interaction between paternal and maternal genomes in fertilized zygotes [1]. They emphasize that the embryogenesis can be considered as an ecological system in which two highly distinct and specialized gametes coordinate through either cooperation or competition, or both, to maximize the fitness of embryos under Darwinian selection. More specifically, they integrate game theory to model the pattern of coordination of paternal genome and maternal genomes mediated by DNA methylation dynamics, and they called this epigenetic game theory.

Scalable computing for evolutionary genomics.

PubMed

Prins, Pjotr; Belhachemi, Dominique; Möller, Steffen; Smant, Geert

2012-01-01

Genomic data analysis in evolutionary biology is becoming so computationally intensive that analysis of multiple hypotheses and scenarios takes too long on a single desktop computer. In this chapter, we discuss techniques for scaling computations through parallelization of calculations, after giving a quick overview of advanced programming techniques. Unfortunately, parallel programming is difficult and requires special software design. The alternative, especially attractive for legacy software, is to introduce poor man's parallelization by running whole programs in parallel as separate processes, using job schedulers. Such pipelines are often deployed on bioinformatics computer clusters. Recent advances in PC virtualization have made it possible to run a full computer operating system, with all of its installed software, on top of another operating system, inside a "box," or virtual machine (VM). Such a VM can flexibly be deployed on multiple computers, in a local network, e.g., on existing desktop PCs, and even in the Cloud, to create a "virtual" computer cluster. Many bioinformatics applications in evolutionary biology can be run in parallel, running processes in one or more VMs. Here, we show how a ready-made bioinformatics VM image, named BioNode, effectively creates a computing cluster, and pipeline, in a few steps. This allows researchers to scale-up computations from their desktop, using available hardware, anytime it is required. BioNode is based on Debian Linux and can run on networked PCs and in the Cloud. Over 200 bioinformatics and statistical software packages, of interest to evolutionary biology, are included, such as PAML, Muscle, MAFFT, MrBayes, and BLAST. Most of these software packages are maintained through the Debian Med project. In addition, BioNode contains convenient configuration scripts for parallelizing bioinformatics software. Where Debian Med encourages packaging free and open source bioinformatics software through one central project, BioNode encourages creating free and open source VM images, for multiple targets, through one central project. BioNode can be deployed on Windows, OSX, Linux, and in the Cloud. Next to the downloadable BioNode images, we provide tutorials online, which empower bioinformaticians to install and run BioNode in different environments, as well as information for future initiatives, on creating and building such images.

Wind Farm Layout Optimization through a Crossover-Elitist Evolutionary Algorithm performed over a High Performing Analytical Wake Model

NASA Astrophysics Data System (ADS)

Kirchner-Bossi, Nicolas; Porté-Agel, Fernando

2017-04-01

Wind turbine wakes can significantly disrupt the performance of further downstream turbines in a wind farm, thus seriously limiting the overall wind farm power output. Such effect makes the layout design of a wind farm to play a crucial role on the whole performance of the project. An accurate definition of the wake interactions added to a computationally compromised layout optimization strategy can result in an efficient resource when addressing the problem. This work presents a novel soft-computing approach to optimize the wind farm layout by minimizing the overall wake effects that the installed turbines exert on one another. An evolutionary algorithm with an elitist sub-optimization crossover routine and an unconstrained (continuous) turbine positioning set up is developed and tested over an 80-turbine offshore wind farm over the North Sea off Denmark (Horns Rev I). Within every generation of the evolution, the wind power output (cost function) is computed through a recently developed and validated analytical wake model with a Gaussian profile velocity deficit [1], which has shown to outperform the traditionally employed wake models through different LES simulations and wind tunnel experiments. Two schemes with slightly different perimeter constraint conditions (full or partial) are tested. Results show, compared to the baseline, gridded layout, a wind power output increase between 5.5% and 7.7%. In addition, it is observed that the electric cable length at the facilities is reduced by up to 21%. [1] Bastankhah, Majid, and Fernando Porté-Agel. "A new analytical model for wind-turbine wakes." Renewable Energy 70 (2014): 116-123.

Computer symbiosis: Emergence of symbiotic behavior through evolution

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ikegami, Takashi; Kaneko, Kunihiko

Symbiosis is altruistic cooperation between distinct species. It is one of the most effective evolutionary processes, but its dynamics are not well understood as yet. A simple model of symbiosis is introduced, where we consider interactions between hosts and parasites and also mutations of hosts and parasites. It is found that a symbiotic state emerges for a suitable range of mutation rates. The symbiotic state is not static, but dynamically oscillates. Harmful parasites violating symbiosis appear periodically, but are rapidly extinguished by hosts and other parasites, and the symbiotic state is recovered. The emergence of ''Tit for Tat'' strategy tomore » maintain symbiosis is discussed. 4 figs.« less

Ecology and evolution of plant–pollinator interactions

PubMed Central

Mitchell, Randall J.; Irwin, Rebecca E.; Flanagan, Rebecca J.; Karron, Jeffrey D.

2009-01-01

Background Some of the most exciting advances in pollination biology have resulted from interdisciplinary research combining ecological and evolutionary perspectives. For example, these two approaches have been essential for understanding the functional ecology of floral traits, the dynamics of pollen transport, competition for pollinator services, and patterns of specialization and generalization in plant–pollinator interactions. However, as research in these and other areas has progressed, many pollination biologists have become more specialized in their research interests, focusing their attention on either evolutionary or ecological questions. We believe that the continuing vigour of a synthetic and interdisciplinary field like pollination biology depends on renewed connections between ecological and evolutionary approaches. Scope In this Viewpoint paper we highlight the application of ecological and evolutionary approaches to two themes in pollination biology: (1) links between pollinator behaviour and plant mating systems, and (2) generalization and specialization in pollination systems. We also describe how mathematical models and synthetic analyses have broadened our understanding of pollination biology, especially in human-modified landscapes. We conclude with several suggestions that we hope will stimulate future research. This Viewpoint also serves as the introduction to this Special Issue on the Ecology and Evolution of Plant–Pollinator Interactions. These papers provide inspiring examples of the synergy between evolutionary and ecological approaches, and offer glimpses of great accomplishments yet to come. PMID:19482881

Ecology and evolution of plant-pollinator interactions.

PubMed

Mitchell, Randall J; Irwin, Rebecca E; Flanagan, Rebecca J; Karron, Jeffrey D

2009-06-01

Some of the most exciting advances in pollination biology have resulted from interdisciplinary research combining ecological and evolutionary perspectives. For example, these two approaches have been essential for understanding the functional ecology of floral traits, the dynamics of pollen transport, competition for pollinator services, and patterns of specialization and generalization in plant-pollinator interactions. However, as research in these and other areas has progressed, many pollination biologists have become more specialized in their research interests, focusing their attention on either evolutionary or ecological questions. We believe that the continuing vigour of a synthetic and interdisciplinary field like pollination biology depends on renewed connections between ecological and evolutionary approaches. In this Viewpoint paper we highlight the application of ecological and evolutionary approaches to two themes in pollination biology: (1) links between pollinator behaviour and plant mating systems, and (2) generalization and specialization in pollination systems. We also describe how mathematical models and synthetic analyses have broadened our understanding of pollination biology, especially in human-modified landscapes. We conclude with several suggestions that we hope will stimulate future research. This Viewpoint also serves as the introduction to this Special Issue on the Ecology and Evolution of Plant-Pollinator Interactions. These papers provide inspiring examples of the synergy between evolutionary and ecological approaches, and offer glimpses of great accomplishments yet to come.

Environmental fluctuations restrict eco-evolutionary dynamics in predator-prey system.

PubMed

Hiltunen, Teppo; Ayan, Gökçe B; Becks, Lutz

2015-06-07

Environmental fluctuations, species interactions and rapid evolution are all predicted to affect community structure and their temporal dynamics. Although the effects of the abiotic environment and prey evolution on ecological community dynamics have been studied separately, these factors can also have interactive effects. Here we used bacteria-ciliate microcosm experiments to test for eco-evolutionary dynamics in fluctuating environments. Specifically, we followed population dynamics and a prey defence trait over time when populations were exposed to regular changes of bottom-up or top-down stressors, or combinations of these. We found that the rate of evolution of a defence trait was significantly lower in fluctuating compared with stable environments, and that the defence trait evolved to lower levels when two environmental stressors changed recurrently. The latter suggests that top-down and bottom-up changes can have additive effects constraining evolutionary response within populations. The differences in evolutionary trajectories are explained by fluctuations in population sizes of the prey and the predator, which continuously alter the supply of mutations in the prey and strength of selection through predation. Thus, it may be necessary to adopt an eco-evolutionary perspective on studies concerning the evolution of traits mediating species interactions. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

The human phosphotyrosine signaling network: Evolution and hotspots of hijacking in cancer

PubMed Central

Li, Lei; Tibiche, Chabane; Fu, Cong; Kaneko, Tomonori; Moran, Michael F.; Schiller, Martin R.; Li, Shawn Shun-Cheng; Wang, Edwin

2012-01-01

Phosphotyrosine (pTyr) signaling, which plays a central role in cell–cell and cell–environment interactions, has been considered to be an evolutionary innovation in multicellular metazoans. However, neither the emergence nor the evolution of the human pTyr signaling system is currently understood. Tyrosine kinase (TK) circuits, each of which consists of a TK writer, a kinase substrate, and a related reader, such as Src homology (SH) 2 domains and pTyr-binding (PTB) domains, comprise the core machinery of the pTyr signaling network. In this study, we analyzed the evolutionary trajectories of 583 literature-derived and 50,000 computationally predicted human TK circuits in 19 representative eukaryotic species and assigned their evolutionary origins. We found that human TK circuits for intracellular pTyr signaling originated largely from primitive organisms, whereas the inter- or extracellular signaling circuits experienced significant expansion in the bilaterian lineage through the “back-wiring” of newly evolved kinases to primitive substrates and SH2/PTB domains. Conversely, the TK circuits that are involved in tissue-specific signaling evolved mainly in vertebrates by the back-wiring of vertebrate substrates to primitive kinases and SH2/PTB domains. Importantly, we found that cancer signaling preferentially employs the pTyr sites, which are linked to more TK circuits. Our work provides insights into the evolutionary paths of the human pTyr signaling circuits and suggests the use of a network approach for cancer intervention through the targeting of key pTyr sites and their associated signaling hubs in the network. PMID:22194470

Ecological divergence and evolutionary transition of resprouting types in Banksia attenuata.

PubMed

He, Tianhua

2014-08-01

Resprouting is a key functional trait that allows plants to survive diverse disturbances. The fitness benefits associated with resprouting include a rapid return to adult growth, early flowering, and setting seed. The resprouting responses observed following fire are varied, as are the ecological outcomes. Understanding the ecological divergence and evolutionary pathways of different resprouting types and how the environment and genetics interact to drive such morphological evolution represents an important, but under-studied, topic. In the present study, microsatellite markers and microevolutionary approaches were used to better understand: (1) whether genetic differentiation is related to morphological divergence among resprouting types and if so, whether there are any specific genetic variations associated with morphological divergence and (2) the evolutionary pathway of the transitions between two resprouting types in Banksia attenuata (epicormic resprouting from aerial stems or branch; resprouting from a underground lignotuber). The results revealed an association between population genetic differentiation and the morphological divergence of postfire resprouting types in B. attenuata. A microsatellite allele has been shown to be associated with epicormic populations. Approximate Bayesian Computation analysis revealed a likely evolutionary transition from epicormic to lignotuberous resprouting in B. attenuata. It is concluded that the postfire resprouting type in B. attenuata is likely determined by the fire's characteristics. The differentiated expression of postfire resprouting types in different environments is likely a consequence of local genetic adaptation. The capacity to shift the postfire resprouting type to adapt to diverse fire regimes is most likely the key factor explaining why B. attenuata is the most widespread member of the Banksia genus.

The autophagy interaction network of the aging model Podospora anserina.

PubMed

Philipp, Oliver; Hamann, Andrea; Osiewacz, Heinz D; Koch, Ina

2017-03-27

Autophagy is a conserved molecular pathway involved in the degradation and recycling of cellular components. It is active either as response to starvation or molecular damage. Evidence is emerging that autophagy plays a key role in the degradation of damaged cellular components and thereby affects aging and lifespan control. In earlier studies, it was found that autophagy in the aging model Podospora anserina acts as a longevity assurance mechanism. However, only little is known about the individual components controlling autophagy in this aging model. Here, we report a biochemical and bioinformatics study to detect the protein-protein interaction (PPI) network of P. anserina combining experimental and theoretical methods. We constructed the PPI network of autophagy in P. anserina based on the corresponding networks of yeast and human. We integrated PaATG8 interaction partners identified in an own yeast two-hybrid analysis using ATG8 of P. anserina as bait. Additionally, we included age-dependent transcriptome data. The resulting network consists of 89 proteins involved in 186 interactions. We applied bioinformatics approaches to analyze the network topology and to prove that the network is not random, but exhibits biologically meaningful properties. We identified hub proteins which play an essential role in the network as well as seven putative sub-pathways, and interactions which are likely to be evolutionary conserved amongst species. We confirmed that autophagy-associated genes are significantly often up-regulated and co-expressed during aging of P. anserina. With the present study, we provide a comprehensive biological network of the autophagy pathway in P. anserina comprising PPI and gene expression data. It is based on computational prediction as well as experimental data. We identified sub-pathways, important hub proteins, and evolutionary conserved interactions. The network clearly illustrates the relation of autophagy to aging processes and enables further specific studies to understand autophagy and aging in P. anserina as well as in other systems.

Evolutionary graph theory: breaking the symmetry between interaction and replacement

PubMed Central

Ohtsuki, Hisashi; Pacheco, Jorge M.; Nowak, Martin A.

2008-01-01

We study evolutionary dynamics in a population whose structure is given by two graphs: the interaction graph determines who plays with whom in an evolutionary game; the replacement graph specifies the geometry of evolutionary competition and updating. First, we calculate the fixation probabilities of frequency dependent selection between two strategies or phenotypes. We consider three different update mechanisms: birth-death, death-birth and imitation. Then, as a particular example, we explore the evolution of cooperation. Suppose the interaction graph is a regular graph of degree h, the replacement graph is a regular graph of degree g and the overlap between the two graphs is a regular graph of degree l. We show that cooperation is favored by natural selection if b/c > hg/l. Here, b and c denote the benefit and cost of the altruistic act. This result holds for death-birth updating, weak selection and large population size. Note that the optimum population structure for cooperators is given by maximum overlap between the interaction and the replacement graph (g = h = l), which means that the two graphs are identical. We also prove that a modified replicator equation can describe how the expected values of the frequencies of an arbitrary number of strategies change on replacement and interaction graphs: the two graphs induce a transformation of the payoff matrix. PMID:17350049

MEGA-CC: computing core of molecular evolutionary genetics analysis program for automated and iterative data analysis.

PubMed

Kumar, Sudhir; Stecher, Glen; Peterson, Daniel; Tamura, Koichiro

2012-10-15

There is a growing need in the research community to apply the molecular evolutionary genetics analysis (MEGA) software tool for batch processing a large number of datasets and to integrate it into analysis workflows. Therefore, we now make available the computing core of the MEGA software as a stand-alone executable (MEGA-CC), along with an analysis prototyper (MEGA-Proto). MEGA-CC provides users with access to all the computational analyses available through MEGA's graphical user interface version. This includes methods for multiple sequence alignment, substitution model selection, evolutionary distance estimation, phylogeny inference, substitution rate and pattern estimation, tests of natural selection and ancestral sequence inference. Additionally, we have upgraded the source code for phylogenetic analysis using the maximum likelihood methods for parallel execution on multiple processors and cores. Here, we describe MEGA-CC and outline the steps for using MEGA-CC in tandem with MEGA-Proto for iterative and automated data analysis. http://www.megasoftware.net/.

On joint subtree distributions under two evolutionary models.

PubMed

Wu, Taoyang; Choi, Kwok Pui

2016-04-01

In population and evolutionary biology, hypotheses about micro-evolutionary and macro-evolutionary processes are commonly tested by comparing the shape indices of empirical evolutionary trees with those predicted by neutral models. A key ingredient in this approach is the ability to compute and quantify distributions of various tree shape indices under random models of interest. As a step to meet this challenge, in this paper we investigate the joint distribution of cherries and pitchforks (that is, subtrees with two and three leaves) under two widely used null models: the Yule-Harding-Kingman (YHK) model and the proportional to distinguishable arrangements (PDA) model. Based on two novel recursive formulae, we propose a dynamic approach to numerically compute the exact joint distribution (and hence the marginal distributions) for trees of any size. We also obtained insights into the statistical properties of trees generated under these two models, including a constant correlation between the cherry and the pitchfork distributions under the YHK model, and the log-concavity and unimodality of the cherry distributions under both models. In addition, we show that there exists a unique change point for the cherry distributions between these two models. Copyright © 2015 Elsevier Inc. All rights reserved.

The fueling of active galaxies

NASA Technical Reports Server (NTRS)

Hernquist, Lars

1991-01-01

Collisions of galaxies are often invoked to explain violent phenomena in the universe. The dynamics of interacting galaxies is intrinsically three-dimensional and involves both gas and stellar dynamics. In general, a computational approach is needed to model galactic collisions. Galaxy encounters are studied using a hybrid N-body/hydrodynamics code, capable of integrating systems of stars, gas, and dark matter in a fully self-consistent manner. These experiments demonstrate that gravitational coupling between gas and stars in galactic interactions can drive most of the gas throughout a galaxy into the nucleus of a merger remnant. The high densities in these gas concentrations are likely to result in strong bursts of star formation. Hence, this process may explain the nuclear starbursts in some systems of interacting galaxies. Further collapse of these gas concentrations can trigger even more intense activity if some gas is eventually accreted by a supermassive black hole. Such an evolutionary sequence may account for some quasars and active galactic nuclei.

Evolutionary Computing Methods for Spectral Retrieval

NASA Technical Reports Server (NTRS)

Terrile, Richard; Fink, Wolfgang; Huntsberger, Terrance; Lee, Seugwon; Tisdale, Edwin; VonAllmen, Paul; Tinetti, Geivanna

2009-01-01

A methodology for processing spectral images to retrieve information on underlying physical, chemical, and/or biological phenomena is based on evolutionary and related computational methods implemented in software. In a typical case, the solution (the information that one seeks to retrieve) consists of parameters of a mathematical model that represents one or more of the phenomena of interest. The methodology was developed for the initial purpose of retrieving the desired information from spectral image data acquired by remote-sensing instruments aimed at planets (including the Earth). Examples of information desired in such applications include trace gas concentrations, temperature profiles, surface types, day/night fractions, cloud/aerosol fractions, seasons, and viewing angles. The methodology is also potentially useful for retrieving information on chemical and/or biological hazards in terrestrial settings. In this methodology, one utilizes an iterative process that minimizes a fitness function indicative of the degree of dissimilarity between observed and synthetic spectral and angular data. The evolutionary computing methods that lie at the heart of this process yield a population of solutions (sets of the desired parameters) within an accuracy represented by a fitness-function value specified by the user. The evolutionary computing methods (ECM) used in this methodology are Genetic Algorithms and Simulated Annealing, both of which are well-established optimization techniques and have also been described in previous NASA Tech Briefs articles. These are embedded in a conceptual framework, represented in the architecture of the implementing software, that enables automatic retrieval of spectral and angular data and analysis of the retrieved solutions for uniqueness.

PhyloDet: a scalable visualization tool for mapping multiple traits to large evolutionary trees

PubMed Central

Lee, Bongshin; Nachmanson, Lev; Robertson, George; Carlson, Jonathan M.; Heckerman, David

2009-01-01

Summary: Evolutionary biologists are often interested in finding correlations among biological traits across a number of species, as such correlations may lead to testable hypotheses about the underlying function. Because some species are more closely related than others, computing and visualizing these correlations must be done in the context of the evolutionary tree that relates species. In this note, we introduce PhyloDet (short for PhyloDetective), an evolutionary tree visualization tool that enables biologists to visualize multiple traits mapped to the tree. Availability: http://research.microsoft.com/cue/phylodet/ Contact: bongshin@microsoft.com. PMID:19633096

Network-level architecture and the evolutionary potential of underground metabolism.

PubMed

Notebaart, Richard A; Szappanos, Balázs; Kintses, Bálint; Pál, Ferenc; Györkei, Ádám; Bogos, Balázs; Lázár, Viktória; Spohn, Réka; Csörgő, Bálint; Wagner, Allon; Ruppin, Eytan; Pál, Csaba; Papp, Balázs

2014-08-12

A central unresolved issue in evolutionary biology is how metabolic innovations emerge. Low-level enzymatic side activities are frequent and can potentially be recruited for new biochemical functions. However, the role of such underground reactions in adaptation toward novel environments has remained largely unknown and out of reach of computational predictions, not least because these issues demand analyses at the level of the entire metabolic network. Here, we provide a comprehensive computational model of the underground metabolism in Escherichia coli. Most underground reactions are not isolated and 45% of them can be fully wired into the existing network and form novel pathways that produce key precursors for cell growth. This observation allowed us to conduct an integrated genome-wide in silico and experimental survey to characterize the evolutionary potential of E. coli to adapt to hundreds of nutrient conditions. We revealed that underground reactions allow growth in new environments when their activity is increased. We estimate that at least ∼20% of the underground reactions that can be connected to the existing network confer a fitness advantage under specific environments. Moreover, our results demonstrate that the genetic basis of evolutionary adaptations via underground metabolism is computationally predictable. The approach used here has potential for various application areas from bioengineering to medical genetics.

Genetic networks and soft computing.

PubMed

Mitra, Sushmita; Das, Ranajit; Hayashi, Yoichi

2011-01-01

The analysis of gene regulatory networks provides enormous information on various fundamental cellular processes involving growth, development, hormone secretion, and cellular communication. Their extraction from available gene expression profiles is a challenging problem. Such reverse engineering of genetic networks offers insight into cellular activity toward prediction of adverse effects of new drugs or possible identification of new drug targets. Tasks such as classification, clustering, and feature selection enable efficient mining of knowledge about gene interactions in the form of networks. It is known that biological data is prone to different kinds of noise and ambiguity. Soft computing tools, such as fuzzy sets, evolutionary strategies, and neurocomputing, have been found to be helpful in providing low-cost, acceptable solutions in the presence of various types of uncertainties. In this paper, we survey the role of these soft methodologies and their hybridizations, for the purpose of generating genetic networks.

Theoretical model to investigate the alkyl chain and anion dependent interactions of gemini surfactant with bovine serum albumin.

PubMed

Vishvakarma, Vijay K; Kumari, Kamlesh; Patel, Rajan; Dixit, V S; Singh, Prashant; Mehrotra, Gopal K; Chandra, Ramesh; Chakrawarty, Anand Kumar

2015-05-15

Surfactants are used to prevent the irreversible aggregation of partially refolded proteins and they also assist in protein refolding. We have reported the design and screening of gemini surfactant to stabilize bovine serum albumin (BSA) with the help of computational tool (iGEMDOCK). A series of gemini surfactant has been designed based on bis-N-alkyl nicotinate dianion via varying the alkyl group and anion. On changing the alkyl group and anion of the surfactant, the value of Log P changes means polarity of surfactant can be tuned. Further, the virtual screening of the gemini surfactant has been carried out based on generic evolutionary method. Herein, thermodynamic data was studied to determine the potential of gemini surfactant as BSA stabilizer. Computational tools help to find out the efficient gemini surfactant to stabilize the BSA rather than to use the surfactant randomly and directionless for the stabilization. It can be confirmed through the experimental techniques. Previously, researcher synthesized one of the designed and used gemini surfactant to stabilize the BSA and their interactions were confirmed through various techniques and computational docking. But herein, the authors find the most competent gemini surfactant to stabilize BSA using computational tools on the basis of energy score. Different from the single chain surfactant, the gemini surfactants exhibit much stronger electrostatic and hydrophobic interactions with the protein and are thus effective at much lower concentrations. Based on the present study, it is expected that gemini surfactants may prove useful in the protein stabilization operations and may thus be effectively employed to circumvent the problem of misfolding and aggregation. Copyright © 2015 Elsevier B.V. All rights reserved.

Theoretical model to investigate the alkyl chain and anion dependent interactions of gemini surfactant with bovine serum albumin

NASA Astrophysics Data System (ADS)

Vishvakarma, Vijay K.; Kumari, Kamlesh; Patel, Rajan; Dixit, V. S.; Singh, Prashant; Mehrotra, Gopal K.; Chandra, Ramesh; Chakrawarty, Anand Kumar

2015-05-01

Surfactants are used to prevent the irreversible aggregation of partially refolded proteins and they also assist in protein refolding. We have reported the design and screening of gemini surfactant to stabilize bovine serum albumin (BSA) with the help of computational tool (iGEMDOCK). A series of gemini surfactant has been designed based on bis-N-alkyl nicotinate dianion via varying the alkyl group and anion. On changing the alkyl group and anion of the surfactant, the value of Log P changes means polarity of surfactant can be tuned. Further, the virtual screening of the gemini surfactant has been carried out based on generic evolutionary method. Herein, thermodynamic data was studied to determine the potential of gemini surfactant as BSA stabilizer. Computational tools help to find out the efficient gemini surfactant to stabilize the BSA rather than to use the surfactant randomly and directionless for the stabilization. It can be confirmed through the experimental techniques. Previously, researcher synthesized one of the designed and used gemini surfactant to stabilize the BSA and their interactions were confirmed through various techniques and computational docking. But herein, the authors find the most competent gemini surfactant to stabilize BSA using computational tools on the basis of energy score. Different from the single chain surfactant, the gemini surfactants exhibit much stronger electrostatic and hydrophobic interactions with the protein and are thus effective at much lower concentrations. Based on the present study, it is expected that gemini surfactants may prove useful in the protein stabilization operations and may thus be effectively employed to circumvent the problem of misfolding and aggregation.

Ecology and evolution of metabolic cross-feeding interactions in bacteria.

PubMed

D'Souza, Glen; Shitut, Shraddha; Preussger, Daniel; Yousif, Ghada; Waschina, Silvio; Kost, Christian

2018-05-01

Literature covered: early 2000s to late 2017Bacteria frequently exchange metabolites with other micro- and macro-organisms. In these often obligate cross-feeding interactions, primary metabolites such as vitamins, amino acids, nucleotides, or growth factors are exchanged. The widespread distribution of this type of metabolic interactions, however, is at odds with evolutionary theory: why should an organism invest costly resources to benefit other individuals rather than using these metabolites to maximize its own fitness? Recent empirical work has shown that bacterial genotypes can significantly benefit from trading metabolites with other bacteria relative to cells not engaging in such interactions. Here, we will provide a comprehensive overview over the ecological factors and evolutionary mechanisms that have been identified to explain the evolution and maintenance of metabolic mutualisms among microorganisms. Furthermore, we will highlight general principles that underlie the adaptive evolution of interconnected microbial metabolic networks as well as the evolutionary consequences that result for cells living in such communities.

Local Geometry and Evolutionary Conservation of Protein Surfaces Reveal the Multiple Recognition Patches in Protein-Protein Interactions

PubMed Central

Laine, Elodie; Carbone, Alessandra

2015-01-01

Protein-protein interactions (PPIs) are essential to all biological processes and they represent increasingly important therapeutic targets. Here, we present a new method for accurately predicting protein-protein interfaces, understanding their properties, origins and binding to multiple partners. Contrary to machine learning approaches, our method combines in a rational and very straightforward way three sequence- and structure-based descriptors of protein residues: evolutionary conservation, physico-chemical properties and local geometry. The implemented strategy yields very precise predictions for a wide range of protein-protein interfaces and discriminates them from small-molecule binding sites. Beyond its predictive power, the approach permits to dissect interaction surfaces and unravel their complexity. We show how the analysis of the predicted patches can foster new strategies for PPIs modulation and interaction surface redesign. The approach is implemented in JET2, an automated tool based on the Joint Evolutionary Trees (JET) method for sequence-based protein interface prediction. JET2 is freely available at www.lcqb.upmc.fr/JET2. PMID:26690684

The Modular Organization of Protein Interactions in Escherichia coli

PubMed Central

Peregrín-Alvarez, José M.; Xiong, Xuejian; Su, Chong; Parkinson, John

2009-01-01

Escherichia coli serves as an excellent model for the study of fundamental cellular processes such as metabolism, signalling and gene expression. Understanding the function and organization of proteins within these processes is an important step towards a ‘systems’ view of E. coli. Integrating experimental and computational interaction data, we present a reliable network of 3,989 functional interactions between 1,941 E. coli proteins (∼45% of its proteome). These were combined with a recently generated set of 3,888 high-quality physical interactions between 918 proteins and clustered to reveal 316 discrete modules. In addition to known protein complexes (e.g., RNA and DNA polymerases), we identified modules that represent biochemical pathways (e.g., nitrate regulation and cell wall biosynthesis) as well as batteries of functionally and evolutionarily related processes. To aid the interpretation of modular relationships, several case examples are presented, including both well characterized and novel biochemical systems. Together these data provide a global view of the modular organization of the E. coli proteome and yield unique insights into structural and evolutionary relationships in bacterial networks. PMID:19798435

Evolutionarily conserved regions and hydrophobic contacts at the superfamily level: The case of the fold-type I, pyridoxal-5′-phosphate-dependent enzymes

PubMed Central

Paiardini, Alessandro; Bossa, Francesco; Pascarella, Stefano

2004-01-01

The wealth of biological information provided by structural and genomic projects opens new prospects of understanding life and evolution at the molecular level. In this work, it is shown how computational approaches can be exploited to pinpoint protein structural features that remain invariant upon long evolutionary periods in the fold-type I, PLP-dependent enzymes. A nonredundant set of 23 superposed crystallographic structures belonging to this superfamily was built. Members of this family typically display high-structural conservation despite low-sequence identity. For each structure, a multiple-sequence alignment of orthologous sequences was obtained, and the 23 alignments were merged using the structural information to obtain a comprehensive multiple alignment of 921 sequences of fold-type I enzymes. The structurally conserved regions (SCRs), the evolutionarily conserved residues, and the conserved hydrophobic contacts (CHCs) were extracted from this data set, using both sequence and structural information. The results of this study identified a structural pattern of hydrophobic contacts shared by all of the superfamily members of fold-type I enzymes and involved in native interactions. This profile highlights the presence of a nucleus for this fold, in which residues participating in the most conserved native interactions exhibit preferential evolutionary conservation, that correlates significantly (r = 0.70) with the extent of mean hydrophobic contact value of their apolar fraction. PMID:15498941

Catalysis by a de novo zinc-mediated protein interface: implications for natural enzyme evolution and rational enzyme engineering.

PubMed

Der, Bryan S; Edwards, David R; Kuhlman, Brian

2012-05-08

Here we show that a recent computationally designed zinc-mediated protein interface is serendipitously capable of catalyzing carboxyester and phosphoester hydrolysis. Although the original motivation was to design a de novo zinc-mediated protein-protein interaction (called MID1-zinc), we observed in the homodimer crystal structure a small cleft and open zinc coordination site. We investigated if the cleft and zinc site at the designed interface were sufficient for formation of a primitive active site that can perform hydrolysis. MID1-zinc hydrolyzes 4-nitrophenyl acetate with a rate acceleration of 10(5) and a k(cat)/K(M) of 630 M(-1) s(-1) and 4-nitrophenyl phosphate with a rate acceleration of 10(4) and a k(cat)/K(M) of 14 M(-1) s(-1). These rate accelerations by an unoptimized active site highlight the catalytic power of zinc and suggest that the clefts formed by protein-protein interactions are well-suited for creating enzyme active sites. This discovery has implications for protein evolution and engineering: from an evolutionary perspective, three-coordinated zinc at a homodimer interface cleft represents a simple evolutionary path to nascent enzymatic activity; from a protein engineering perspective, future efforts in de novo design of enzyme active sites may benefit from exploring clefts at protein interfaces for active site placement.

Drug-target interaction prediction from PSSM based evolutionary information.

PubMed

Mousavian, Zaynab; Khakabimamaghani, Sahand; Kavousi, Kaveh; Masoudi-Nejad, Ali

2016-01-01

The labor-intensive and expensive experimental process of drug-target interaction prediction has motivated many researchers to focus on in silico prediction, which leads to the helpful information in supporting the experimental interaction data. Therefore, they have proposed several computational approaches for discovering new drug-target interactions. Several learning-based methods have been increasingly developed which can be categorized into two main groups: similarity-based and feature-based. In this paper, we firstly use the bi-gram features extracted from the Position Specific Scoring Matrix (PSSM) of proteins in predicting drug-target interactions. Our results demonstrate the high-confidence prediction ability of the Bigram-PSSM model in terms of several performance indicators specifically for enzymes and ion channels. Moreover, we investigate the impact of negative selection strategy on the performance of the prediction, which is not widely taken into account in the other relevant studies. This is important, as the number of non-interacting drug-target pairs are usually extremely large in comparison with the number of interacting ones in existing drug-target interaction data. An interesting observation is that different levels of performance reduction have been attained for four datasets when we change the sampling method from the random sampling to the balanced sampling. Copyright © 2015 Elsevier Inc. All rights reserved.

Matrix metalloproteinases: structures, evolution, and diversification.

PubMed

Massova, I; Kotra, L P; Fridman, R; Mobashery, S

1998-09-01

A comprehensive sequence alignment of 64 members of the family of matrix metalloproteinases (MMPs) for the entire sequences, and subsequently the catalytic and the hemopexin-like domains, have been performed. The 64 MMPs were selected from plants, invertebrates, and vertebrates. The analyses disclosed that as many as 23 distinct subfamilies of these proteins are known to exist. Information from the sequence alignments was correlated with structures, both crystallographic as well as computational, of the catalytic domains for the 23 representative members of the MMP family. A survey of the metal binding sites and two loops containing variable sequences of amino acids, which are important for substrate interactions, are discussed. The collective data support the proposal that the assembly of the domains into multidomain enzymes was likely to be an early evolutionary event. This was followed by diversification, perhaps in parallel among the MMPs, in a subsequent evolutionary time scale. Analysis indicates that a retrograde structure simplification may have accounted for the evolution of MMPs with simple domain constituents, such as matrilysin, from the larger and more elaborate enzymes.

Eco-evolutionary dynamics in a coevolving host-virus system.

PubMed

Frickel, Jens; Sieber, Michael; Becks, Lutz

2016-04-01

Eco-evolutionary dynamics have been shown to be important for understanding population and community stability and their adaptive potential. However, coevolution in the framework of eco-evolutionary theory has not been addressed directly. Combining experiments with an algal host and its viral parasite, and mathematical model analyses we show eco-evolutionary dynamics in antagonistic coevolving populations. The interaction between antagonists initially resulted in arms race dynamics (ARD) with selective sweeps, causing oscillating host-virus population dynamics. However, ARD ended and populations stabilised after the evolution of a general resistant host, whereas a trade-off between host resistance and growth then maintained host diversity over time (trade-off driven dynamics). Most importantly, our study shows that the interaction between ecology and evolution had important consequences for the predictability of the mode and tempo of adaptive change and for the stability and adaptive potential of populations. © 2016 John Wiley & Sons Ltd/CNRS.

CRITTERS! A Realistic Simulation for Teaching Evolutionary Biology

ERIC Educational Resources Information Center

Latham, Luke G., II; Scully, Erik P.

2008-01-01

Evolutionary processes can be studied in nature and in the laboratory, but time and financial constraints result in few opportunities for undergraduate and high school students to explore the agents of genetic change in populations. One alternative to time consuming and expensive teaching laboratories is the use of computer simulations. We…

Speeding Up Ecological and Evolutionary Computations in R; Essentials of High Performance Computing for Biologists

PubMed Central

Visser, Marco D.; McMahon, Sean M.; Merow, Cory; Dixon, Philip M.; Record, Sydne; Jongejans, Eelke

2015-01-01

Computation has become a critical component of research in biology. A risk has emerged that computational and programming challenges may limit research scope, depth, and quality. We review various solutions to common computational efficiency problems in ecological and evolutionary research. Our review pulls together material that is currently scattered across many sources and emphasizes those techniques that are especially effective for typical ecological and environmental problems. We demonstrate how straightforward it can be to write efficient code and implement techniques such as profiling or parallel computing. We supply a newly developed R package (aprof) that helps to identify computational bottlenecks in R code and determine whether optimization can be effective. Our review is complemented by a practical set of examples and detailed Supporting Information material (S1–S3 Texts) that demonstrate large improvements in computational speed (ranging from 10.5 times to 14,000 times faster). By improving computational efficiency, biologists can feasibly solve more complex tasks, ask more ambitious questions, and include more sophisticated analyses in their research. PMID:25811842

An Adaptive Evolutionary Algorithm for Traveling Salesman Problem with Precedence Constraints

PubMed Central

Sung, Jinmo; Jeong, Bongju

2014-01-01

Traveling sales man problem with precedence constraints is one of the most notorious problems in terms of the efficiency of its solution approach, even though it has very wide range of industrial applications. We propose a new evolutionary algorithm to efficiently obtain good solutions by improving the search process. Our genetic operators guarantee the feasibility of solutions over the generations of population, which significantly improves the computational efficiency even when it is combined with our flexible adaptive searching strategy. The efficiency of the algorithm is investigated by computational experiments. PMID:24701158

An adaptive evolutionary algorithm for traveling salesman problem with precedence constraints.

PubMed

Sung, Jinmo; Jeong, Bongju

2014-01-01

Traveling sales man problem with precedence constraints is one of the most notorious problems in terms of the efficiency of its solution approach, even though it has very wide range of industrial applications. We propose a new evolutionary algorithm to efficiently obtain good solutions by improving the search process. Our genetic operators guarantee the feasibility of solutions over the generations of population, which significantly improves the computational efficiency even when it is combined with our flexible adaptive searching strategy. The efficiency of the algorithm is investigated by computational experiments.

Cancer Evolution: Mathematical Models and Computational Inference

PubMed Central

Beerenwinkel, Niko; Schwarz, Roland F.; Gerstung, Moritz; Markowetz, Florian

2015-01-01

Cancer is a somatic evolutionary process characterized by the accumulation of mutations, which contribute to tumor growth, clinical progression, immune escape, and drug resistance development. Evolutionary theory can be used to analyze the dynamics of tumor cell populations and to make inference about the evolutionary history of a tumor from molecular data. We review recent approaches to modeling the evolution of cancer, including population dynamics models of tumor initiation and progression, phylogenetic methods to model the evolutionary relationship between tumor subclones, and probabilistic graphical models to describe dependencies among mutations. Evolutionary modeling helps to understand how tumors arise and will also play an increasingly important prognostic role in predicting disease progression and the outcome of medical interventions, such as targeted therapy. PMID:25293804

Mean-Potential Law in Evolutionary Games

NASA Astrophysics Data System (ADS)

Nałecz-Jawecki, Paweł; Miekisz, Jacek

2018-01-01

The Letter presents a novel way to connect random walks, stochastic differential equations, and evolutionary game theory. We introduce a new concept of a potential function for discrete-space stochastic systems. It is based on a correspondence between one-dimensional stochastic differential equations and random walks, which may be exact not only in the continuous limit but also in finite-state spaces. Our method is useful for computation of fixation probabilities in discrete stochastic dynamical systems with two absorbing states. We apply it to evolutionary games, formulating two simple and intuitive criteria for evolutionary stability of pure Nash equilibria in finite populations. In particular, we show that the 1 /3 law of evolutionary games, introduced by Nowak et al. [Nature, 2004], follows from a more general mean-potential law.

Open Issues in Evolutionary Robotics.

PubMed

Silva, Fernando; Duarte, Miguel; Correia, Luís; Oliveira, Sancho Moura; Christensen, Anders Lyhne

2016-01-01

One of the long-term goals in evolutionary robotics is to be able to automatically synthesize controllers for real autonomous robots based only on a task specification. While a number of studies have shown the applicability of evolutionary robotics techniques for the synthesis of behavioral control, researchers have consistently been faced with a number of issues preventing the widespread adoption of evolutionary robotics for engineering purposes. In this article, we review and discuss the open issues in evolutionary robotics. First, we analyze the benefits and challenges of simulation-based evolution and subsequent deployment of controllers versus evolution on real robotic hardware. Second, we discuss specific evolutionary computation issues that have plagued evolutionary robotics: (1) the bootstrap problem, (2) deception, and (3) the role of genomic encoding and genotype-phenotype mapping in the evolution of controllers for complex tasks. Finally, we address the absence of standard research practices in the field. We also discuss promising avenues of research. Our underlying motivation is the reduction of the current gap between evolutionary robotics and mainstream robotics, and the establishment of evolutionary robotics as a canonical approach for the engineering of autonomous robots.

Toward a unifying framework for evolutionary processes.

PubMed

Paixão, Tiago; Badkobeh, Golnaz; Barton, Nick; Çörüş, Doğan; Dang, Duc-Cuong; Friedrich, Tobias; Lehre, Per Kristian; Sudholt, Dirk; Sutton, Andrew M; Trubenová, Barbora

2015-10-21

The theory of population genetics and evolutionary computation have been evolving separately for nearly 30 years. Many results have been independently obtained in both fields and many others are unique to its respective field. We aim to bridge this gap by developing a unifying framework for evolutionary processes that allows both evolutionary algorithms and population genetics models to be cast in the same formal framework. The framework we present here decomposes the evolutionary process into its several components in order to facilitate the identification of similarities between different models. In particular, we propose a classification of evolutionary operators based on the defining properties of the different components. We cast several commonly used operators from both fields into this common framework. Using this, we map different evolutionary and genetic algorithms to different evolutionary regimes and identify candidates with the most potential for the translation of results between the fields. This provides a unified description of evolutionary processes and represents a stepping stone towards new tools and results to both fields. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Landscape community genomics: understanding eco-evolutionary processes in complex environments

USGS Publications Warehouse

Hand, Brian K.; Lowe, Winsor H.; Kovach, Ryan P.; Muhlfeld, Clint C.; Luikart, Gordon

2015-01-01

Extrinsic factors influencing evolutionary processes are often categorically lumped into interactions that are environmentally (e.g., climate, landscape) or community-driven, with little consideration of the overlap or influence of one on the other. However, genomic variation is strongly influenced by complex and dynamic interactions between environmental and community effects. Failure to consider both effects on evolutionary dynamics simultaneously can lead to incomplete, spurious, or erroneous conclusions about the mechanisms driving genomic variation. We highlight the need for a landscape community genomics (LCG) framework to help to motivate and challenge scientists in diverse fields to consider a more holistic, interdisciplinary perspective on the genomic evolution of multi-species communities in complex environments.

Evolutionary Influenced Interaction Pattern as Indicator for the Investigation of Natural Variants Causing Nephrogenic Diabetes Insipidus

PubMed Central

Labudde, Dirk

2015-01-01

The importance of short membrane sequence motifs has been shown in many works and emphasizes the related sequence motif analysis. Together with specific transmembrane helix-helix interactions, the analysis of interacting sequence parts is helpful for understanding the process during membrane protein folding and in retaining the three-dimensional fold. Here we present a simple high-throughput analysis method for deriving mutational information of interacting sequence parts. Applied on aquaporin water channel proteins, our approach supports the analysis of mutational variants within different interacting subsequences and finally the investigation of natural variants which cause diseases like, for example, nephrogenic diabetes insipidus. In this work we demonstrate a simple method for massive membrane protein data analysis. As shown, the presented in silico analyses provide information about interacting sequence parts which are constrained by protein evolution. We present a simple graphical visualization medium for the representation of evolutionary influenced interaction pattern pairs (EIPPs) adapted to mutagen investigations of aquaporin-2, a protein whose mutants are involved in the rare endocrine disorder known as nephrogenic diabetes insipidus, and membrane proteins in general. Furthermore, we present a new method to derive new evolutionary variations within EIPPs which can be used for further mutagen laboratory investigations. PMID:26180540

Evolutionary Influenced Interaction Pattern as Indicator for the Investigation of Natural Variants Causing Nephrogenic Diabetes Insipidus.

PubMed

Grunert, Steffen; Labudde, Dirk

2015-01-01

The importance of short membrane sequence motifs has been shown in many works and emphasizes the related sequence motif analysis. Together with specific transmembrane helix-helix interactions, the analysis of interacting sequence parts is helpful for understanding the process during membrane protein folding and in retaining the three-dimensional fold. Here we present a simple high-throughput analysis method for deriving mutational information of interacting sequence parts. Applied on aquaporin water channel proteins, our approach supports the analysis of mutational variants within different interacting subsequences and finally the investigation of natural variants which cause diseases like, for example, nephrogenic diabetes insipidus. In this work we demonstrate a simple method for massive membrane protein data analysis. As shown, the presented in silico analyses provide information about interacting sequence parts which are constrained by protein evolution. We present a simple graphical visualization medium for the representation of evolutionary influenced interaction pattern pairs (EIPPs) adapted to mutagen investigations of aquaporin-2, a protein whose mutants are involved in the rare endocrine disorder known as nephrogenic diabetes insipidus, and membrane proteins in general. Furthermore, we present a new method to derive new evolutionary variations within EIPPs which can be used for further mutagen laboratory investigations.

Evolution in agriculture: the application of evolutionary approaches to the management of biotic interactions in agro-ecosystems

PubMed Central

Thrall, Peter H; Oakeshott, John G; Fitt, Gary; Southerton, Simon; Burdon, Jeremy J; Sheppard, Andy; Russell, Robyn J; Zalucki, Myron; Heino, Mikko; Ford Denison, R

2011-01-01

Anthropogenic impacts increasingly drive ecological and evolutionary processes at many spatio-temporal scales, demanding greater capacity to predict and manage their consequences. This is particularly true for agro-ecosystems, which not only comprise a significant proportion of land use, but which also involve conflicting imperatives to expand or intensify production while simultaneously reducing environmental impacts. These imperatives reinforce the likelihood of further major changes in agriculture over the next 30–40 years. Key transformations include genetic technologies as well as changes in land use. The use of evolutionary principles is not new in agriculture (e.g. crop breeding, domestication of animals, management of selection for pest resistance), but given land-use trends and other transformative processes in production landscapes, ecological and evolutionary research in agro-ecosystems must consider such issues in a broader systems context. Here, we focus on biotic interactions involving pests and pathogens as exemplars of situations where integration of agronomic, ecological and evolutionary perspectives has practical value. Although their presence in agro-ecosystems may be new, many traits involved in these associations evolved in natural settings. We advocate the use of predictive frameworks based on evolutionary models as pre-emptive management tools and identify some specific research opportunities to facilitate this. We conclude with a brief discussion of multidisciplinary approaches in applied evolutionary problems. PMID:25567968

Cultural and climatic changes shape the evolutionary history of the Uralic languages.

PubMed

Honkola, T; Vesakoski, O; Korhonen, K; Lehtinen, J; Syrjänen, K; Wahlberg, N

2013-06-01

Quantitative phylogenetic methods have been used to study the evolutionary relationships and divergence times of biological species, and recently, these have also been applied to linguistic data to elucidate the evolutionary history of language families. In biology, the factors driving macroevolutionary processes are assumed to be either mainly biotic (the Red Queen model) or mainly abiotic (the Court Jester model) or a combination of both. The applicability of these models is assumed to depend on the temporal and spatial scale observed as biotic factors act on species divergence faster and in smaller spatial scale than the abiotic factors. Here, we used the Uralic language family to investigate whether both 'biotic' interactions (i.e. cultural interactions) and abiotic changes (i.e. climatic fluctuations) are also connected to language diversification. We estimated the times of divergence using Bayesian phylogenetics with a relaxed-clock method and related our results to climatic, historical and archaeological information. Our timing results paralleled the previous linguistic studies but suggested a later divergence of Finno-Ugric, Finnic and Saami languages. Some of the divergences co-occurred with climatic fluctuation and some with cultural interaction and migrations of populations. Thus, we suggest that both 'biotic' and abiotic factors contribute either directly or indirectly to the diversification of languages and that both models can be applied when studying language evolution. © 2013 The Authors. Journal of Evolutionary Biology © 2013 European Society For Evolutionary Biology.

Genetic diversity, virulence and fitness evolution in an obligate fungal parasite of bees.

PubMed

Evison, S E F; Foley, K; Jensen, A B; Hughes, W O H

2015-01-01

Within-host competition is predicted to drive the evolution of virulence in parasites, but the precise outcomes of such interactions are often unpredictable due to many factors including the biology of the host and the parasite, stochastic events and co-evolutionary interactions. Here, we use a serial passage experiment (SPE) with three strains of a heterothallic fungal parasite (Ascosphaera apis) of the Honey bee (Apis mellifera) to assess how evolving under increasing competitive pressure affects parasite virulence and fitness evolution. The results show an increase in virulence after successive generations of selection and consequently faster production of spores. This faster sporulation, however, did not translate into more spores being produced during this longer window of sporulation; rather, it appeared to induce a loss of fitness in terms of total spore production. There was no evidence to suggest that a greater diversity of competing strains was a driver of this increased virulence and subsequent fitness cost, but rather that strain-specific competitive interactions influenced the evolutionary outcomes of mixed infections. It is possible that the parasite may have evolved to avoid competition with multiple strains because of its heterothallic mode of reproduction, which highlights the importance of understanding parasite biology when predicting disease dynamics. © 2014 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2014 European Society For Evolutionary Biology.

Rana computatrix to human language: towards a computational neuroethology of language evolution.

PubMed

Arbib, Michael A

2003-10-15

Walter's Machina speculatrix inspired the name Rana computatrix for a family of models of visuomotor coordination in the frog, which contributed to the development of computational neuroethology. We offer here an 'evolutionary' perspective on models in the same tradition for rat, monkey and human. For rat, we show how the frog-like taxon affordance model provides a basis for the spatial navigation mechanisms that involve the hippocampus and other brain regions. For monkey, we recall two models of neural mechanisms for visuomotor coordination. The first, for saccades, shows how interactions between the parietal and frontal cortex augment superior colliculus seen as the homologue of frog tectum. The second, for grasping, continues the theme of parieto-frontal interactions, linking parietal affordances to motor schemas in premotor cortex. It further emphasizes the mirror system for grasping, in which neurons are active both when the monkey executes a specific grasp and when it observes a similar grasp executed by others. The model of human-brain mechanisms is based on the mirror-system hypothesis of the evolution of the language-ready brain, which sees the human Broca's area as an evolved extension of the mirror system for grasping.

Investigating host-pathogen behavior and their interaction using genome-scale metabolic network models.

PubMed

Sadhukhan, Priyanka P; Raghunathan, Anu

2014-01-01

Genome Scale Metabolic Modeling methods represent one way to compute whole cell function starting from the genome sequence of an organism and contribute towards understanding and predicting the genotype-phenotype relationship. About 80 models spanning all the kingdoms of life from archaea to eukaryotes have been built till date and used to interrogate cell phenotype under varying conditions. These models have been used to not only understand the flux distribution in evolutionary conserved pathways like glycolysis and the Krebs cycle but also in applications ranging from value added product formation in Escherichia coli to predicting inborn errors of Homo sapiens metabolism. This chapter describes a protocol that delineates the process of genome scale metabolic modeling for analysing host-pathogen behavior and interaction using flux balance analysis (FBA). The steps discussed in the process include (1) reconstruction of a metabolic network from the genome sequence, (2) its representation in a precise mathematical framework, (3) its translation to a model, and (4) the analysis using linear algebra and optimization. The methods for biological interpretations of computed cell phenotypes in the context of individual host and pathogen models and their integration are also discussed.

Computation of direct and inverse mutations with the SEGM web server (Stochastic Evolution of Genetic Motifs): an application to splice sites of human genome introns.

PubMed

Benard, Emmanuel; Michel, Christian J

2009-08-01

We present here the SEGM web server (Stochastic Evolution of Genetic Motifs) in order to study the evolution of genetic motifs both in the direct evolutionary sense (past-present) and in the inverse evolutionary sense (present-past). The genetic motifs studied can be nucleotides, dinucleotides and trinucleotides. As an example of an application of SEGM and to understand its functionalities, we give an analysis of inverse mutations of splice sites of human genome introns. SEGM is freely accessible at http://lsiit-bioinfo.u-strasbg.fr:8080/webMathematica/SEGM/SEGM.html directly or by the web site http://dpt-info.u-strasbg.fr/~michel/. To our knowledge, this SEGM web server is to date the only computational biology software in this evolutionary approach.

JET2 Viewer: a database of predicted multiple, possibly overlapping, protein-protein interaction sites for PDB structures.

PubMed

Ripoche, Hugues; Laine, Elodie; Ceres, Nicoletta; Carbone, Alessandra

2017-01-04

The database JET2 Viewer, openly accessible at http://www.jet2viewer.upmc.fr/, reports putative protein binding sites for all three-dimensional (3D) structures available in the Protein Data Bank (PDB). This knowledge base was generated by applying the computational method JET 2 at large-scale on more than 20 000 chains. JET 2 strategy yields very precise predictions of interacting surfaces and unravels their evolutionary process and complexity. JET2 Viewer provides an online intelligent display, including interactive 3D visualization of the binding sites mapped onto PDB structures and suitable files recording JET 2 analyses. Predictions were evaluated on more than 15 000 experimentally characterized protein interfaces. This is, to our knowledge, the largest evaluation of a protein binding site prediction method. The overall performance of JET 2 on all interfaces are: Sen = 52.52, PPV = 51.24, Spe = 80.05, Acc = 75.89. The data can be used to foster new strategies for protein-protein interactions modulation and interaction surface redesign. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Biology Needs Evolutionary Software Tools: Let’s Build Them Right

PubMed Central

Team, Galaxy; Goecks, Jeremy; Taylor, James

2018-01-01

Abstract Research in population genetics and evolutionary biology has always provided a computational backbone for life sciences as a whole. Today evolutionary and population biology reasoning are essential for interpretation of large complex datasets that are characteristic of all domains of today’s life sciences ranging from cancer biology to microbial ecology. This situation makes algorithms and software tools developed by our community more important than ever before. This means that we, developers of software tool for molecular evolutionary analyses, now have a shared responsibility to make these tools accessible using modern technological developments as well as provide adequate documentation and training. PMID:29688462

A Deep Learning Framework for Robust and Accurate Prediction of ncRNA-Protein Interactions Using Evolutionary Information.

PubMed

Yi, Hai-Cheng; You, Zhu-Hong; Huang, De-Shuang; Li, Xiao; Jiang, Tong-Hai; Li, Li-Ping

2018-06-01

The interactions between non-coding RNAs (ncRNAs) and proteins play an important role in many biological processes, and their biological functions are primarily achieved by binding with a variety of proteins. High-throughput biological techniques are used to identify protein molecules bound with specific ncRNA, but they are usually expensive and time consuming. Deep learning provides a powerful solution to computationally predict RNA-protein interactions. In this work, we propose the RPI-SAN model by using the deep-learning stacked auto-encoder network to mine the hidden high-level features from RNA and protein sequences and feed them into a random forest (RF) model to predict ncRNA binding proteins. Stacked assembling is further used to improve the accuracy of the proposed method. Four benchmark datasets, including RPI2241, RPI488, RPI1807, and NPInter v2.0, were employed for the unbiased evaluation of five established prediction tools: RPI-Pred, IPMiner, RPISeq-RF, lncPro, and RPI-SAN. The experimental results show that our RPI-SAN model achieves much better performance than other methods, with accuracies of 90.77%, 89.7%, 96.1%, and 99.33%, respectively. It is anticipated that RPI-SAN can be used as an effective computational tool for future biomedical researches and can accurately predict the potential ncRNA-protein interacted pairs, which provides reliable guidance for biological research. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

Environmental osmolality influences sperm motility activation in an anuran amphibian.

PubMed

Byrne, P G; Dunne, C; Munn, A J; Silla, A J

2015-03-01

Evolutionary theory predicts that selection will favour sperm traits that maximize fertilization success in local fertilization environments. In externally fertilizing species, osmolality of the fertilization medium is known to play a critical role in activating sperm motility, but there remains limited evidence for adaptive responses to local osmotic environments. In this study, we used a split-sample experimental design and computer-assisted sperm analysis to (i) determine the optimal medium osmolality for sperm activation (% sperm motility and sperm velocity) in male common eastern froglets (Crinia signifera), (ii) test for among-population variation in percentage sperm motility and sperm velocity at various activation-medium osmolalities and (iii) test for among-population covariation between sperm performance and environmental osmolality. Frogs were obtained from nine populations that differed in environmental osmolality, and sperm samples of males from different populations were subjected to a range of activation-medium osmolalities. Percentage sperm motility was optimal between 10 and 50 mOsm kg(-1) , and sperm velocity was optimal between 10 and 100 mOsm kg(-1) , indicating that C. signifera has evolved sperm that can function across a broad range of osmolalities. As predicted, there was significant among-population variation in sperm performance. Furthermore, there was a significant interaction between activation-medium osmolality and environmental osmolality, indicating that frogs from populations with higher environmental osmolality produced sperm that performed better at higher osmolalities in vitro. This finding may reflect phenotypic plasticity in sperm functioning, or genetic divergence resulting from spatial variation in the strength of directional selection. Both of these explanations are consistent with evolutionary theory, providing some of the first empirical evidence that local osmotic environments can favour adaptive sperm motility responses in species that use an external mode of fertilization. © 2015 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2015 European Society For Evolutionary Biology.

RegulonDB version 9.0: high-level integration of gene regulation, coexpression, motif clustering and beyond.

PubMed

Gama-Castro, Socorro; Salgado, Heladia; Santos-Zavaleta, Alberto; Ledezma-Tejeida, Daniela; Muñiz-Rascado, Luis; García-Sotelo, Jair Santiago; Alquicira-Hernández, Kevin; Martínez-Flores, Irma; Pannier, Lucia; Castro-Mondragón, Jaime Abraham; Medina-Rivera, Alejandra; Solano-Lira, Hilda; Bonavides-Martínez, César; Pérez-Rueda, Ernesto; Alquicira-Hernández, Shirley; Porrón-Sotelo, Liliana; López-Fuentes, Alejandra; Hernández-Koutoucheva, Anastasia; Del Moral-Chávez, Víctor; Rinaldi, Fabio; Collado-Vides, Julio

2016-01-04

RegulonDB (http://regulondb.ccg.unam.mx) is one of the most useful and important resources on bacterial gene regulation,as it integrates the scattered scientific knowledge of the best-characterized organism, Escherichia coli K-12, in a database that organizes large amounts of data. Its electronic format enables researchers to compare their results with the legacy of previous knowledge and supports bioinformatics tools and model building. Here, we summarize our progress with RegulonDB since our last Nucleic Acids Research publication describing RegulonDB, in 2013. In addition to maintaining curation up-to-date, we report a collection of 232 interactions with small RNAs affecting 192 genes, and the complete repertoire of 189 Elementary Genetic Sensory-Response units (GENSOR units), integrating the signal, regulatory interactions, and metabolic pathways they govern. These additions represent major progress to a higher level of understanding of regulated processes. We have updated the computationally predicted transcription factors, which total 304 (184 with experimental evidence and 120 from computational predictions); we updated our position-weight matrices and have included tools for clustering them in evolutionary families. We describe our semiautomatic strategy to accelerate curation, including datasets from high-throughput experiments, a novel coexpression distance to search for 'neighborhood' genes to known operons and regulons, and computational developments. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Improving protein–protein interactions prediction accuracy using protein evolutionary information and relevance vector machine model

PubMed Central

An, Ji‐Yong; Meng, Fan‐Rong; Chen, Xing; Yan, Gui‐Ying; Hu, Ji‐Pu

2016-01-01

Abstract Predicting protein–protein interactions (PPIs) is a challenging task and essential to construct the protein interaction networks, which is important for facilitating our understanding of the mechanisms of biological systems. Although a number of high‐throughput technologies have been proposed to predict PPIs, there are unavoidable shortcomings, including high cost, time intensity, and inherently high false positive rates. For these reasons, many computational methods have been proposed for predicting PPIs. However, the problem is still far from being solved. In this article, we propose a novel computational method called RVM‐BiGP that combines the relevance vector machine (RVM) model and Bi‐gram Probabilities (BiGP) for PPIs detection from protein sequences. The major improvement includes (1) Protein sequences are represented using the Bi‐gram probabilities (BiGP) feature representation on a Position Specific Scoring Matrix (PSSM), in which the protein evolutionary information is contained; (2) For reducing the influence of noise, the Principal Component Analysis (PCA) method is used to reduce the dimension of BiGP vector; (3) The powerful and robust Relevance Vector Machine (RVM) algorithm is used for classification. Five‐fold cross‐validation experiments executed on yeast and Helicobacter pylori datasets, which achieved very high accuracies of 94.57 and 90.57%, respectively. Experimental results are significantly better than previous methods. To further evaluate the proposed method, we compare it with the state‐of‐the‐art support vector machine (SVM) classifier on the yeast dataset. The experimental results demonstrate that our RVM‐BiGP method is significantly better than the SVM‐based method. In addition, we achieved 97.15% accuracy on imbalance yeast dataset, which is higher than that of balance yeast dataset. The promising experimental results show the efficiency and robust of the proposed method, which can be an automatic decision support tool for future proteomics research. For facilitating extensive studies for future proteomics research, we developed a freely available web server called RVM‐BiGP‐PPIs in Hypertext Preprocessor (PHP) for predicting PPIs. The web server including source code and the datasets are available at http://219.219.62.123:8888/BiGP/. PMID:27452983

Cultural evolutionary theory: How culture evolves and why it matters

PubMed Central

Creanza, Nicole; Kolodny, Oren; Feldman, Marcus W.

2017-01-01

Human cultural traits—behaviors, ideas, and technologies that can be learned from other individuals—can exhibit complex patterns of transmission and evolution, and researchers have developed theoretical models, both verbal and mathematical, to facilitate our understanding of these patterns. Many of the first quantitative models of cultural evolution were modified from existing concepts in theoretical population genetics because cultural evolution has many parallels with, as well as clear differences from, genetic evolution. Furthermore, cultural and genetic evolution can interact with one another and influence both transmission and selection. This interaction requires theoretical treatments of gene–culture coevolution and dual inheritance, in addition to purely cultural evolution. In addition, cultural evolutionary theory is a natural component of studies in demography, human ecology, and many other disciplines. Here, we review the core concepts in cultural evolutionary theory as they pertain to the extension of biology through culture, focusing on cultural evolutionary applications in population genetics, ecology, and demography. For each of these disciplines, we review the theoretical literature and highlight relevant empirical studies. We also discuss the societal implications of the study of cultural evolution and of the interactions of humans with one another and with their environment. PMID:28739941

Constraints imposed by pollinator behaviour on the ecology and evolution of plant mating systems.

PubMed

Devaux, C; Lepers, C; Porcher, E

2014-07-01

Most flowering plants rely on pollinators for their reproduction. Plant-pollinator interactions, although mutualistic, involve an inherent conflict of interest between both partners and may constrain plant mating systems at multiple levels: the immediate ecological plant selfing rates, their distribution in and contribution to pollination networks, and their evolution. Here, we review experimental evidence that pollinator behaviour influences plant selfing rates in pairs of interacting species, and that plants can modify pollinator behaviour through plastic and evolutionary changes in floral traits. We also examine how theoretical studies include pollinators, implicitly or explicitly, to investigate the role of their foraging behaviour in plant mating system evolution. In doing so, we call for more evolutionary models combining ecological and genetic factors, and additional experimental data, particularly to describe pollinator foraging behaviour. Finally, we show that recent developments in ecological network theory help clarify the impact of community-level interactions on plant selfing rates and their evolution and suggest new research avenues to expand the study of mating systems of animal-pollinated plant species to the level of the plant-pollinator networks. © 2014 The Authors. Journal of Evolutionary Biology © 2014 European Society For Evolutionary Biology.

Cultural evolutionary theory: How culture evolves and why it matters.

PubMed

Creanza, Nicole; Kolodny, Oren; Feldman, Marcus W

2017-07-24

Human cultural traits-behaviors, ideas, and technologies that can be learned from other individuals-can exhibit complex patterns of transmission and evolution, and researchers have developed theoretical models, both verbal and mathematical, to facilitate our understanding of these patterns. Many of the first quantitative models of cultural evolution were modified from existing concepts in theoretical population genetics because cultural evolution has many parallels with, as well as clear differences from, genetic evolution. Furthermore, cultural and genetic evolution can interact with one another and influence both transmission and selection. This interaction requires theoretical treatments of gene-culture coevolution and dual inheritance, in addition to purely cultural evolution. In addition, cultural evolutionary theory is a natural component of studies in demography, human ecology, and many other disciplines. Here, we review the core concepts in cultural evolutionary theory as they pertain to the extension of biology through culture, focusing on cultural evolutionary applications in population genetics, ecology, and demography. For each of these disciplines, we review the theoretical literature and highlight relevant empirical studies. We also discuss the societal implications of the study of cultural evolution and of the interactions of humans with one another and with their environment.

Evolving Better Cars: Teaching Evolution by Natural Selection with a Digital Inquiry Activity

ERIC Educational Resources Information Center

Royer, Anne M.; Schultheis, Elizabeth H.

2014-01-01

Evolutionary experiments are usually difficult to perform in the classroom because of the large sizes and long timescales of experiments testing evolutionary hypotheses. Computer applications give students a window to observe evolution in action, allowing them to gain comfort with the process of natural selection and facilitating inquiry…

Memetic Algorithms, Domain Knowledge, and Financial Investing

ERIC Educational Resources Information Center

Du, Jie

2012-01-01

While the question of how to use human knowledge to guide evolutionary search is long-recognized, much remains to be done to answer this question adequately. This dissertation aims to further answer this question by exploring the role of domain knowledge in evolutionary computation as applied to real-world, complex problems, such as financial…

Resistance and relatedness on an evolutionary graph

PubMed Central

Maciejewski, Wes

2012-01-01

When investigating evolution in structured populations, it is often convenient to consider the population as an evolutionary graph—individuals as nodes, and whom they may act with as edges. There has, in recent years, been a surge of interest in evolutionary graphs, especially in the study of the evolution of social behaviours. An inclusive fitness framework is best suited for this type of study. A central requirement for an inclusive fitness analysis is an expression for the genetic similarity between individuals residing on the graph. This has been a major hindrance for work in this area as highly technical mathematics are often required. Here, I derive a result that links genetic relatedness between haploid individuals on an evolutionary graph to the resistance between vertices on a corresponding electrical network. An example that demonstrates the potential computational advantage of this result over contemporary approaches is provided. This result offers more, however, to the study of population genetics than strictly computationally efficient methods. By establishing a link between gene transfer and electric circuit theory, conceptualizations of the latter can enhance understanding of the former. PMID:21849384

An Evolutionary Algorithm for Fast Intensity Based Image Matching Between Optical and SAR Satellite Imagery

NASA Astrophysics Data System (ADS)

Fischer, Peter; Schuegraf, Philipp; Merkle, Nina; Storch, Tobias

2018-04-01

This paper presents a hybrid evolutionary algorithm for fast intensity based matching between satellite imagery from SAR and very high-resolution (VHR) optical sensor systems. The precise and accurate co-registration of image time series and images of different sensors is a key task in multi-sensor image processing scenarios. The necessary preprocessing step of image matching and tie-point detection is divided into a search problem and a similarity measurement. Within this paper we evaluate the use of an evolutionary search strategy for establishing the spatial correspondence between satellite imagery of optical and radar sensors. The aim of the proposed algorithm is to decrease the computational costs during the search process by formulating the search as an optimization problem. Based upon the canonical evolutionary algorithm, the proposed algorithm is adapted for SAR/optical imagery intensity based matching. Extensions are drawn using techniques like hybridization (e.g. local search) and others to lower the number of objective function calls and refine the result. The algorithm significantely decreases the computational costs whilst finding the optimal solution in a reliable way.

An evolutionary firefly algorithm for the estimation of nonlinear biological model parameters.

PubMed

Abdullah, Afnizanfaizal; Deris, Safaai; Anwar, Sohail; Arjunan, Satya N V

2013-01-01

The development of accurate computational models of biological processes is fundamental to computational systems biology. These models are usually represented by mathematical expressions that rely heavily on the system parameters. The measurement of these parameters is often difficult. Therefore, they are commonly estimated by fitting the predicted model to the experimental data using optimization methods. The complexity and nonlinearity of the biological processes pose a significant challenge, however, to the development of accurate and fast optimization methods. We introduce a new hybrid optimization method incorporating the Firefly Algorithm and the evolutionary operation of the Differential Evolution method. The proposed method improves solutions by neighbourhood search using evolutionary procedures. Testing our method on models for the arginine catabolism and the negative feedback loop of the p53 signalling pathway, we found that it estimated the parameters with high accuracy and within a reasonable computation time compared to well-known approaches, including Particle Swarm Optimization, Nelder-Mead, and Firefly Algorithm. We have also verified the reliability of the parameters estimated by the method using an a posteriori practical identifiability test.

An Evolutionary Firefly Algorithm for the Estimation of Nonlinear Biological Model Parameters

PubMed Central

Abdullah, Afnizanfaizal; Deris, Safaai; Anwar, Sohail; Arjunan, Satya N. V.

2013-01-01

The development of accurate computational models of biological processes is fundamental to computational systems biology. These models are usually represented by mathematical expressions that rely heavily on the system parameters. The measurement of these parameters is often difficult. Therefore, they are commonly estimated by fitting the predicted model to the experimental data using optimization methods. The complexity and nonlinearity of the biological processes pose a significant challenge, however, to the development of accurate and fast optimization methods. We introduce a new hybrid optimization method incorporating the Firefly Algorithm and the evolutionary operation of the Differential Evolution method. The proposed method improves solutions by neighbourhood search using evolutionary procedures. Testing our method on models for the arginine catabolism and the negative feedback loop of the p53 signalling pathway, we found that it estimated the parameters with high accuracy and within a reasonable computation time compared to well-known approaches, including Particle Swarm Optimization, Nelder-Mead, and Firefly Algorithm. We have also verified the reliability of the parameters estimated by the method using an a posteriori practical identifiability test. PMID:23469172

Development and validation of real-time simulation of X-ray imaging with respiratory motion.

PubMed

Vidal, Franck P; Villard, Pierre-Frédéric

2016-04-01

We present a framework that combines evolutionary optimisation, soft tissue modelling and ray tracing on GPU to simultaneously compute the respiratory motion and X-ray imaging in real-time. Our aim is to provide validated building blocks with high fidelity to closely match both the human physiology and the physics of X-rays. A CPU-based set of algorithms is presented to model organ behaviours during respiration. Soft tissue deformation is computed with an extension of the Chain Mail method. Rigid elements move according to kinematic laws. A GPU-based surface rendering method is proposed to compute the X-ray image using the Beer-Lambert law. It is provided as an open-source library. A quantitative validation study is provided to objectively assess the accuracy of both components: (i) the respiration against anatomical data, and (ii) the X-ray against the Beer-Lambert law and the results of Monte Carlo simulations. Our implementation can be used in various applications, such as interactive medical virtual environment to train percutaneous transhepatic cholangiography in interventional radiology, 2D/3D registration, computation of digitally reconstructed radiograph, simulation of 4D sinograms to test tomography reconstruction tools. Copyright © 2015 Elsevier Ltd. All rights reserved.

EvolQG - An R package for evolutionary quantitative genetics

PubMed Central

Melo, Diogo; Garcia, Guilherme; Hubbe, Alex; Assis, Ana Paula; Marroig, Gabriel

2016-01-01

We present an open source package for performing evolutionary quantitative genetics analyses in the R environment for statistical computing. Evolutionary theory shows that evolution depends critically on the available variation in a given population. When dealing with many quantitative traits this variation is expressed in the form of a covariance matrix, particularly the additive genetic covariance matrix or sometimes the phenotypic matrix, when the genetic matrix is unavailable and there is evidence the phenotypic matrix is sufficiently similar to the genetic matrix. Given this mathematical representation of available variation, the \\textbf{EvolQG} package provides functions for calculation of relevant evolutionary statistics; estimation of sampling error; corrections for this error; matrix comparison via correlations, distances and matrix decomposition; analysis of modularity patterns; and functions for testing evolutionary hypotheses on taxa diversification. PMID:27785352

Mean-Potential Law in Evolutionary Games.

PubMed

Nałęcz-Jawecki, Paweł; Miękisz, Jacek

2018-01-12

The Letter presents a novel way to connect random walks, stochastic differential equations, and evolutionary game theory. We introduce a new concept of a potential function for discrete-space stochastic systems. It is based on a correspondence between one-dimensional stochastic differential equations and random walks, which may be exact not only in the continuous limit but also in finite-state spaces. Our method is useful for computation of fixation probabilities in discrete stochastic dynamical systems with two absorbing states. We apply it to evolutionary games, formulating two simple and intuitive criteria for evolutionary stability of pure Nash equilibria in finite populations. In particular, we show that the 1/3 law of evolutionary games, introduced by Nowak et al. [Nature, 2004], follows from a more general mean-potential law.

Emergence of evolutionary cycles in size-structured food webs.

PubMed

Ritterskamp, Daniel; Bearup, Daniel; Blasius, Bernd

2016-11-07

The interplay of population dynamics and evolution within ecological communities has been of long-standing interest for ecologists and can give rise to evolutionary cycles, e.g. taxon cycles. Evolutionary cycling was intensely studied in small communities with asymmetric competition; the latter drives the evolutionary processes. Here we demonstrate that evolutionary cycling arises naturally in larger communities if trophic interactions are present, since these are intrinsically asymmetric. To investigate the evolutionary dynamics of a trophic community, we use an allometric food web model. We find that evolutionary cycles emerge naturally for a large parameter ranges. The origin of the evolutionary dynamics is an intrinsic asymmetry in the feeding kernel which creates an evolutionary ratchet, driving species towards larger bodysize. We reveal different kinds of cycles: single morph cycles, and coevolutionary and mixed cycling of complete food webs. The latter refers to the case where each trophic level can have different evolutionary dynamics. We discuss the generality of our findings and conclude that ongoing evolution in food webs may be more frequent than commonly believed. Copyright © 2016 Elsevier Ltd. All rights reserved.

Predator and prey functional traits: understanding the adaptive machinery driving predator–prey interactions

PubMed Central

Schmitz, Oswald

2017-01-01

Predator–prey relationships are a central component of community dynamics. Classic approaches have tried to understand and predict these relationships in terms of consumptive interactions between predator and prey species, but characterizing the interaction this way is insufficient to predict the complexity and context dependency inherent in predator–prey relationships. Recent approaches have begun to explore predator–prey relationships in terms of an evolutionary-ecological game in which predator and prey adapt to each other through reciprocal interactions involving context-dependent expression of functional traits that influence their biomechanics. Functional traits are defined as any morphological, behavioral, or physiological trait of an organism associated with a biotic interaction. Such traits include predator and prey body size, predator and prey personality, predator hunting mode, prey mobility, prey anti-predator behavior, and prey physiological stress. Here, I discuss recent advances in this functional trait approach. Evidence shows that the nature and strength of many interactions are dependent upon the relative magnitude of predator and prey functional traits. Moreover, trait responses can be triggered by non-consumptive predator–prey interactions elicited by responses of prey to risk of predation. These interactions in turn can have dynamic feedbacks that can change the context of the predator–prey interaction, causing predator and prey to adapt their traits—through phenotypically plastic or rapid evolutionary responses—and the nature of their interaction. Research shows that examining predator–prey interactions through the lens of an adaptive evolutionary-ecological game offers a foundation to explain variety in the nature and strength of predator–prey interactions observed in different ecological contexts. PMID:29043073

Predator and prey functional traits: understanding the adaptive machinery driving predator-prey interactions.

PubMed

Schmitz, Oswald

2017-01-01

Predator-prey relationships are a central component of community dynamics. Classic approaches have tried to understand and predict these relationships in terms of consumptive interactions between predator and prey species, but characterizing the interaction this way is insufficient to predict the complexity and context dependency inherent in predator-prey relationships. Recent approaches have begun to explore predator-prey relationships in terms of an evolutionary-ecological game in which predator and prey adapt to each other through reciprocal interactions involving context-dependent expression of functional traits that influence their biomechanics. Functional traits are defined as any morphological, behavioral, or physiological trait of an organism associated with a biotic interaction. Such traits include predator and prey body size, predator and prey personality, predator hunting mode, prey mobility, prey anti-predator behavior, and prey physiological stress. Here, I discuss recent advances in this functional trait approach. Evidence shows that the nature and strength of many interactions are dependent upon the relative magnitude of predator and prey functional traits. Moreover, trait responses can be triggered by non-consumptive predator-prey interactions elicited by responses of prey to risk of predation. These interactions in turn can have dynamic feedbacks that can change the context of the predator-prey interaction, causing predator and prey to adapt their traits-through phenotypically plastic or rapid evolutionary responses-and the nature of their interaction. Research shows that examining predator-prey interactions through the lens of an adaptive evolutionary-ecological game offers a foundation to explain variety in the nature and strength of predator-prey interactions observed in different ecological contexts.

Evolutionary versatility of eukaryotic protein domains revealed by their bigram networks

PubMed Central

2011-01-01

Background Protein domains are globular structures of independently folded polypeptides that exert catalytic or binding activities. Their sequences are recognized as evolutionary units that, through genome recombination, constitute protein repertoires of linkage patterns. Via mutations, domains acquire modified functions that contribute to the fitness of cells and organisms. Recent studies have addressed the evolutionary selection that may have shaped the functions of individual domains and the emergence of particular domain combinations, which led to new cellular functions in multi-cellular animals. This study focuses on modeling domain linkage globally and investigates evolutionary implications that may be revealed by novel computational analysis. Results A survey of 77 completely sequenced eukaryotic genomes implies a potential hierarchical and modular organization of biological functions in most living organisms. Domains in a genome or multiple genomes are modeled as a network of hetero-duplex covalent linkages, termed bigrams. A novel computational technique is introduced to decompose such networks, whereby the notion of domain "networking versatility" is derived and measured. The most and least "versatile" domains (termed "core domains" and "peripheral domains" respectively) are examined both computationally via sequence conservation measures and experimentally using selected domains. Our study suggests that such a versatility measure extracted from the bigram networks correlates with the adaptivity of domains during evolution, where the network core domains are highly adaptive, significantly contrasting the network peripheral domains. Conclusions Domain recombination has played a major part in the evolution of eukaryotes attributing to genome complexity. From a system point of view, as the results of selection and constant refinement, networks of domain linkage are structured in a hierarchical modular fashion. Domains with high degree of networking versatility appear to be evolutionary adaptive, potentially through functional innovations. Domain bigram networks are informative as a model of biological functions. The networking versatility indices extracted from such networks for individual domains reflect the strength of evolutionary selection that the domains have experienced. PMID:21849086

Evolutionary versatility of eukaryotic protein domains revealed by their bigram networks.

PubMed

Xie, Xueying; Jin, Jing; Mao, Yongyi

2011-08-18

Protein domains are globular structures of independently folded polypeptides that exert catalytic or binding activities. Their sequences are recognized as evolutionary units that, through genome recombination, constitute protein repertoires of linkage patterns. Via mutations, domains acquire modified functions that contribute to the fitness of cells and organisms. Recent studies have addressed the evolutionary selection that may have shaped the functions of individual domains and the emergence of particular domain combinations, which led to new cellular functions in multi-cellular animals. This study focuses on modeling domain linkage globally and investigates evolutionary implications that may be revealed by novel computational analysis. A survey of 77 completely sequenced eukaryotic genomes implies a potential hierarchical and modular organization of biological functions in most living organisms. Domains in a genome or multiple genomes are modeled as a network of hetero-duplex covalent linkages, termed bigrams. A novel computational technique is introduced to decompose such networks, whereby the notion of domain "networking versatility" is derived and measured. The most and least "versatile" domains (termed "core domains" and "peripheral domains" respectively) are examined both computationally via sequence conservation measures and experimentally using selected domains. Our study suggests that such a versatility measure extracted from the bigram networks correlates with the adaptivity of domains during evolution, where the network core domains are highly adaptive, significantly contrasting the network peripheral domains. Domain recombination has played a major part in the evolution of eukaryotes attributing to genome complexity. From a system point of view, as the results of selection and constant refinement, networks of domain linkage are structured in a hierarchical modular fashion. Domains with high degree of networking versatility appear to be evolutionary adaptive, potentially through functional innovations. Domain bigram networks are informative as a model of biological functions. The networking versatility indices extracted from such networks for individual domains reflect the strength of evolutionary selection that the domains have experienced.

Multifidelity, multidisciplinary optimization of turbomachines with shock interaction

NASA Astrophysics Data System (ADS)

Joly, Michael Marie

Research on high-speed air-breathing propulsion aims at developing aircraft with antipodal range and space access. Before reaching high speed at high altitude, the flight vehicle needs to accelerate from takeoff to scramjet takeover. Air turbo rocket engines combine turbojet and rocket engine cycles to provide the necessary thrust in the so-called low-speed regime. Challenges related to turbomachinery components are multidisciplinary, since both the high compression ratio compressor and the powering high-pressure turbine operate in the transonic regime in compact environments with strong shock interactions. Besides, lightweight is vital to avoid hindering the scramjet operation. Recent progress in evolutionary computing provides aerospace engineers with robust and efficient optimization algorithms to address concurrent objectives. The present work investigates Multidisciplinary Design Optimization (MDO) of innovative transonic turbomachinery components. Inter-stage aerodynamic shock interaction in turbomachines are known to generate high-cycle fatigue on the rotor blades compromising their structural integrity. A soft-computing strategy is proposed to mitigate the vane downstream distortion, and shown to successfully attenuate the unsteady forcing on the rotor of a high-pressure turbine. Counter-rotation offers promising prospects to reduce the weight of the machine, with fewer stages and increased load per row. An integrated approach based on increasing level of fidelity and aero-structural coupling is then presented and allows achieving a highly loaded compact counter-rotating compressor.

HVint: A Strategy for Identifying Novel Protein-Protein Interactions in Herpes Simplex Virus Type 1*

PubMed Central

Hernandez, Anna; Buch, Anna; Sodeik, Beate; Cristea, Ileana Mihaela

2016-01-01

Human herpesviruses are widespread human pathogens with a remarkable impact on worldwide public health. Despite intense decades of research, the molecular details in many aspects of their function remain to be fully characterized. To unravel the details of how these viruses operate, a thorough understanding of the relationships between the involved components is key. Here, we present HVint, a novel protein-protein intraviral interaction resource for herpes simplex virus type 1 (HSV-1) integrating data from five external sources. To assess each interaction, we used a scoring scheme that takes into consideration aspects such as the type of detection method and the number of lines of evidence. The coverage of the initial interactome was further increased using evolutionary information, by importing interactions reported for other human herpesviruses. These latter interactions constitute, therefore, computational predictions for potential novel interactions in HSV-1. An independent experimental analysis was performed to confirm a subset of our predicted interactions. This subset covers proteins that contribute to nuclear egress and primary envelopment events, including VP26, pUL31, pUL40, and the recently characterized pUL32 and pUL21. Our findings support a coordinated crosstalk between VP26 and proteins such as pUL31, pUS9, and the CSVC complex, contributing to the development of a model describing the nuclear egress and primary envelopment pathways of newly synthesized HSV-1 capsids. The results are also consistent with recent findings on the involvement of pUL32 in capsid maturation and early tegumentation events. Further, they open the door to new hypotheses on virus-specific regulators of pUS9-dependent transport. To make this repository of interactions readily accessible for the scientific community, we also developed a user-friendly and interactive web interface. Our approach demonstrates the power of computational predictions to assist in the design of targeted experiments for the discovery of novel protein-protein interactions. PMID:27384951

Robust controller design for flexible structures using normalized coprime factor plant descriptions

NASA Technical Reports Server (NTRS)

Armstrong, Ernest S.

1993-01-01

Stabilization is a fundamental requirement in the design of feedback compensators for flexible structures. The search for the largest neighborhood around a given design plant for which a single controller produces closed-loop stability can be formulated as an H(sub infinity) control problem. The use of normalized coprime factor plant descriptions, in which the plant perturbations are defined as additive modifications to the coprime factors, leads to a closed-form expression for the maximum neighborhood boundary allowing optimal and suboptimal H(sub infinity) compensators to be computed directly without the usual gamma iteration. A summary of the theory on robust stabilization using normalized coprime factor plant descriptions is presented, and the application of the theory to the computation of robustly stable compensators for the phase version of the Control-Structures Interaction (CSI) Evolutionary Model is described. Results from the application indicate that the suboptimal version of the theory has the potential of providing the bases for the computation of low-authority compensators that are robustly stable to expected variations in design model parameters and additive unmodeled dynamics.

Evolving learning rules and emergence of cooperation in spatial prisoner's dilemma.

PubMed

Moyano, Luis G; Sánchez, Angel

2009-07-07

In the evolutionary Prisoner's dilemma (PD) game, agents play with each other and update their strategies in every generation according to some microscopic dynamical rule. In its spatial version, agents do not play with every other but, instead, interact only with their neighbours, thus mimicking the existing of a social or contact network that defines who interacts with whom. In this work, we explore evolutionary, spatial PD systems consisting of two types of agents, each with a certain update (reproduction, learning) rule. We investigate two different scenarios: in the first case, update rules remain fixed for the entire evolution of the system; in the second case, agents update both strategy and update rule in every generation. We show that in a well-mixed population the evolutionary outcome is always full defection. We subsequently focus on two-strategy competition with nearest-neighbour interactions on the contact network and synchronised update of strategies. Our results show that, for an important range of the parameters of the game, the final state of the system is largely different from that arising from the usual setup of a single, fixed dynamical rule. Furthermore, the results are also very different if update rules are fixed or evolve with the strategies. In these respect, we have studied representative update rules, finding that some of them may become extinct while others prevail. We describe the new and rich variety of final outcomes that arise from this co-evolutionary dynamics. We include examples of other neighbourhoods and asynchronous updating that confirm the robustness of our conclusions. Our results pave the way to an evolutionary rationale for modelling social interactions through game theory with a preferred set of update rules.

Evolutionary origins of leadership and followership.

PubMed

Van Vugt, Mark

2006-01-01

Drawing upon evolutionary logic, leadership is reconceptualized in terms of the outcome of strategic interactions among individuals who are following different, yet complementary, decision rules to solve recurrent coordination problems. This article uses the vast psychological literature on leadership as a database to test several evolutionary hypotheses about the origins of leadership and followership in humans. As expected, leadership correlates with initiative taking, trait measures of intelligence, specific task competencies, and several indicators of generosity. The review finds no link between leadership and dominance. The evolutionary analysis accounts for reliable age, health, and sex differences in leadership emergence. In general, evolutionary theory provides a useful, integrative framework for studying leader-follower relationships and generates various novel research hypotheses.

Evolutionary dynamics with fluctuating population sizes and strong mutualism.

PubMed

Chotibut, Thiparat; Nelson, David R

2015-08-01

Game theory ideas provide a useful framework for studying evolutionary dynamics in a well-mixed environment. This approach, however, typically enforces a strictly fixed overall population size, deemphasizing natural growth processes. We study a competitive Lotka-Volterra model, with number fluctuations, that accounts for natural population growth and encompasses interaction scenarios typical of evolutionary games. We show that, in an appropriate limit, the model describes standard evolutionary games with both genetic drift and overall population size fluctuations. However, there are also regimes where a varying population size can strongly influence the evolutionary dynamics. We focus on the strong mutualism scenario and demonstrate that standard evolutionary game theory fails to describe our simulation results. We then analytically and numerically determine fixation probabilities as well as mean fixation times using matched asymptotic expansions, taking into account the population size degree of freedom. These results elucidate the interplay between population dynamics and evolutionary dynamics in well-mixed systems.

Evolutionary dynamics with fluctuating population sizes and strong mutualism

NASA Astrophysics Data System (ADS)

Chotibut, Thiparat; Nelson, David R.

2015-08-01

Game theory ideas provide a useful framework for studying evolutionary dynamics in a well-mixed environment. This approach, however, typically enforces a strictly fixed overall population size, deemphasizing natural growth processes. We study a competitive Lotka-Volterra model, with number fluctuations, that accounts for natural population growth and encompasses interaction scenarios typical of evolutionary games. We show that, in an appropriate limit, the model describes standard evolutionary games with both genetic drift and overall population size fluctuations. However, there are also regimes where a varying population size can strongly influence the evolutionary dynamics. We focus on the strong mutualism scenario and demonstrate that standard evolutionary game theory fails to describe our simulation results. We then analytically and numerically determine fixation probabilities as well as mean fixation times using matched asymptotic expansions, taking into account the population size degree of freedom. These results elucidate the interplay between population dynamics and evolutionary dynamics in well-mixed systems.

The role of epistatic interactions underpinning resistance to parasitic Varroa mites in haploid honey bee (Apis mellifera) drones.

PubMed

Conlon, Benjamin H; Frey, Eva; Rosenkranz, Peter; Locke, Barbara; Moritz, Robin F A; Routtu, Jarkko

2018-06-01

The Red Queen hypothesis predicts that host-parasite coevolutionary dynamics can select for host resistance through increased genetic diversity, recombination and evolutionary rates. However, in haplodiploid organisms such as the honeybee (Apis mellifera), models suggest the selective pressure is weaker than in diploids. Haplodiploid sex determination, found in A. mellifera, can allow deleterious recessive alleles to persist in the population through the diploid sex with negative effects predominantly expressed in the haploid sex. To overcome these negative effects in haploid genomes, epistatic interactions have been hypothesized to play an important role. Here, we use the interaction between A. mellifera and the parasitic mite Varroa destructor to test epistasis in the expression of resistance, through the inhibition of parasite reproduction, in haploid drones. We find novel loci on three chromosomes which explain over 45% of the resistance phenotype. Two of these loci interact only additively, suggesting their expression is independent of each other, but both loci interact epistatically with the third locus. With drone offspring inheriting only one copy of the queen's chromosomes, the drones will only possess one of two queen alleles throughout the years-long lifetime of the honeybee colony. Varroa, in comparison, completes its highly inbred reproductive cycle in a matter of weeks, allowing it to rapidly evolve resistance. Faced with the rapidly evolving Varroa, a diversity of pathways and epistatic interactions for the inhibition of Varroa reproduction could therefore provide a selective advantage to the high levels of recombination seen in A. mellifera. This allows for the remixing of phenotypes despite a fixed queen genotype. © 2018 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2018 European Society For Evolutionary Biology.

An evolutionary conserved pattern of 18S rRNA sequence complementarity to mRNA 5′ UTRs and its implications for eukaryotic gene translation regulation

PubMed Central

Pánek, Josef; Kolář, Michal; Vohradský, Jiří; Shivaya Valášek, Leoš

2013-01-01

There are several key mechanisms regulating eukaryotic gene expression at the level of protein synthesis. Interestingly, the least explored mechanisms of translational control are those that involve the translating ribosome per se, mediated for example via predicted interactions between the ribosomal RNAs (rRNAs) and mRNAs. Here, we took advantage of robustly growing large-scale data sets of mRNA sequences for numerous organisms, solved ribosomal structures and computational power to computationally explore the mRNA–rRNA complementarity that is statistically significant across the species. Our predictions reveal highly specific sequence complementarity of 18S rRNA sequences with mRNA 5′ untranslated regions (UTRs) forming a well-defined 3D pattern on the rRNA sequence of the 40S subunit. Broader evolutionary conservation of this pattern may imply that 5′ UTRs of eukaryotic mRNAs, which have already emerged from the mRNA-binding channel, may contact several complementary spots on 18S rRNA situated near the exit of the mRNA binding channel and on the middle-to-lower body of the solvent-exposed 40S ribosome including its left foot. We discuss physiological significance of this structurally conserved pattern and, in the context of previously published experimental results, propose that it modulates scanning of the 40S subunit through 5′ UTRs of mRNAs. PMID:23804757

Exploiting Genomic Knowledge in Optimising Molecular Breeding Programmes: Algorithms from Evolutionary Computing

PubMed Central

O'Hagan, Steve; Knowles, Joshua; Kell, Douglas B.

2012-01-01

Comparatively few studies have addressed directly the question of quantifying the benefits to be had from using molecular genetic markers in experimental breeding programmes (e.g. for improved crops and livestock), nor the question of which organisms should be mated with each other to best effect. We argue that this requires in silico modelling, an approach for which there is a large literature in the field of evolutionary computation (EC), but which has not really been applied in this way to experimental breeding programmes. EC seeks to optimise measurable outcomes (phenotypic fitnesses) by optimising in silico the mutation, recombination and selection regimes that are used. We review some of the approaches from EC, and compare experimentally, using a biologically relevant in silico landscape, some algorithms that have knowledge of where they are in the (genotypic) search space (G-algorithms) with some (albeit well-tuned ones) that do not (F-algorithms). For the present kinds of landscapes, F- and G-algorithms were broadly comparable in quality and effectiveness, although we recognise that the G-algorithms were not equipped with any ‘prior knowledge’ of epistatic pathway interactions. This use of algorithms based on machine learning has important implications for the optimisation of experimental breeding programmes in the post-genomic era when we shall potentially have access to the full genome sequence of every organism in a breeding population. The non-proprietary code that we have used is made freely available (via Supplementary information). PMID:23185279

The 'Biologically-Inspired Computing' Column

NASA Technical Reports Server (NTRS)

Hinchey, Mike

2006-01-01

The field of Biology changed dramatically in 1953, with the determination by Francis Crick and James Dewey Watson of the double helix structure of DNA. This discovery changed Biology for ever, allowing the sequencing of the human genome, and the emergence of a "new Biology" focused on DNA, genes, proteins, data, and search. Computational Biology and Bioinformatics heavily rely on computing to facilitate research into life and development. Simultaneously, an understanding of the biology of living organisms indicates a parallel with computing systems: molecules in living cells interact, grow, and transform according to the "program" dictated by DNA. Moreover, paradigms of Computing are emerging based on modelling and developing computer-based systems exploiting ideas that are observed in nature. This includes building into computer systems self-management and self-governance mechanisms that are inspired by the human body's autonomic nervous system, modelling evolutionary systems analogous to colonies of ants or other insects, and developing highly-efficient and highly-complex distributed systems from large numbers of (often quite simple) largely homogeneous components to reflect the behaviour of flocks of birds, swarms of bees, herds of animals, or schools of fish. This new field of "Biologically-Inspired Computing", often known in other incarnations by other names, such as: Autonomic Computing, Pervasive Computing, Organic Computing, Biomimetics, and Artificial Life, amongst others, is poised at the intersection of Computer Science, Engineering, Mathematics, and the Life Sciences. Successes have been reported in the fields of drug discovery, data communications, computer animation, control and command, exploration systems for space, undersea, and harsh environments, to name but a few, and augur much promise for future progress.

Evolutionary computing for the design search and optimization of space vehicle power subsystems

NASA Technical Reports Server (NTRS)

Kordon, Mark; Klimeck, Gerhard; Hanks, David; Hua, Hook

2004-01-01

Evolutionary computing has proven to be a straightforward and robust approach for optimizing a wide range of difficult analysis and design problems. This paper discusses the application of these techniques to an existing space vehicle power subsystem resource and performance analysis simulation in a parallel processing environment. Out preliminary results demonstrate that this approach has the potential to improve the space system trade study process by allowing engineers to statistically weight subsystem goals of mass, cost and performance then automatically size power elements based on anticipated performance of the subsystem rather than on worst-case estimates.

Ecological perspectives on synthetic biology: insights from microbial population biology

PubMed Central

Escalante, Ana E.; Rebolleda-Gómez, María; Benítez, Mariana; Travisano, Michael

2015-01-01

The metabolic capabilities of microbes are the basis for many major biotechnological advances, exploiting microbial diversity by selection or engineering of single strains. However, there are limits to the advances that can be achieved with single strains, and attention has turned toward the metabolic potential of consortia and the field of synthetic ecology. The main challenge for the synthetic ecology is that consortia are frequently unstable, largely because evolution by constituent members affects their interactions, which are the basis of collective metabolic functionality. Current practices in modeling consortia largely consider interactions as fixed circuits of chemical reactions, which greatly increases their tractability. This simplification comes at the cost of essential biological realism, stripping out the ecological context in which the metabolic actions occur and the potential for evolutionary change. In other words, evolutionary stability is not engineered into the system. This realization highlights the necessity to better identify the key components that influence the stable coexistence of microorganisms. Inclusion of ecological and evolutionary principles, in addition to biophysical variables and stoichiometric modeling of metabolism, is critical for microbial consortia design. This review aims to bring ecological and evolutionary concepts to the discussion on the stability of microbial consortia. In particular, we focus on the combined effect of spatial structure (connectivity of molecules and cells within the system) and ecological interactions (reciprocal and non-reciprocal) on the persistence of microbial consortia. We discuss exemplary cases to illustrate these ideas from published studies in evolutionary biology and biotechnology. We conclude by making clear the relevance of incorporating evolutionary and ecological principles to the design of microbial consortia, as a way of achieving evolutionarily stable and sustainable systems. PMID:25767468

Introduction to bioinformatics.

PubMed

Can, Tolga

2014-01-01

Bioinformatics is an interdisciplinary field mainly involving molecular biology and genetics, computer science, mathematics, and statistics. Data intensive, large-scale biological problems are addressed from a computational point of view. The most common problems are modeling biological processes at the molecular level and making inferences from collected data. A bioinformatics solution usually involves the following steps: Collect statistics from biological data. Build a computational model. Solve a computational modeling problem. Test and evaluate a computational algorithm. This chapter gives a brief introduction to bioinformatics by first providing an introduction to biological terminology and then discussing some classical bioinformatics problems organized by the types of data sources. Sequence analysis is the analysis of DNA and protein sequences for clues regarding function and includes subproblems such as identification of homologs, multiple sequence alignment, searching sequence patterns, and evolutionary analyses. Protein structures are three-dimensional data and the associated problems are structure prediction (secondary and tertiary), analysis of protein structures for clues regarding function, and structural alignment. Gene expression data is usually represented as matrices and analysis of microarray data mostly involves statistics analysis, classification, and clustering approaches. Biological networks such as gene regulatory networks, metabolic pathways, and protein-protein interaction networks are usually modeled as graphs and graph theoretic approaches are used to solve associated problems such as construction and analysis of large-scale networks.

The Evolution of Biological Complexity in Digital Organisms

NASA Astrophysics Data System (ADS)

Ofria, Charles

2013-03-01

When Darwin first proposed his theory of evolution by natural selection, he realized that it had a problem explaining the origins of traits of ``extreme perfection and complication'' such as the vertebrate eye. Critics of Darwin's theory have latched onto this perceived flaw as a proof that Darwinian evolution is impossible. In anticipation of this issue, Darwin described the perfect data needed to understand this process, but lamented that such data are ``scarcely ever possible'' to obtain. In this talk, I will discuss research where we use populations of digital organisms (self-replicating and evolving computer programs) to elucidate the genetic and evolutionary processes by which new, highly-complex traits arise, drawing inspiration directly from Darwin's wistful thinking and hypotheses. During the process of evolution in these fully-transparent computational environments we can measure the incorporation of new information into the genome, a process akin to a natural Maxwell's Demon, and identify the original source of any such information. We show that, as Darwin predicted, much of the information used to encode a complex trait was already in the genome as part of simpler evolved traits, and that many routes must be possible for a new complex trait to have a high probability of successfully evolving. In even more extreme examples of the evolution of complexity, we are now using these same principles to examine the evolutionary dynamics the drive major transitions in evolution; that is transitions to higher-levels of organization, which are some of the most complex evolutionary events to occur in nature. Finally, I will explore some of the implications of this research to other aspects of evolutionary biology and as well as ways that these evolutionary principles can be applied toward solving computational and engineering problems.

Artificial intelligence in peer review: How can evolutionary computation support journal editors?

PubMed

Mrowinski, Maciej J; Fronczak, Piotr; Fronczak, Agata; Ausloos, Marcel; Nedic, Olgica

2017-01-01

With the volume of manuscripts submitted for publication growing every year, the deficiencies of peer review (e.g. long review times) are becoming more apparent. Editorial strategies, sets of guidelines designed to speed up the process and reduce editors' workloads, are treated as trade secrets by publishing houses and are not shared publicly. To improve the effectiveness of their strategies, editors in small publishing groups are faced with undertaking an iterative trial-and-error approach. We show that Cartesian Genetic Programming, a nature-inspired evolutionary algorithm, can dramatically improve editorial strategies. The artificially evolved strategy reduced the duration of the peer review process by 30%, without increasing the pool of reviewers (in comparison to a typical human-developed strategy). Evolutionary computation has typically been used in technological processes or biological ecosystems. Our results demonstrate that genetic programs can improve real-world social systems that are usually much harder to understand and control than physical systems.

Evolutionary fuzzy modeling human diagnostic decisions.

PubMed

Peña-Reyes, Carlos Andrés

2004-05-01

Fuzzy CoCo is a methodology, combining fuzzy logic and evolutionary computation, for constructing systems able to accurately predict the outcome of a human decision-making process, while providing an understandable explanation of the underlying reasoning. Fuzzy logic provides a formal framework for constructing systems exhibiting both good numeric performance (accuracy) and linguistic representation (interpretability). However, fuzzy modeling--meaning the construction of fuzzy systems--is an arduous task, demanding the identification of many parameters. To solve it, we use evolutionary computation techniques (specifically cooperative coevolution), which are widely used to search for adequate solutions in complex spaces. We have successfully applied the algorithm to model the decision processes involved in two breast cancer diagnostic problems, the WBCD problem and the Catalonia mammography interpretation problem, obtaining systems both of high performance and high interpretability. For the Catalonia problem, an evolved system was embedded within a Web-based tool-called COBRA-for aiding radiologists in mammography interpretation.

Development of a population of cancer cells: Observation and modeling by a Mixed Spatial Evolutionary Games approach.

PubMed

Świerniak, Andrzej; Krześlak, Michał; Student, Sebastian; Rzeszowska-Wolny, Joanna

2016-09-21

Living cells, like whole living organisms during evolution, communicate with their neighbors, interact with the environment, divide, change their phenotypes, and eventually die. The development of specific ways of communication (through signaling molecules and receptors) allows some cellular subpopulations to survive better, to coordinate their physiological status, and during embryonal development to create tissues and organs or in some conditions to become tumors. Populations of cells cultured in vitro interact similarly, also competing for space and nutrients and stimulating each other to better survive or to die. The results of these intercellular interactions of different types seem to be good examples of biological evolutionary games, and have been the subjects of simulations by the methods of evolutionary game theory where individual cells are treated as players. Here we present examples of intercellular contacts in a population of living human cancer HeLa cells cultured in vitro and propose an evolutionary game theory approach to model the development of such populations. We propose a new technique termed Mixed Spatial Evolutionary Games (MSEG) which are played on multiple lattices corresponding to the possible cellular phenotypes which gives the possibility of simulating and investigating the effects of heterogeneity at the cellular level in addition to the population level. Analyses performed with MSEG suggested different ways in which cellular populations develop in the case of cells communicating directly and through factors released to the environment. Copyright © 2016 Elsevier Ltd. All rights reserved.

Tangled nature: a model of evolutionary ecology.

PubMed

Christensen, Kim; di Collobiano, Simone A; Hall, Matt; Jensen, Henrik J

2002-05-07

We discuss a simple model of co-evolution. In order to emphasize the effect of interaction between individuals, the entire population is subjected to the same physical environment. Species are emergent structures and extinction, origination and diversity are entirely a consequence of co-evolutionary interaction between individuals. For comparison, we consider both asexual and sexually reproducing populations. In either case, the system evolves through periods of hectic reorganization separated by periods of coherent stable coexistence. Copyright 2002 Elsevier Science Ltd. All rights reserved.

A Bright Future for Evolutionary Methods in Drug Design.

PubMed

Le, Tu C; Winkler, David A

2015-08-01

Most medicinal chemists understand that chemical space is extremely large, essentially infinite. Although high-throughput experimental methods allow exploration of drug-like space more rapidly, they are still insufficient to fully exploit the opportunities that such large chemical space offers. Evolutionary methods can synergistically blend automated synthesis and characterization methods with computational design to identify promising regions of chemical space more efficiently. We describe how evolutionary methods are implemented, and provide examples of published drug development research in which these methods have generated molecules with increased efficacy. We anticipate that evolutionary methods will play an important role in future drug discovery. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

EvoluZion: A Computer Simulator for Teaching Genetic and Evolutionary Concepts

ERIC Educational Resources Information Center

Zurita, Adolfo R.

2017-01-01

EvoluZion is a forward-in-time genetic simulator developed in Java and designed to perform real time simulations on the evolutionary history of virtual organisms. These model organisms harbour a set of 13 genes that codify an equal number of phenotypic features. These genes change randomly during replication, and mutant genes can have null,…

Evolutionary games on graphs

NASA Astrophysics Data System (ADS)

Szabó, György; Fáth, Gábor

2007-07-01

Game theory is one of the key paradigms behind many scientific disciplines from biology to behavioral sciences to economics. In its evolutionary form and especially when the interacting agents are linked in a specific social network the underlying solution concepts and methods are very similar to those applied in non-equilibrium statistical physics. This review gives a tutorial-type overview of the field for physicists. The first four sections introduce the necessary background in classical and evolutionary game theory from the basic definitions to the most important results. The fifth section surveys the topological complications implied by non-mean-field-type social network structures in general. The next three sections discuss in detail the dynamic behavior of three prominent classes of models: the Prisoner's Dilemma, the Rock-Scissors-Paper game, and Competing Associations. The major theme of the review is in what sense and how the graph structure of interactions can modify and enrich the picture of long term behavioral patterns emerging in evolutionary games.

Consumer co-evolution as an important component of the eco-evolutionary feedback.

PubMed

Hiltunen, Teppo; Becks, Lutz

2014-10-22

Rapid evolution in ecologically relevant traits has recently been recognized to significantly alter the interaction between consumers and their resources, a key interaction in all ecological communities. While these eco-evolutionary dynamics have been shown to occur when prey populations are evolving, little is known about the role of predator evolution and co-evolution between predator and prey in this context. Here, we investigate the role of consumer co-evolution for eco-evolutionary feedback in bacteria-ciliate microcosm experiments by manipulating the initial trait variation in the predator populations. With co-evolved predators, prey evolve anti-predatory defences faster, trait values are more variable, and predator and prey population sizes are larger at the end of the experiment compared with the non-co-evolved predators. Most importantly, differences in predator traits results in a shift from evolution driving ecology, to ecology driving evolution. Thus we demonstrate that predator co-evolution has important effects on eco-evolutionary dynamics.

SLiM 2: Flexible, Interactive Forward Genetic Simulations.

PubMed

Haller, Benjamin C; Messer, Philipp W

2017-01-01

Modern population genomic datasets hold immense promise for revealing the evolutionary processes operating in natural populations, but a crucial prerequisite for this goal is the ability to model realistic evolutionary scenarios and predict their expected patterns in genomic data. To that end, we present SLiM 2: an evolutionary simulation framework that combines a powerful, fast engine for forward population genetic simulations with the capability of modeling a wide variety of complex evolutionary scenarios. SLiM achieves this flexibility through scriptability, which provides control over most aspects of the simulated evolutionary scenarios with a simple R-like scripting language called Eidos. An example SLiM simulation is presented to illustrate the power of this approach. SLiM 2 also includes a graphical user interface for simulation construction, interactive runtime control, and dynamic visualization of simulation output, facilitating easy and fast model development with quick prototyping and visual debugging. We conclude with a performance comparison between SLiM and two other popular forward genetic simulation packages. © The Author 2016. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Generative Representations for Computer-Automated Evolutionary Design

NASA Technical Reports Server (NTRS)

Hornby, Gregory S.

2006-01-01

With the increasing computational power of computers, software design systems are progressing from being tools for architects and designers to express their ideas to tools capable of creating designs under human guidance. One of the main limitations for these computer-automated design systems is the representation with which they encode designs. If the representation cannot encode a certain design, then the design system cannot produce it. To be able to produce new types of designs, and not just optimize pre-defined parameterizations, evolutionary design systems must use generative representations. Generative representations are assembly procedures, or algorithms, for constructing a design thereby allowing for truly novel design solutions to be encoded. In addition, by enabling modularity, regularity and hierarchy, the level of sophistication that can be evolved is increased. We demonstrate the advantages of generative representations on two different design domains: the evolution of spacecraft antennas and the evolution of 3D objects.

Delay-time distribution of core-collapse supernovae with late events resulting from binary interaction

NASA Astrophysics Data System (ADS)

Zapartas, E.; de Mink, S. E.; Izzard, R. G.; Yoon, S.-C.; Badenes, C.; Götberg, Y.; de Koter, A.; Neijssel, C. J.; Renzo, M.; Schootemeijer, A.; Shrotriya, T. S.

2017-05-01

Most massive stars, the progenitors of core-collapse supernovae, are in close binary systems and may interact with their companion through mass transfer or merging. We undertake a population synthesis study to compute the delay-time distribution of core-collapse supernovae, that is, the supernova rate versus time following a starburst, taking into account binary interactions. We test the systematic robustness of our results by running various simulations to account for the uncertainties in our standard assumptions. We find that a significant fraction, %, of core-collapse supernovae are "late", that is, they occur 50-200 Myr after birth, when all massive single stars have already exploded. These late events originate predominantly from binary systems with at least one, or, in most cases, with both stars initially being of intermediate mass (4-8 M⊙). The main evolutionary channels that contribute often involve either the merging of the initially more massive primary star with its companion or the engulfment of the remaining core of the primary by the expanding secondary that has accreted mass at an earlier evolutionary stage. Also, the total number of core-collapse supernovae increases by % because of binarity for the same initial stellar mass. The high rate implies that we should have already observed such late core-collapse supernovae, but have not recognized them as such. We argue that φ Persei is a likely progenitor and that eccentric neutron star - white dwarf systems are likely descendants. Late events can help explain the discrepancy in the delay-time distributions derived from supernova remnants in the Magellanic Clouds and extragalactic type Ia events, lowering the contribution of prompt Ia events. We discuss ways to test these predictions and speculate on the implications for supernova feedback in simulations of galaxy evolution.

Predicting rates of interspecific interaction from phylogenetic trees.

PubMed

Nuismer, Scott L; Harmon, Luke J

2015-01-01

Integrating phylogenetic information can potentially improve our ability to explain species' traits, patterns of community assembly, the network structure of communities, and ecosystem function. In this study, we use mathematical models to explore the ecological and evolutionary factors that modulate the explanatory power of phylogenetic information for communities of species that interact within a single trophic level. We find that phylogenetic relationships among species can influence trait evolution and rates of interaction among species, but only under particular models of species interaction. For example, when interactions within communities are mediated by a mechanism of phenotype matching, phylogenetic trees make specific predictions about trait evolution and rates of interaction. In contrast, if interactions within a community depend on a mechanism of phenotype differences, phylogenetic information has little, if any, predictive power for trait evolution and interaction rate. Together, these results make clear and testable predictions for when and how evolutionary history is expected to influence contemporary rates of species interaction. © 2014 John Wiley & Sons Ltd/CNRS.

Deciphering the Interdependence between Ecological and Evolutionary Networks.

PubMed

Melián, Carlos J; Matthews, Blake; de Andreazzi, Cecilia S; Rodríguez, Jorge P; Harmon, Luke J; Fortuna, Miguel A

2018-05-24

Biological systems consist of elements that interact within and across hierarchical levels. For example, interactions among genes determine traits of individuals, competitive and cooperative interactions among individuals influence population dynamics, and interactions among species affect the dynamics of communities and ecosystem processes. Such systems can be represented as hierarchical networks, but can have complex dynamics when interdependencies among levels of the hierarchy occur. We propose integrating ecological and evolutionary processes in hierarchical networks to explore interdependencies in biological systems. We connect gene networks underlying predator-prey trait distributions to food webs. Our approach addresses longstanding questions about how complex traits and intraspecific trait variation affect the interdependencies among biological levels and the stability of meta-ecosystems. Copyright © 2018 Elsevier Ltd. All rights reserved.

Cancer evolution: mathematical models and computational inference.

PubMed

Beerenwinkel, Niko; Schwarz, Roland F; Gerstung, Moritz; Markowetz, Florian

2015-01-01

Cancer is a somatic evolutionary process characterized by the accumulation of mutations, which contribute to tumor growth, clinical progression, immune escape, and drug resistance development. Evolutionary theory can be used to analyze the dynamics of tumor cell populations and to make inference about the evolutionary history of a tumor from molecular data. We review recent approaches to modeling the evolution of cancer, including population dynamics models of tumor initiation and progression, phylogenetic methods to model the evolutionary relationship between tumor subclones, and probabilistic graphical models to describe dependencies among mutations. Evolutionary modeling helps to understand how tumors arise and will also play an increasingly important prognostic role in predicting disease progression and the outcome of medical interventions, such as targeted therapy. © The Author(s) 2014. Published by Oxford University Press on behalf of the Society of Systematic Biologists.

Predicting protein complexes from weighted protein-protein interaction graphs with a novel unsupervised methodology: Evolutionary enhanced Markov clustering.

PubMed

Theofilatos, Konstantinos; Pavlopoulou, Niki; Papasavvas, Christoforos; Likothanassis, Spiros; Dimitrakopoulos, Christos; Georgopoulos, Efstratios; Moschopoulos, Charalampos; Mavroudi, Seferina

2015-03-01

Proteins are considered to be the most important individual components of biological systems and they combine to form physical protein complexes which are responsible for certain molecular functions. Despite the large availability of protein-protein interaction (PPI) information, not much information is available about protein complexes. Experimental methods are limited in terms of time, efficiency, cost and performance constraints. Existing computational methods have provided encouraging preliminary results, but they phase certain disadvantages as they require parameter tuning, some of them cannot handle weighted PPI data and others do not allow a protein to participate in more than one protein complex. In the present paper, we propose a new fully unsupervised methodology for predicting protein complexes from weighted PPI graphs. The proposed methodology is called evolutionary enhanced Markov clustering (EE-MC) and it is a hybrid combination of an adaptive evolutionary algorithm and a state-of-the-art clustering algorithm named enhanced Markov clustering. EE-MC was compared with state-of-the-art methodologies when applied to datasets from the human and the yeast Saccharomyces cerevisiae organisms. Using public available datasets, EE-MC outperformed existing methodologies (in some datasets the separation metric was increased by 10-20%). Moreover, when applied to new human datasets its performance was encouraging in the prediction of protein complexes which consist of proteins with high functional similarity. In specific, 5737 protein complexes were predicted and 72.58% of them are enriched for at least one gene ontology (GO) function term. EE-MC is by design able to overcome intrinsic limitations of existing methodologies such as their inability to handle weighted PPI networks, their constraint to assign every protein in exactly one cluster and the difficulties they face concerning the parameter tuning. This fact was experimentally validated and moreover, new potentially true human protein complexes were suggested as candidates for further validation using experimental techniques. Copyright © 2015 Elsevier B.V. All rights reserved.

Much more than a ratio: multivariate selection on female bodies.

PubMed

Brooks, R; Shelly, J P; Fan, J; Zhai, L; Chau, D K P

2010-10-01

Studies of the attractiveness of female bodies have focussed strongly on the waist, hips and bust, but sexual selection operates on whole phenotypes rather than the relative proportions of just two or three body parts. Here, we use body scanners to extract computer-generated images of 96 Chinese women's bodies with all traits unrelated to body shape removed. We first show that Chinese and Australian men and women rate the attractiveness of these bodies the same. We then statistically explore the roles of age, body weight and a range of length and girth measures on ratings of attractiveness. Last, we use nonlinear selection analysis, a statistical approach developed by evolutionary biologists to explore the interacting effects of suites of traits on fitness, to study how body traits interact to determine attractiveness. Established proxies of adiposity and reproductive value, including age, body mass index and waist-to-hip ratio, were all correlated with attractiveness. Nonlinear response surface methods using the original traits consistently outperform all of these indices and ratios, suggesting that indices of youth and abdominal adiposity tell only part of the story of body attractiveness. In particular, our findings draw attention to the importance of integration between abdominal measures, including the bust, and the length and girth of limbs. Our results provide the most comprehensive analysis to date of the effect of body shape and fat deposition on female attractiveness. © 2010 The Authors. Journal Compilation © 2010 European Society For Evolutionary Biology.

Diversity of neighborhoods promotes cooperation in evolutionary social dilemmas

NASA Astrophysics Data System (ADS)

Ma, Yongjuan; Lu, Jun; Shi, Lei

2017-02-01

Explaining the evolution of cooperative behavior is one of the most important and interesting problems in a myriad of disciplines, such as evolutionary biology, mathematics, statistical physics, social science and economics Up to now, there have been a great number of works aiming to this issue with the help of evolutionary game theory. However, vast majority of existing literatures simply assume that the interaction neighborhood and replacement neighborhood are symmetric, which seems inconsistent with real-world cases. In this paper, we consider the asymmetrical neighborhood: player of type A, whose factor is controlled by a parameter τ, has four interaction neighbors and four replacement neighbors, while player of type B, whose factor is controlled by a parameter 1 - τ, possess eight interaction neighbors and four replacement neighbors. By means of numerous Monte Carlo simulations, we found that middle τ can make the cooperation reach the highest level While for this finding, its robustness can be further validated in more games.

Parasites and deleterious mutations: interactions influencing the evolutionary maintenance of sex.

PubMed

Park, A W; Jokela, J; Michalakis, Y

2010-05-01

The restrictive assumptions associated with purely genetic and purely ecological mechanisms suggest that neither of the two forces, in isolation, can offer a general explanation for the evolutionary maintenance of sex. Consequently, attention has turned to pluralistic models (i.e. models that apply both ecological and genetic mechanisms). Existing research has shown that combining mutation accumulation and parasitism allows restrictive assumptions about genetic and parasite parameter values to be relaxed while still predicting the maintenance of sex. However, several empirical studies have shown that deleterious mutations and parasitism can reduce fitness to a greater extent than would be expected if the two acted independently. We show how interactions between these genetic and ecological forces can completely reverse predictions about the evolution of reproductive modes. Moreover, we demonstrate that synergistic interactions between infection and deleterious mutations can render sex evolutionarily stable even when there is antagonistic epistasis among deleterious mutations, thereby widening the conditions for the evolutionary maintenance of sex.

Animal behaviour and algal camouflage jointly structure predation and selection.

PubMed

Start, Denon

2018-05-01

Trait variation can structure interactions between individuals, thus shaping selection. Although antipredator strategies are an important component of many aquatic systems, how multiple antipredator traits interact to influence consumption and selection remains contentious. Here, I use a common larval dragonfly (Epitheca canis) and its predator (Anax junius) to test for the joint effects of activity rate and algal camouflage on predation and survival selection. I found that active and poorly camouflaged Epitheca were more likely to be consumed, and thus, survival selection favoured inactive and well-camouflaged individuals. Notably, camouflage dampened selection on activity rate, likely by reducing attack rates when Epitheca encountered a predator. Correlational selection is therefore conferred by the ecological interaction of traits, rather than by opposing selection acting on linked traits. I suggest that antipredator traits with different adaptive functions can jointly structure patterns of consumption and selection. © 2018 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2018 European Society For Evolutionary Biology.

Fixation of competing strategies when interacting agents differ in the time scale of strategy updating

NASA Astrophysics Data System (ADS)

Zhang, Jianlei; Weissing, Franz J.; Cao, Ming

2016-09-01

A commonly used assumption in evolutionary game theory is that natural selection acts on individuals in the same time scale; e.g., players use the same frequency to update their strategies. Variation in learning rates within populations suggests that evolutionary game theory may not necessarily be restricted to uniform time scales associated with the game interaction and strategy adaption evolution. In this study, we remove this restricting assumption by dividing the population into fast and slow groups according to the players' strategy updating frequencies and investigate how different strategy compositions of one group influence the evolutionary outcome of the other's fixation probabilities of strategies within its own group. Analytical analysis and numerical calculations are performed to study the evolutionary dynamics of strategies in typical classes of two-player games (prisoner's dilemma game, snowdrift game, and stag-hunt game). The introduction of the heterogeneity in strategy-update time scales leads to substantial changes in the evolution dynamics of strategies. We provide an approximation formula for the fixation probability of mutant types in finite populations and study the outcome of strategy evolution under the weak selection. We find that although heterogeneity in time scales makes the collective evolutionary dynamics more complicated, the possible long-run evolutionary outcome can be effectively predicted under technical assumptions when knowing the population composition and payoff parameters.

Population genetics and demography unite ecology and evolution

USGS Publications Warehouse

Lowe, Winsor H.; Kovach, Ryan; Allendorf, Fred W.

2017-01-01

The interplay of ecology and evolution has been a rich area of research for decades. A surge of interest in this area was catalyzed by the observation that evolution by natural selection can operate at the same contemporary timescales as ecological dynamics. Specifically, recent eco-evolutionary research focuses on how rapid adaptation influences ecology, and vice versa. Evolution by non-adaptive forces also occurs quickly, with ecological consequences, but understanding the full scope of ecology–evolution (eco–evo) interactions requires explicitly addressing population-level processes – genetic and demographic. We show the strong ecological effects of non-adaptive evolutionary forces and, more broadly, the value of population-level research for gaining a mechanistic understanding of eco–evo interactions. The breadth of eco-evolutionary research should expand to incorporate the breadth of evolution itself.

Computational evolution: taking liberties.

PubMed

Correia, Luís

2010-09-01

Evolution has, for a long time, inspired computer scientists to produce computer models mimicking its behavior. Evolutionary algorithm (EA) is one of the areas where this approach has flourished. EAs have been used to model and study evolution, but they have been especially developed for their aptitude as optimization tools for engineering. Developed models are quite simple in comparison with their natural sources of inspiration. However, since EAs run on computers, we have the freedom, especially in optimization models, to test approaches both realistic and outright speculative, from the biological point of view. In this article, we discuss different common evolutionary algorithm models, and then present some alternatives of interest. These include biologically inspired models, such as co-evolution and, in particular, symbiogenetics and outright artificial operators and representations. In each case, the advantages of the modifications to the standard model are identified. The other area of computational evolution, which has allowed us to study basic principles of evolution and ecology dynamics, is the development of artificial life platforms for open-ended evolution of artificial organisms. With these platforms, biologists can test theories by directly manipulating individuals and operators, observing the resulting effects in a realistic way. An overview of the most prominent of such environments is also presented. If instead of artificial platforms we use the real world for evolving artificial life, then we are dealing with evolutionary robotics (ERs). A brief description of this area is presented, analyzing its relations to biology. Finally, we present the conclusions and identify future research avenues in the frontier of computation and biology. Hopefully, this will help to draw the attention of more biologists and computer scientists to the benefits of such interdisciplinary research.

Understanding sequence similarity and framework analysis between centromere proteins using computational biology.

PubMed

Doss, C George Priya; Chakrabarty, Chiranjib; Debajyoti, C; Debottam, S

2014-11-01

Certain mysteries pointing toward their recruitment pathways, cell cycle regulation mechanisms, spindle checkpoint assembly, and chromosome segregation process are considered the centre of attraction in cancer research. In modern times, with the established databases, ranges of computational platforms have provided a platform to examine almost all the physiological and biochemical evidences in disease-associated phenotypes. Using existing computational methods, we have utilized the amino acid residues to understand the similarity within the evolutionary variance of different associated centromere proteins. This study related to sequence similarity, protein-protein networking, co-expression analysis, and evolutionary trajectory of centromere proteins will speed up the understanding about centromere biology and will create a road map for upcoming researchers who are initiating their work of clinical sequencing using centromere proteins.

Social traits, social networks and evolutionary biology.

PubMed

Fisher, D N; McAdam, A G

2017-12-01

The social environment is both an important agent of selection for most organisms, and an emergent property of their interactions. As an aggregation of interactions among members of a population, the social environment is a product of many sets of relationships and so can be represented as a network or matrix. Social network analysis in animals has focused on why these networks possess the structure they do, and whether individuals' network traits, representing some aspect of their social phenotype, relate to their fitness. Meanwhile, quantitative geneticists have demonstrated that traits expressed in a social context can depend on the phenotypes and genotypes of interacting partners, leading to influences of the social environment on the traits and fitness of individuals and the evolutionary trajectories of populations. Therefore, both fields are investigating similar topics, yet have arrived at these points relatively independently. We review how these approaches are diverged, and yet how they retain clear parallelism and so strong potential for complementarity. This demonstrates that, despite separate bodies of theory, advances in one might inform the other. Techniques in network analysis for quantifying social phenotypes, and for identifying community structure, should be useful for those studying the relationship between individual behaviour and group-level phenotypes. Entering social association matrices into quantitative genetic models may also reduce bias in heritability estimates, and allow the estimation of the influence of social connectedness on trait expression. Current methods for measuring natural selection in a social context explicitly account for the fact that a trait is not necessarily the property of a single individual, something the network approaches have not yet considered when relating network metrics to individual fitness. Harnessing evolutionary models that consider traits affected by genes in other individuals (i.e. indirect genetic effects) provides the potential to understand how entire networks of social interactions in populations influence phenotypes and predict how these traits may evolve. By theoretical integration of social network analysis and quantitative genetics, we hope to identify areas of compatibility and incompatibility and to direct research efforts towards the most promising areas. Continuing this synthesis could provide important insights into the evolution of traits expressed in a social context and the evolutionary consequences of complex and nuanced social phenotypes. © 2017 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2017 European Society For Evolutionary Biology.

Multivariate Analysis of Conformational Changes Induced by Macromolecular Interactions

NASA Astrophysics Data System (ADS)

Mitra, Indranil; Alexov, Emil

2009-11-01

Understanding protein-protein binding and associated conformational changes is critical for both understanding thermodynamics of protein interactions and successful drug discovery. Our study focuses on computational analysis of plausible correlations between induced conformational changes and set of biophysical characteristics of interacting monomers. It was done by comparing 3D structures of unbound and bound monomers to calculate the RMSD which is used as measure of the structural changed induced by the binding. We correlate RMSD with volumetric and interfacial charge of the monomers, the amino acid composition, the energy of binding, and type of amino acids at the interface. as predictors. The data set was analyzed with SVM in R & SPSS which is trained on a combination of a new robust evolutionary conservation signal with the monomeric properties to predict the induced RMSD. The goal of this study is to undergo parametric tests and heirchiacal cluster and discriminant multivariate analysis to find key predictors which will be used to develop algorithm to predict the magnitude of conformational changes provided by the structure of interacting monomers. Results indicate that the most promising predictor is the net charge of the monomers, however, other parameters as the type of amino acids at the interface have significant contribution as well.

The Handicap Principle for Trust in Computer Security, the Semantic Web and Social Networking

NASA Astrophysics Data System (ADS)

Ma, Zhanshan (Sam); Krings, Axel W.; Hung, Chih-Cheng

Communication is a fundamental function of life, and it exists in almost all living things: from single-cell bacteria to human beings. Communication, together with competition and cooperation,arethree fundamental processes in nature. Computer scientists are familiar with the study of competition or 'struggle for life' through Darwin's evolutionary theory, or even evolutionary computing. They may be equally familiar with the study of cooperation or altruism through the Prisoner's Dilemma (PD) game. However, they are likely to be less familiar with the theory of animal communication. The objective of this article is three-fold: (i) To suggest that the study of animal communication, especially the honesty (reliability) of animal communication, in which some significant advances in behavioral biology have been achieved in the last three decades, should be on the verge to spawn important cross-disciplinary research similar to that generated by the study of cooperation with the PD game. One of the far-reaching advances in the field is marked by the publication of "The Handicap Principle: a Missing Piece of Darwin's Puzzle" by Zahavi (1997). The 'Handicap' principle [34][35], which states that communication signals must be costly in some proper way to be reliable (honest), is best elucidated with evolutionary games, e.g., Sir Philip Sidney (SPS) game [23]. Accordingly, we suggest that the Handicap principle may serve as a fundamental paradigm for trust research in computer science. (ii) To suggest to computer scientists that their expertise in modeling computer networks may help behavioral biologists in their study of the reliability of animal communication networks. This is largely due to the historical reason that, until the last decade, animal communication was studied with the dyadic paradigm (sender-receiver) rather than with the network paradigm. (iii) To pose several open questions, the answers to which may bear some refreshing insights to trust research in computer science, especially secure and resilient computing, the semantic web, and social networking. One important thread unifying the three aspects is the evolutionary game theory modeling or its extensions with survival analysis and agreement algorithms [19][20], which offer powerful game models for describing time-, space-, and covariate-dependent frailty (uncertainty and vulnerability) and deception (honesty).

The interactions of ants with their biotic environment.

PubMed

Chomicki, Guillaume; Renner, Susanne S

2017-03-15

This s pecial feature results from the symposium 'Ants 2016: ant interactions with their biotic environments' held in Munich in May 2016 and deals with the interactions between ants and other insects, plants, microbes and fungi, studied at micro- and macroevolutionary levels with a wide range of approaches, from field ecology to next-generation sequencing, chemical ecology and molecular genetics. In this paper, we review key aspects of these biotic interactions to provide background information for the papers of this s pecial feature After listing the major types of biotic interactions that ants engage in, we present a brief overview of ant/ant communication, ant/plant interactions, ant/fungus symbioses, and recent insights about ants and their endosymbionts. Using a large molecular clock-dated Formicidae phylogeny, we map the evolutionary origins of different ant clades' interactions with plants, fungi and hemiptera. Ants' biotic interactions provide ideal systems to address fundamental ecological and evolutionary questions about mutualism, coevolution, adaptation and animal communication. © 2017 The Author(s).

The interactions of ants with their biotic environment

PubMed Central

Renner, Susanne S.

2017-01-01

This special feature results from the symposium ‘Ants 2016: ant interactions with their biotic environments’ held in Munich in May 2016 and deals with the interactions between ants and other insects, plants, microbes and fungi, studied at micro- and macroevolutionary levels with a wide range of approaches, from field ecology to next-generation sequencing, chemical ecology and molecular genetics. In this paper, we review key aspects of these biotic interactions to provide background information for the papers of this special feature. After listing the major types of biotic interactions that ants engage in, we present a brief overview of ant/ant communication, ant/plant interactions, ant/fungus symbioses, and recent insights about ants and their endosymbionts. Using a large molecular clock-dated Formicidae phylogeny, we map the evolutionary origins of different ant clades' interactions with plants, fungi and hemiptera. Ants' biotic interactions provide ideal systems to address fundamental ecological and evolutionary questions about mutualism, coevolution, adaptation and animal communication. PMID:28298352

Bridging Developmental Systems Theory and Evolutionary Psychology Using Dynamic Optimization

ERIC Educational Resources Information Center

Frankenhuis, Willem E.; Panchanathan, Karthik; Clark Barrett, H.

2013-01-01

Interactions between evolutionary psychologists and developmental systems theorists have been largely antagonistic. This is unfortunate because potential synergies between the two approaches remain unexplored. This article presents a method that may help to bridge the divide, and that has proven fruitful in biology: dynamic optimization. Dynamic…

Epigenetic battle of the sexes. Comment on: ;Epigenetic game theory: How to compute the epigenetic control of maternal-to-zygotic transition; by Qian Wang et al.

NASA Astrophysics Data System (ADS)

Wu, Song

2017-03-01

Qian Wang et al. present an interesting framework, named epigenetic game theory, for modeling sex-based epigenetic dynamics during embryogenesis from a new viewpoint of evolutionary game theory [1]. That is, epigenomes of sperms and oocytes may coordinate through either cooperation or competition, or both, to affect the fitness of embryos. The work uses a set of ordinary differential equations (ODEs) to describe longitudinal trajectories of DNA methylation levels in both parental and maternal gametes and their dependence on each other. The insights gained from this review, i.e. dynamic methylation profiles and their interaction are potentially important to many fields, such as biomedicine and agriculture.

Neuroendocrine-Immune Circuits, Phenotypes, and Interactions

PubMed Central

Ashley, Noah T.; Demas, Gregory E.

2016-01-01

Multidirectional interactions among the immune, endocrine, and nervous systems have been demonstrated in humans and non-human animal models for many decades by the biomedical community, but ecological and evolutionary perspectives are lacking. Neuroendocrine-immune interactions can be conceptualized using a series of feedback loops, which culminate into distinct neuroendocrine-immune phenotypes. Behavior can exert profound influences on these phenotypes, which can in turn reciprocally modulate behavior. For example, the behavioral aspects of reproduction, including courtship, aggression, mate selection and parental behaviors can impinge upon neuroendocrine-immune interactions. One classic example is the immunocompetence handicap hypothesis (ICHH), which proposes that steroid hormones act as mediators of traits important for female choice while suppressing the immune system. Reciprocally, neuroendocrine-immune pathways can promote the development of altered behavioral states, such as sickness behavior. Understanding the energetic signals that mediate neuroendocrine-immune crosstalk is an active area of research. Although the field of psychoneuroimmunology (PNI) has begun to explore this crosstalk from a biomedical standpoint, the neuroendocrine-immune-behavior nexus has been relatively underappreciated in comparative species. The field of ecoimmunology, while traditionally emphasizing the study of non-model systems from an ecological evolutionary perspective, often under natural conditions, has focused less on the physiological mechanisms underlying behavioral responses. This review summarizes neuroendocrine-immune interactions using a comparative framework to understand the ecological and evolutionary forces that shape these complex physiological interactions. PMID:27765499

Neuroendocrine-immune circuits, phenotypes, and interactions.

PubMed

Ashley, Noah T; Demas, Gregory E

2017-01-01

Multidirectional interactions among the immune, endocrine, and nervous systems have been demonstrated in humans and non-human animal models for many decades by the biomedical community, but ecological and evolutionary perspectives are lacking. Neuroendocrine-immune interactions can be conceptualized using a series of feedback loops, which culminate into distinct neuroendocrine-immune phenotypes. Behavior can exert profound influences on these phenotypes, which can in turn reciprocally modulate behavior. For example, the behavioral aspects of reproduction, including courtship, aggression, mate selection and parental behaviors can impinge upon neuroendocrine-immune interactions. One classic example is the immunocompetence handicap hypothesis (ICHH), which proposes that steroid hormones act as mediators of traits important for female choice while suppressing the immune system. Reciprocally, neuroendocrine-immune pathways can promote the development of altered behavioral states, such as sickness behavior. Understanding the energetic signals that mediate neuroendocrine-immune crosstalk is an active area of research. Although the field of psychoneuroimmunology (PNI) has begun to explore this crosstalk from a biomedical standpoint, the neuroendocrine-immune-behavior nexus has been relatively underappreciated in comparative species. The field of ecoimmunology, while traditionally emphasizing the study of non-model systems from an ecological evolutionary perspective, often under natural conditions, has focused less on the physiological mechanisms underlying behavioral responses. This review summarizes neuroendocrine-immune interactions using a comparative framework to understand the ecological and evolutionary forces that shape these complex physiological interactions. Copyright © 2016 Elsevier Inc. All rights reserved.

Asymmetric ecological conditions favor Red-Queen type of continued evolution over stasis.

PubMed

Nordbotten, Jan Martin; Stenseth, Nils C

2016-02-16

Four decades ago, Leigh Van Valen presented the Red Queen's hypothesis to account for evolution of species within a multispecies ecological community [Van Valen L (1973) Evol Theory 1(1):1-30]. The overall conclusion of Van Valen's analysis was that evolution would continue even in the absence of abiotic perturbations. Stenseth and Maynard Smith presented in 1984 [Stenseth NC, Maynard Smith J (1984) Evolution 38(4):870-880] a model for the Red Queen's hypothesis showing that both Red-Queen type of continuous evolution and stasis could result from a model with biotically driven evolution. However, although that contribution demonstrated that both evolutionary outcomes were possible, it did not identify which ecological conditions would lead to each of these evolutionary outcomes. Here, we provide, using a simple, yet general population-biologically founded eco-evolutionary model, such analytically derived conditions: Stasis will predominantly emerge whenever the ecological system contains only symmetric ecological interactions, whereas both Red-Queen and stasis type of evolution may result if the ecological interactions are asymmetrical, and more likely so with increasing degree of asymmetry in the ecological system (i.e., the more trophic interactions, host-pathogen interactions, and the like there are [i.e., +/- type of ecological interactions as well as asymmetric competitive (-/-) and mutualistic (+/+) ecological interactions]). In the special case of no between-generational genetic variance, our results also predict dynamics within these types of purely ecological systems.

Computer-Automated Evolution of Spacecraft X-Band Antennas

NASA Technical Reports Server (NTRS)

Lohn, Jason D.; Homby, Gregory S.; Linden, Derek S.

2010-01-01

A document discusses the use of computer- aided evolution in arriving at a design for X-band communication antennas for NASA s three Space Technology 5 (ST5) satellites, which were launched on March 22, 2006. Two evolutionary algorithms, incorporating different representations of the antenna design and different fitness functions, were used to automatically design and optimize an X-band antenna design. A set of antenna designs satisfying initial ST5 mission requirements was evolved by use these algorithms. The two best antennas - one from each evolutionary algorithm - were built. During flight-qualification testing of these antennas, the mission requirements were changed. After minimal changes in the evolutionary algorithms - mostly in the fitness functions - new antenna designs satisfying the changed mission requirements were evolved and within one month of this change, two new antennas were designed and prototypes of the antennas were built and tested. One of these newly evolved antennas was approved for deployment on the ST5 mission, and flight-qualified versions of this design were built and installed on the spacecraft. At the time of writing the document, these antennas were the first computer-evolved hardware in outer space.

Optimizing a reconfigurable material via evolutionary computation

NASA Astrophysics Data System (ADS)

Wilken, Sam; Miskin, Marc Z.; Jaeger, Heinrich M.

2015-08-01

Rapid prototyping by combining evolutionary computation with simulations is becoming a powerful tool for solving complex design problems in materials science. This method of optimization operates in a virtual design space that simulates potential material behaviors and after completion needs to be validated by experiment. However, in principle an evolutionary optimizer can also operate on an actual physical structure or laboratory experiment directly, provided the relevant material parameters can be accessed by the optimizer and information about the material's performance can be updated by direct measurements. Here we provide a proof of concept of such direct, physical optimization by showing how a reconfigurable, highly nonlinear material can be tuned to respond to impact. We report on an entirely computer controlled laboratory experiment in which a 6 ×6 grid of electromagnets creates a magnetic field pattern that tunes the local rigidity of a concentrated suspension of ferrofluid and iron filings. A genetic algorithm is implemented and tasked to find field patterns that minimize the force transmitted through the suspension. Searching within a space of roughly 1010 possible configurations, after testing only 1500 independent trials the algorithm identifies an optimized configuration of layered rigid and compliant regions.

Coiled-Coil Proteins Facilitated the Functional Expansion of the Centrosome

PubMed Central

Kuhn, Michael; Hyman, Anthony A.; Beyer, Andreas

2014-01-01

Repurposing existing proteins for new cellular functions is recognized as a main mechanism of evolutionary innovation, but its role in organelle evolution is unclear. Here, we explore the mechanisms that led to the evolution of the centrosome, an ancestral eukaryotic organelle that expanded its functional repertoire through the course of evolution. We developed a refined sequence alignment technique that is more sensitive to coiled coil proteins, which are abundant in the centrosome. For proteins with high coiled-coil content, our algorithm identified 17% more reciprocal best hits than BLAST. Analyzing 108 eukaryotic genomes, we traced the evolutionary history of centrosome proteins. In order to assess how these proteins formed the centrosome and adopted new functions, we computationally emulated evolution by iteratively removing the most recently evolved proteins from the centrosomal protein interaction network. Coiled-coil proteins that first appeared in the animal–fungi ancestor act as scaffolds and recruit ancestral eukaryotic proteins such as kinases and phosphatases to the centrosome. This process created a signaling hub that is crucial for multicellular development. Our results demonstrate how ancient proteins can be co-opted to different cellular localizations, thereby becoming involved in novel functions. PMID:24901223

Computational modeling of Repeat1 region of INI1/hSNF5: An evolutionary link with ubiquitin

PubMed Central

Bhutoria, Savita

2016-01-01

Abstract The structure of a protein can be very informative of its function. However, determining protein structures experimentally can often be very challenging. Computational methods have been used successfully in modeling structures with sufficient accuracy. Here we have used computational tools to predict the structure of an evolutionarily conserved and functionally significant domain of Integrase interactor (INI)1/hSNF5 protein. INI1 is a component of the chromatin remodeling SWI/SNF complex, a tumor suppressor and is involved in many protein‐protein interactions. It belongs to SNF5 family of proteins that contain two conserved repeat (Rpt) domains. Rpt1 domain of INI1 binds to HIV‐1 Integrase, and acts as a dominant negative mutant to inhibit viral replication. Rpt1 domain also interacts with oncogene c‐MYC and modulates its transcriptional activity. We carried out an ab initio modeling of a segment of INI1 protein containing the Rpt1 domain. The structural model suggested the presence of a compact and well defined ββαα topology as core structure in the Rpt1 domain of INI1. This topology in Rpt1 was similar to PFU domain of Phospholipase A2 Activating Protein, PLAA. Interestingly, PFU domain shares similarity with Ubiquitin and has ubiquitin binding activity. Because of the structural similarity between Rpt1 domain of INI1 and PFU domain of PLAA, we propose that Rpt1 domain of INI1 may participate in ubiquitin recognition or binding with ubiquitin or ubiquitin related proteins. This modeling study may shed light on the mode of interactions of Rpt1 domain of INI1 and is likely to facilitate future functional studies of INI1. PMID:27261671

Analyzing machupo virus-receptor binding by molecular dynamics simulations.

PubMed

Meyer, Austin G; Sawyer, Sara L; Ellington, Andrew D; Wilke, Claus O

2014-01-01

In many biological applications, we would like to be able to computationally predict mutational effects on affinity in protein-protein interactions. However, many commonly used methods to predict these effects perform poorly in important test cases. In particular, the effects of multiple mutations, non alanine substitutions, and flexible loops are difficult to predict with available tools and protocols. We present here an existing method applied in a novel way to a new test case; we interrogate affinity differences resulting from mutations in a host-virus protein-protein interface. We use steered molecular dynamics (SMD) to computationally pull the machupo virus (MACV) spike glycoprotein (GP1) away from the human transferrin receptor (hTfR1). We then approximate affinity using the maximum applied force of separation and the area under the force-versus-distance curve. We find, even without the rigor and planning required for free energy calculations, that these quantities can provide novel biophysical insight into the GP1/hTfR1 interaction. First, with no prior knowledge of the system we can differentiate among wild type and mutant complexes. Moreover, we show that this simple SMD scheme correlates well with relative free energy differences computed via free energy perturbation. Second, although the static co-crystal structure shows two large hydrogen-bonding networks in the GP1/hTfR1 interface, our simulations indicate that one of them may not be important for tight binding. Third, one viral site known to be critical for infection may mark an important evolutionary suppressor site for infection-resistant hTfR1 mutants. Finally, our approach provides a framework to compare the effects of multiple mutations, individually and jointly, on protein-protein interactions.

Analyzing machupo virus-receptor binding by molecular dynamics simulations

PubMed Central

Sawyer, Sara L.; Ellington, Andrew D.; Wilke, Claus O.

2014-01-01

In many biological applications, we would like to be able to computationally predict mutational effects on affinity in protein–protein interactions. However, many commonly used methods to predict these effects perform poorly in important test cases. In particular, the effects of multiple mutations, non alanine substitutions, and flexible loops are difficult to predict with available tools and protocols. We present here an existing method applied in a novel way to a new test case; we interrogate affinity differences resulting from mutations in a host–virus protein–protein interface. We use steered molecular dynamics (SMD) to computationally pull the machupo virus (MACV) spike glycoprotein (GP1) away from the human transferrin receptor (hTfR1). We then approximate affinity using the maximum applied force of separation and the area under the force-versus-distance curve. We find, even without the rigor and planning required for free energy calculations, that these quantities can provide novel biophysical insight into the GP1/hTfR1 interaction. First, with no prior knowledge of the system we can differentiate among wild type and mutant complexes. Moreover, we show that this simple SMD scheme correlates well with relative free energy differences computed via free energy perturbation. Second, although the static co-crystal structure shows two large hydrogen-bonding networks in the GP1/hTfR1 interface, our simulations indicate that one of them may not be important for tight binding. Third, one viral site known to be critical for infection may mark an important evolutionary suppressor site for infection-resistant hTfR1 mutants. Finally, our approach provides a framework to compare the effects of multiple mutations, individually and jointly, on protein–protein interactions. PMID:24624315

Learning Natural Selection in 4th Grade with Multi-Agent-Based Computational Models

NASA Astrophysics Data System (ADS)

Dickes, Amanda Catherine; Sengupta, Pratim

2013-06-01

In this paper, we investigate how elementary school students develop multi-level explanations of population dynamics in a simple predator-prey ecosystem, through scaffolded interactions with a multi-agent-based computational model (MABM). The term "agent" in an MABM indicates individual computational objects or actors (e.g., cars), and these agents obey simple rules assigned or manipulated by the user (e.g., speeding up, slowing down, etc.). It is the interactions between these agents, based on the rules assigned by the user, that give rise to emergent, aggregate-level behavior (e.g., formation and movement of the traffic jam). Natural selection is such an emergent phenomenon, which has been shown to be challenging for novices (K16 students) to understand. Whereas prior research on learning evolutionary phenomena with MABMs has typically focused on high school students and beyond, we investigate how elementary students (4th graders) develop multi-level explanations of some introductory aspects of natural selection—species differentiation and population change—through scaffolded interactions with an MABM that simulates predator-prey dynamics in a simple birds-butterflies ecosystem. We conducted a semi-clinical interview based study with ten participants, in which we focused on the following: a) identifying the nature of learners' initial interpretations of salient events or elements of the represented phenomena, b) identifying the roles these interpretations play in the development of their multi-level explanations, and c) how attending to different levels of the relevant phenomena can make explicit different mechanisms to the learners. In addition, our analysis also shows that although there were differences between high- and low-performing students (in terms of being able to explain population-level behaviors) in the pre-test, these differences disappeared in the post-test.

Computational modeling of Repeat1 region of INI1/hSNF5: An evolutionary link with ubiquitin.

PubMed

Bhutoria, Savita; Kalpana, Ganjam V; Acharya, Seetharama A

2016-09-01

The structure of a protein can be very informative of its function. However, determining protein structures experimentally can often be very challenging. Computational methods have been used successfully in modeling structures with sufficient accuracy. Here we have used computational tools to predict the structure of an evolutionarily conserved and functionally significant domain of Integrase interactor (INI)1/hSNF5 protein. INI1 is a component of the chromatin remodeling SWI/SNF complex, a tumor suppressor and is involved in many protein-protein interactions. It belongs to SNF5 family of proteins that contain two conserved repeat (Rpt) domains. Rpt1 domain of INI1 binds to HIV-1 Integrase, and acts as a dominant negative mutant to inhibit viral replication. Rpt1 domain also interacts with oncogene c-MYC and modulates its transcriptional activity. We carried out an ab initio modeling of a segment of INI1 protein containing the Rpt1 domain. The structural model suggested the presence of a compact and well defined ββαα topology as core structure in the Rpt1 domain of INI1. This topology in Rpt1 was similar to PFU domain of Phospholipase A2 Activating Protein, PLAA. Interestingly, PFU domain shares similarity with Ubiquitin and has ubiquitin binding activity. Because of the structural similarity between Rpt1 domain of INI1 and PFU domain of PLAA, we propose that Rpt1 domain of INI1 may participate in ubiquitin recognition or binding with ubiquitin or ubiquitin related proteins. This modeling study may shed light on the mode of interactions of Rpt1 domain of INI1 and is likely to facilitate future functional studies of INI1. © 2016 The Protein Society.

Simulated Breeding

NASA Astrophysics Data System (ADS)

Unemi, Tatsuo

This chapter describes a basic framework of simulated breeding, a type of interactive evolutionary computing to breed artifacts, whose origin is Blind Watchmaker by Dawkins. These methods make it easy for humans to design a complex object adapted to his/her subjective criteria, just similarly to agricultural products we have been developing over thousands of years. Starting from randomly initialized genome, the solution candidates are improved through several generations with artificial selection. The graphical user interface helps the process of breeding with techniques of multifield user interface and partial breeding. The former improves the diversity of individuals that prevents being trapped at local optimum. The latter makes it possible for the user to fix features he/she already satisfied. These methods were examined through artistic applications by the author: SBART for graphics art and SBEAT for music. Combining with a direct genome editor and exportation to another graphical or musical tool on the computer, they can be powerful tools for artistic creation. These systems may contribute to the creation of a type of new culture.

Evaluation of Generation Alternation Models in Evolutionary Robotics

NASA Astrophysics Data System (ADS)

Oiso, Masashi; Matsumura, Yoshiyuki; Yasuda, Toshiyuki; Ohkura, Kazuhiro

For efficient implementation of Evolutionary Algorithms (EA) to a desktop grid computing environment, we propose a new generation alternation model called Grid-Oriented-Deletion (GOD) based on comparison with the conventional techniques. In previous research, generation alternation models are generally evaluated by using test functions. However, their exploration performance on the real problems such as Evolutionary Robotics (ER) has not been made very clear yet. Therefore we investigate the relationship between the exploration performance of EA on an ER problem and its generation alternation model. We applied four generation alternation models to the Evolutionary Multi-Robotics (EMR), which is the package-pushing problem to investigate their exploration performance. The results show that GOD is more effective than the other conventional models.

Scheduling Earth Observing Fleets Using Evolutionary Algorithms: Problem Description and Approach

NASA Technical Reports Server (NTRS)

Globus, Al; Crawford, James; Lohn, Jason; Morris, Robert; Clancy, Daniel (Technical Monitor)

2002-01-01

We describe work in progress concerning multi-instrument, multi-satellite scheduling. Most, although not all, Earth observing instruments currently in orbit are unique. In the relatively near future, however, we expect to see fleets of Earth observing spacecraft, many carrying nearly identical instruments. This presents a substantially new scheduling challenge. Inspired by successful commercial applications of evolutionary algorithms in scheduling domains, this paper presents work in progress regarding the use of evolutionary algorithms to solve a set of Earth observing related model problems. Both the model problems and the software are described. Since the larger problems will require substantial computation and evolutionary algorithms are embarrassingly parallel, we discuss our parallelization techniques using dedicated and cycle-scavenged workstations.

Nanotube Heterojunctions and Endo-Fullerenes for Nanoelectronics

NASA Technical Reports Server (NTRS)

Srivastava, Deepak; Menon, M.; Andriotis, Antonis; Cho, K.; Park, Jun; Biegel, Bryan A. (Technical Monitor)

2002-01-01

Topics discussed include: (1) Light-Weight Multi-Functional Materials: Nanomechanics; Nanotubes and Composites; Thermal/Chemical/Electrical Characterization; (2) Biomimetic/Revolutionary Concepts: Evolutionary Computing and Sensing; Self-Heating Materials; (3) Central Computing System: Molecular Electronics; Materials for Quantum Bits; and (4) Molecular Machines.

An overview of ecological and evolutionary research on disease in natural systems: an annotated reference list

Treesearch

Helen M. Alexander

2012-01-01

The Fourth International Workshop on the Genetics of Host-Parasite Interactions in Forestry (July 31-August 5, 2011) included a session on “Ecology and Evolutionary Biology of Resistance and Tolerance, Natural Systems.” Within this session, I gave a talk entitled “An overview of ecological and evolutionary research on disease in ‘natural’ systems” that reviewed...

Hidden long evolutionary memory in a model biochemical network

NASA Astrophysics Data System (ADS)

Ali, Md. Zulfikar; Wingreen, Ned S.; Mukhopadhyay, Ranjan

2018-04-01

We introduce a minimal model for the evolution of functional protein-interaction networks using a sequence-based mutational algorithm, and apply the model to study neutral drift in networks that yield oscillatory dynamics. Starting with a functional core module, random evolutionary drift increases network complexity even in the absence of specific selective pressures. Surprisingly, we uncover a hidden order in sequence space that gives rise to long-term evolutionary memory, implying strong constraints on network evolution due to the topology of accessible sequence space.

Sequential interactions-in which one player plays first and another responds-promote cooperation in evolutionary-dynamical simulations of single-shot Prisoner's Dilemma and Snowdrift games.

PubMed

Laird, Robert A

2018-09-07

Cooperation is a central topic in evolutionary biology because (a) it is difficult to reconcile why individuals would act in a way that benefits others if such action is costly to themselves, and (b) it underpins many of the 'major transitions of evolution', making it essential for explaining the origins of successively higher levels of biological organization. Within evolutionary game theory, the Prisoner's Dilemma and Snowdrift games are the main theoretical constructs used to study the evolution of cooperation in dyadic interactions. In single-shot versions of these games, wherein individuals play each other only once, players typically act simultaneously rather than sequentially. Allowing one player to respond to the actions of its co-player-in the absence of any possibility of the responder being rewarded for cooperation or punished for defection, as in simultaneous or sequential iterated games-may seem to invite more incentive for exploitation and retaliation in single-shot games, compared to when interactions occur simultaneously, thereby reducing the likelihood that cooperative strategies can thrive. To the contrary, I use lattice-based, evolutionary-dynamical simulation models of single-shot games to demonstrate that under many conditions, sequential interactions have the potential to enhance unilaterally or mutually cooperative outcomes and increase the average payoff of populations, relative to simultaneous interactions-benefits that are especially prevalent in a spatially explicit context. This surprising result is attributable to the presence of conditional strategies that emerge in sequential games that can't occur in the corresponding simultaneous versions. Copyright © 2018 Elsevier Ltd. All rights reserved.

Historical contingency and ecological determinism interact to prime speciation in sticklebacks, Gasterosteus.

PubMed Central

Taylor, E B; McPhail, J D

2000-01-01

Historical contingency and determinism are often cast as opposing paradigms under which evolutionary diversification operates. It may be, however, that both factors act together to promote evolutionary divergence, although there are few examples of such interaction in nature. We tested phylogenetic predictions of an explicit historical model of divergence (double invasions of freshwater by marine ancestors) in sympatric species of three-spined sticklebacks (Gasterosteus aculeatus) where determinism has been implicated as an important factor driving evolutionary novelty. Microsatellite DNA variation at six loci revealed relatively low genetic variation in freshwater populations, supporting the hypothesis that they were derived by colonization of freshwater by more diverse marine ancestors. Phylogenetic and genetic distance analyses suggested that pairs of sympatric species have evolved multiple times, further implicating determinism as a factor in speciation. Our data also supported predictions based on the hypothesis that the evolution of sympatric species was contingent upon 'double invasions' of postglacial lakes by ancestral marine sticklebacks. Sympatric sticklebacks, therefore, provide an example of adaptive radiation by determinism contingent upon historical conditions promoting unique ecological interactions, and illustrate how contingency and determinism may interact to generate geographical variation in species diversity PMID:11133026

Experimental evolution of protein–protein interaction networks

PubMed Central

Kaçar, Betül; Gaucher, Eric A.

2013-01-01

The modern synthesis of evolutionary theory and genetics has enabled us to discover underlying molecular mechanisms of organismal evolution. We know that in order to maximize an organism's fitness in a particular environment, individual interactions among components of protein and nucleic acid networks need to be optimized by natural selection, or sometimes through random processes, as the organism responds to changes and/or challenges in the environment. Despite the significant role of molecular networks in determining an organism's adaptation to its environment, we still do not know how such inter- and intra-molecular interactions within networks change over time and contribute to an organism's evolvability while maintaining overall network functions. One way to address this challenge is to identify connections between molecular networks and their host organisms, to manipulate these connections, and then attempt to understand how such perturbations influence molecular dynamics of the network and thus influence evolutionary paths and organismal fitness. In the present review, we discuss how integrating evolutionary history with experimental systems that combine tools drawn from molecular evolution, synthetic biology and biochemistry allow us to identify the underlying mechanisms of organismal evolution, particularly from the perspective of protein interaction networks. PMID:23849056

Adaptive evolution of body size subject to indirect effect in trophic cascade system.

PubMed

Wang, Xin; Fan, Meng; Hao, Lina

2017-09-01

Trophic cascades represent a classic example of indirect effect and are wide-spread in nature. Their ecological impact are well established, but the evolutionary consequences have received even less theoretical attention. We theoretically and numerically investigate the trait (i.e., body size of consumer) evolution in response to indirect effect in a trophic cascade system. By applying the quantitative trait evolutionary theory and the adaptive dynamic theory, we formulate and explore two different types of eco-evolutionary resource-consumer-predator trophic cascade model. First, an eco-evolutionary model incorporating the rapid evolution is formulated to investigate the effect of rapid evolution of the consumer's body size, and to explore the impact of density-mediate indirect effect on the population dynamics and trait dynamics. Next, by employing the adaptive dynamic theory, a long-term evolutionary model of consumer body size is formulated to evaluate the effect of long-term evolution on the population dynamics and the effect of trait-mediate indirect effect. Those models admit rich dynamics that has not been observed yet in empirical studies. It is found that, both in the trait-mediated and density-mediated system, the body size of consumer in predator-consumer-resource interaction (indirect effect) evolves smaller than that in consumer-resource and predator-consumer interaction (direct effect). Moreover, in the density-mediated system, we found that the evolution of consumer body size contributes to avoiding consumer extinction (i.e., evolutionary rescue). The trait-mediate and density-mediate effects may produce opposite evolutionary response. This study suggests that the trophic cascade indirect effect affects consumer evolution, highlights a more comprehensive mechanistic understanding of the intricate interplay between ecological and evolutionary force. The modeling approaches provide avenue for study on indirect effects from an evolutionary perspective. Copyright © 2017 Elsevier B.V. All rights reserved.

Understanding how animal groups achieve coordinated movement

PubMed Central

Herbert-Read, J. E.

2016-01-01

ABSTRACT Moving animal groups display remarkable feats of coordination. This coordination is largely achieved when individuals adjust their movement in response to their neighbours' movements and positions. Recent advancements in automated tracking technologies, including computer vision and GPS, now allow researchers to gather large amounts of data on the movements and positions of individuals in groups. Furthermore, analytical techniques from fields such as statistical physics now allow us to identify the precise interaction rules used by animals on the move. These interaction rules differ not only between species, but also between individuals in the same group. These differences have wide-ranging implications, affecting how groups make collective decisions and driving the evolution of collective motion. Here, I describe how trajectory data can be used to infer how animals interact in moving groups. I give examples of the similarities and differences in the spatial and directional organisations of animal groups between species, and discuss the rules that animals use to achieve this organisation. I then explore how groups of the same species can exhibit different structures, and ask whether this results from individuals adapting their interaction rules. I then examine how the interaction rules between individuals in the same groups can also differ, and discuss how this can affect ecological and evolutionary processes. Finally, I suggest areas of future research. PMID:27707862

Evolutionary games with coordination and self-dependent interactions

NASA Astrophysics Data System (ADS)

Király, Balázs; Szabó, György

2017-01-01

Multistrategy evolutionary games are studied on a square lattice when the pair interactions are composed of coordinations between strategy pairs and an additional term with self-dependent payoff. We describe a method for determining the strength of each elementary coordination component in n -strategy potential games. Using analytical and numerical methods, the presence and absence of Ising-type order-disorder phase transitions are studied when a single pair coordination is extended by some types of self-dependent elementary games. We also introduce noise-dependent three-strategy equivalents of the n -strategy elementary coordination games.

Evolutionary games combining two or three pair coordinations on a square lattice

NASA Astrophysics Data System (ADS)

Király, Balázs; Szabó, György

2017-10-01

We study multiagent logit-rule-driven evolutionary games on a square lattice whose pair interactions are composed of a maximal number of nonoverlapping elementary coordination games describing Ising-type interactions between just two of the available strategies. Using Monte Carlo simulations we investigate the macroscopic noise-level-dependent behavior of the two- and three-pair games and the critical properties of the continuous phase transtitions these systems exhibit. The four-strategy game is shown to be equivalent to a system that consists of two independent and identical Ising models.

Evolutionary games combining two or three pair coordinations on a square lattice.

PubMed

Király, Balázs; Szabó, György

2017-10-01

We study multiagent logit-rule-driven evolutionary games on a square lattice whose pair interactions are composed of a maximal number of nonoverlapping elementary coordination games describing Ising-type interactions between just two of the available strategies. Using Monte Carlo simulations we investigate the macroscopic noise-level-dependent behavior of the two- and three-pair games and the critical properties of the continuous phase transtitions these systems exhibit. The four-strategy game is shown to be equivalent to a system that consists of two independent and identical Ising models.

Ready…, Set, Go!. Comment on "Towards a Computational Comparative Neuroprimatology: Framing the language-ready brain" by Michael A. Arbib

NASA Astrophysics Data System (ADS)

Iriki, Atsushi

2016-03-01

;Language-READY brain; in the title of this article [1] seems to be the expression that the author prefers to use to illustrate his theoretical framework. The usage of the term ;READY; appears to be of extremely deep connotation, for three reasons. Firstly, of course it needs a ;principle; - the depth and the width of the computational theory depicted here is as expected from the author's reputation. However, ;readiness; implies that it is much more than just ;a theory;. That is, such a principle is not static, but it rather has dynamic properties, which are ready to gradually proceed to flourish once brains are put in adequate conditions to make time progressions - namely, evolution and development. So the second major connotation is that this article brought in the perspectives of the comparative primatology as a tool to relativise the language-realizing human brains among other animal species, primates in particular, in the context of evolutionary time scale. The tertiary connotation lies in the context of the developmental time scale. The author claims that it is the interaction of the newborn with its care takers, namely its mother and other family or social members in its ecological conditions, that brings the brain mechanism subserving language faculty to really mature to its final completion. Taken together, this article proposes computational theories and mechanisms of Evo-Devo-Eco interactions for language acquisition in the human brains.

Computer Modeling of Protocellular Functions: Peptide Insertion in Membranes

NASA Technical Reports Server (NTRS)

Rodriquez-Gomez, D.; Darve, E.; Pohorille, A.

2006-01-01

Lipid vesicles became the precursors to protocells by acquiring the capabilities needed to survive and reproduce. These include transport of ions, nutrients and waste products across cell walls and capture of energy and its conversion into a chemically usable form. In modem organisms these functions are carried out by membrane-bound proteins (about 30% of the genome codes for this kind of proteins). A number of properties of alpha-helical peptides suggest that their associations are excellent candidates for protobiological precursors of proteins. In particular, some simple a-helical peptides can aggregate spontaneously and form functional channels. This process can be described conceptually by a three-step thermodynamic cycle: 1 - folding of helices at the water-membrane interface, 2 - helix insertion into the lipid bilayer and 3 - specific interactions of these helices that result in functional tertiary structures. Although a crucial step, helix insertion has not been adequately studied because of the insolubility and aggregation of hydrophobic peptides. In this work, we use computer simulation methods (Molecular Dynamics) to characterize the energetics of helix insertion and we discuss its importance in an evolutionary context. Specifically, helices could self-assemble only if their interactions were sufficiently strong to compensate the unfavorable Free Energy of insertion of individual helices into membranes, providing a selection mechanism for protobiological evolution.

Feature Selection for Speech Emotion Recognition in Spanish and Basque: On the Use of Machine Learning to Improve Human-Computer Interaction

PubMed Central

Arruti, Andoni; Cearreta, Idoia; Álvarez, Aitor; Lazkano, Elena; Sierra, Basilio

2014-01-01

Study of emotions in human–computer interaction is a growing research area. This paper shows an attempt to select the most significant features for emotion recognition in spoken Basque and Spanish Languages using different methods for feature selection. RekEmozio database was used as the experimental data set. Several Machine Learning paradigms were used for the emotion classification task. Experiments were executed in three phases, using different sets of features as classification variables in each phase. Moreover, feature subset selection was applied at each phase in order to seek for the most relevant feature subset. The three phases approach was selected to check the validity of the proposed approach. Achieved results show that an instance-based learning algorithm using feature subset selection techniques based on evolutionary algorithms is the best Machine Learning paradigm in automatic emotion recognition, with all different feature sets, obtaining a mean of 80,05% emotion recognition rate in Basque and a 74,82% in Spanish. In order to check the goodness of the proposed process, a greedy searching approach (FSS-Forward) has been applied and a comparison between them is provided. Based on achieved results, a set of most relevant non-speaker dependent features is proposed for both languages and new perspectives are suggested. PMID:25279686

The role of inflammation in depression: from evolutionary imperative to modern treatment target.

PubMed

Miller, Andrew H; Raison, Charles L

2016-01-01

Crosstalk between inflammatory pathways and neurocircuits in the brain can lead to behavioural responses, such as avoidance and alarm, that are likely to have provided early humans with an evolutionary advantage in their interactions with pathogens and predators. However, in modern times, such interactions between inflammation and the brain appear to drive the development of depression and may contribute to non-responsiveness to current antidepressant therapies. Recent data have elucidated the mechanisms by which the innate and adaptive immune systems interact with neurotransmitters and neurocircuits to influence the risk for depression. Here, we detail our current understanding of these pathways and discuss the therapeutic potential of targeting the immune system to treat depression.

Constructing phylogenetic trees using interacting pathways.

PubMed

Wan, Peng; Che, Dongsheng

2013-01-01

Phylogenetic trees are used to represent evolutionary relationships among biological species or organisms. The construction of phylogenetic trees is based on the similarities or differences of their physical or genetic features. Traditional approaches of constructing phylogenetic trees mainly focus on physical features. The recent advancement of high-throughput technologies has led to accumulation of huge amounts of biological data, which in turn changed the way of biological studies in various aspects. In this paper, we report our approach of building phylogenetic trees using the information of interacting pathways. We have applied hierarchical clustering on two domains of organisms-eukaryotes and prokaryotes. Our preliminary results have shown the effectiveness of using the interacting pathways in revealing evolutionary relationships.

Stabilization of Large Generalized Lotka-Volterra Foodwebs By Evolutionary Feedback

NASA Astrophysics Data System (ADS)

Ackland, G. J.; Gallagher, I. D.

2004-10-01

Conventional ecological models show that complexity destabilizes foodwebs, suggesting that foodwebs should have neither large numbers of species nor a large number of interactions. However, in nature the opposite appears to be the case. Here we show that if the interactions between species are allowed to evolve within a generalized Lotka-Volterra model such stabilizing feedbacks and weak interactions emerge automatically. Moreover, we show that trophic levels also emerge spontaneously from the evolutionary approach, and the efficiency of the unperturbed ecosystem increases with time. The key to stability in large foodwebs appears to arise not from complexity perse but from evolution at the level of the ecosystem which favors stabilizing (negative) feedbacks.

Stabilization of large generalized Lotka-Volterra foodwebs by evolutionary feedback.

PubMed

Ackland, G J; Gallagher, I D

2004-10-08

Conventional ecological models show that complexity destabilizes foodwebs, suggesting that foodwebs should have neither large numbers of species nor a large number of interactions. However, in nature the opposite appears to be the case. Here we show that if the interactions between species are allowed to evolve within a generalized Lotka-Volterra model such stabilizing feedbacks and weak interactions emerge automatically. Moreover, we show that trophic levels also emerge spontaneously from the evolutionary approach, and the efficiency of the unperturbed ecosystem increases with time. The key to stability in large foodwebs appears to arise not from complexity per se but from evolution at the level of the ecosystem which favors stabilizing (negative) feedbacks.

Evolution: Language Use and the Evolution of Languages

NASA Astrophysics Data System (ADS)

Croft, William

Language change can be understood as an evolutionary process. Language change occurs at two different timescales, corresponding to the two steps of the evolutionary process. The first timescale is very short, namely, the production of an utterance: this is where linguistic structures are replicated and language variation is generated. The second timescale is (or can be) very long, namely, the propagation of linguistic variants in the speech community: this is where certain variants are selected over others. At both timescales, the evolutionary process is driven by social interaction and the role language plays in it. An understanding of social interaction at the micro-level—face-to-face interactions—and at the macro-level—the structure of speech communities—gives us the basis for understanding the generation and propagation of language structures, and understanding the nature of language itself.

Genes, communities & invasive species: understanding the ecological and evolutionary dynamics of host-pathogen interactions.

PubMed

Burdon, J J; Thrall, P H; Ericson, L

2013-08-01

Reciprocal interactions between hosts and pathogens drive ecological, epidemiological and co-evolutionary trajectories, resulting in complex patterns of diversity at population, species and community levels. Recent results confirm the importance of negative frequency-dependent rather than 'arms-race' processes in the evolution of individual host-pathogen associations. At the community level, complex relationships between species abundance and diversity dampen or alter pathogen impacts. Invasive pathogens challenge these controls reflecting the earliest stages of evolutionary associations (akin to arms-race) where disease effects may be so great that they overwhelm the host's and community's ability to respond. Viewing these different stabilization/destabilization phases as a continuum provides a valuable perspective to assessment of the role of genetics and ecology in the dynamics of both natural and invasive host-pathogen associations. Copyright © 2013 Elsevier Ltd. All rights reserved.

Population Genetics and Demography Unite Ecology and Evolution.

PubMed

Lowe, Winsor H; Kovach, Ryan P; Allendorf, Fred W

2017-02-01

The interplay of ecology and evolution has been a rich area of research for decades. A surge of interest in this area was catalyzed by the observation that evolution by natural selection can operate at the same contemporary timescales as ecological dynamics. Specifically, recent eco-evolutionary research focuses on how rapid adaptation influences ecology, and vice versa. Evolution by non-adaptive forces also occurs quickly, with ecological consequences, but understanding the full scope of ecology-evolution (eco-evo) interactions requires explicitly addressing population-level processes - genetic and demographic. We show the strong ecological effects of non-adaptive evolutionary forces and, more broadly, the value of population-level research for gaining a mechanistic understanding of eco-evo interactions. The breadth of eco-evolutionary research should expand to incorporate the breadth of evolution itself. Copyright © 2016 Elsevier Ltd. All rights reserved.

The phylogenetic structure of plant-pollinator networks increases with habitat size and isolation.

PubMed

Aizen, Marcelo A; Gleiser, Gabriela; Sabatino, Malena; Gilarranz, Luis J; Bascompte, Jordi; Verdú, Miguel

2016-01-01

Similarity among species in traits related to ecological interactions is frequently associated with common ancestry. Thus, closely related species usually interact with ecologically similar partners, which can be reinforced by diverse co-evolutionary processes. The effect of habitat fragmentation on the phylogenetic signal in interspecific interactions and correspondence between plant and animal phylogenies is, however, unknown. Here, we address to what extent phylogenetic signal and co-phylogenetic congruence of plant-animal interactions depend on habitat size and isolation by analysing the phylogenetic structure of 12 pollination webs from isolated Pampean hills. Phylogenetic signal in interspecific interactions differed among webs, being stronger for flower-visiting insects than plants. Phylogenetic signal and overall co-phylogenetic congruence increased independently with hill size and isolation. We propose that habitat fragmentation would erode the phylogenetic structure of interaction webs. A decrease in phylogenetic signal and co-phylogenetic correspondence in plant-pollinator interactions could be associated with less reliable mutualism and erratic co-evolutionary change. © 2015 John Wiley & Sons Ltd/CNRS.

Vision 2010: The Future of Higher Education Business and Learning Applications

ERIC Educational Resources Information Center

Carey, Patrick; Gleason, Bernard

2006-01-01

The global software industry is in the midst of a major evolutionary shift--one based on open computing--and this trend, like many transformative trends in technology, is being led by the IT staffs and academic computing faculty of the higher education industry. The elements of this open computing approach are open source, open standards, open…

Behavioral Evidence for Host Transitions in Plant, Plant Parasite, and Insect Interactions.

PubMed

Halbritter, Dale A; Willett, Denis S; Gordon, Johnalyn M; Stelinski, Lukasz L; Daniels, Jaret C

2018-06-06

Specialized herbivorous insects have the ability to transition between host plant taxa, and considering the co-evolutionary history between plants and the organisms utilizing them is important to understanding plant insect interactions. We investigated the role of a pine tree parasite, dwarf mistletoe (Arceuthobium spp.) M. Bieb. Santalales: Viscaceae, in mediating interactions between Neophasia (Lepidoptera: Pieridae) butterflies and pine trees, the butterflies' larval hosts. Mistletoe is considered the butterflies' ancestral host, and the evolutionary transition to pine may have occurred recently. In Arizona, United States, we studied six sites in pine forest habitats: three in Neophasia menapia (Felder and R. Felder, 1859) habitat and three in Neophasia terlooii Behr, 1869 habitat. Each site contained six stands of trees that varied in mistletoe infection severity. Butterfly behavior was observed and ranked at each stand. Volatile compounds were collected from trees at each site and analyzed using gas chromatography-mass spectroscopy. Female butterflies landed on or patrolled around pine trees (i.e., interacted) more than males, and N. terlooii interacted more with pine trees than N. menapia. Both butterfly species interacted more with tree stands harboring greater mistletoe infection, and N. terlooii interacted more with heavily infected tree stands than did N. menapia. The influence of mistletoe on Neophasia behavior may be mediated by differences in tree volatiles resulting from mistletoe infection. Volatile profiles significantly differed between infected and uninfected pine trees. The role of mistletoe in mediating butterfly interactions with pines has implications for conservation biology and forest management, and highlights the importance of understanding an organism's niche in an evolutionary context.

Multi-Objective UAV Mission Planning Using Evolutionary Computation

DTIC Science & Technology

2008-03-01

on a Solution Space. . . . . . . . . . . . . . . . . . . . 41 4.3. Crowding distance calculation. Dark points are non-dominated solutions. [14...SPEA2 was devel- oped by Zitzler [64] as an improvement to the original SPEA algorithm [65]. SPEA2 Figure 4.3: Crowding distance calculation. Dark ...thesis, Los Angeles, CA, USA, 2003. Adviser-Maja J. Mataric . 114 21. Homberger, Joerg and Hermann Gehring. “Two Evolutionary Metaheuristics for the

Darwinism in quantum systems?

NASA Astrophysics Data System (ADS)

Iqbal, A.; Toor, A. H.

2002-03-01

We investigate the role of quantum mechanical effects in the central stability concept of evolutionary game theory, i.e., an evolutionarily stable strategy (ESS). Using two and three-player symmetric quantum games we show how the presence of quantum phenomenon of entanglement can be crucial to decide the course of evolutionary dynamics in a population of interacting individuals.

The evolution of plant-insect mutualisms.

PubMed

Bronstein, Judith L; Alarcón, Ruben; Geber, Monica

2006-01-01

Mutualisms (cooperative interactions between species) have had a central role in the generation and maintenance of life on earth. Insects and plants are involved in diverse forms of mutualism. Here we review evolutionary features of three prominent insect-plant mutualisms: pollination, protection and seed dispersal. We focus on addressing five central phenomena: evolutionary origins and maintenance of mutualism; the evolution of mutualistic traits; the evolution of specialization and generalization; coevolutionary processes; and the existence of cheating. Several features uniting very diverse insect-plant mutualisms are identified and their evolutionary implications are discussed: the involvement of one mobile and one sedentary partner; natural selection on plant rewards; the existence of a continuum from specialization to generalization; and the ubiquity of cheating, particularly on the part of insects. Plant-insect mutualisms have apparently both arisen and been lost repeatedly. Many adaptive hypotheses have been proposed to explain these transitions, and it is unlikely that any one of them dominates across interactions differing so widely in natural history. Evolutionary theory has a potentially important, but as yet largely unfilled, role to play in explaining the origins, maintenance, breakdown and evolution of insect-plant mutualisms.

Evolutionary Developmental Robotics: Improving Morphology and Control of Physical Robots.

PubMed

Vujovic, Vuk; Rosendo, Andre; Brodbeck, Luzius; Iida, Fumiya

2017-01-01

Evolutionary algorithms have previously been applied to the design of morphology and control of robots. The design space for such tasks can be very complex, which can prevent evolution from efficiently discovering fit solutions. In this article we introduce an evolutionary-developmental (evo-devo) experiment with real-world robots. It allows robots to grow their leg size to simulate ontogenetic morphological changes, and this is the first time that such an experiment has been performed in the physical world. To test diverse robot morphologies, robot legs of variable shapes were generated during the evolutionary process and autonomously built using additive fabrication. We present two cases with evo-devo experiments and one with evolution, and we hypothesize that the addition of a developmental stage can be used within robotics to improve performance. Moreover, our results show that a nonlinear system-environment interaction exists, which explains the nontrivial locomotion patterns observed. In the future, robots will be present in our daily lives, and this work introduces for the first time physical robots that evolve and grow while interacting with the environment.

NH2- in a cold ion trap with He buffer gas: Ab initio quantum modeling of the interaction potential and of state-changing multichannel dynamics

NASA Astrophysics Data System (ADS)

Hernández Vera, Mario; Yurtsever, Ersin; Wester, Roland; Gianturco, Franco A.

2018-05-01

We present an extensive range of accurate ab initio calculations, which map in detail the spatial electronic potential energy surface that describes the interaction between the molecular anion NH2 - (1A1) in its ground electronic state and the He atom. The time-independent close-coupling method is employed to generate the corresponding rotationally inelastic cross sections, and then the state-changing rates over a range of temperatures from 10 to 30 K, which is expected to realistically represent the experimental trapping conditions for this ion in a radio frequency ion trap filled with helium buffer gas. The overall evolutionary kinetics of the rotational level population involving the molecular anion in the cold trap is also modelled during a photodetachment experiment and analyzed using the computed rates. The present results clearly indicate the possibility of selectively detecting differences in behavior between the ortho- and para-anions undergoing photodetachment in the trap.

Simulation of Yeast Cooperation in 2D.

PubMed

Wang, M; Huang, Y; Wu, Z

2016-03-01

Evolution of cooperation has been an active research area in evolutionary biology in decades. An important type of cooperation is developed from group selection, when individuals form spatial groups to prevent them from foreign invasions. In this paper, we study the evolution of cooperation in a mixed population of cooperating and cheating yeast strains in 2D with the interactions among the yeast cells restricted to their small neighborhoods. We conduct a computer simulation based on a game theoretic model and show that cooperation is increased when the interactions are spatially restricted, whether the game is of a prisoner's dilemma, snow drifting, or mutual benefit type. We study the evolution of homogeneous groups of cooperators or cheaters and describe the conditions for them to sustain or expand in an opponent population. We show that under certain spatial restrictions, cooperator groups are able to sustain and expand as group sizes become large, while cheater groups fail to expand and keep them from collapse.

Toward the Language-Ready Brain: Biological Evolution and Primate Comparisons.

PubMed

Arbib, Michael A

2017-02-01

The approach to language evolution suggested here focuses on three questions: How did the human brain evolve so that humans can develop, use, and acquire languages? How can the evolutionary quest be informed by studying brain, behavior, and social interaction in monkeys, apes, and humans? How can computational modeling advance these studies? I hypothesize that the brain is language ready in that the earliest humans had protolanguages but not languages (i.e., communication systems endowed with rich and open-ended lexicons and grammars supporting a compositional semantics), and that it took cultural evolution to yield societies (a cultural constructed niche) in which language-ready brains could become language-using brains. The mirror system hypothesis is a well-developed example of this approach, but I offer it here not as a closed theory but as an evolving framework for the development and analysis of conflicting subhypotheses in the hope of their eventual integration. I also stress that computational modeling helps us understand the evolving role of mirror neurons, not in and of themselves, but only in their interaction with systems "beyond the mirror." Because a theory of evolution needs a clear characterization of what it is that evolved, I also outline ideas for research in neurolinguistics to complement studies of the evolution of the language-ready brain. A clear challenge is to go beyond models of speech comprehension to include sign language and models of production, and to link language to visuomotor interaction with the physical and social world.

Comprehensive inventory of protein complexes in the Protein Data Bank from consistent classification of interfaces.

PubMed

Bordner, Andrew J; Gorin, Andrey A

2008-05-12

Protein-protein interactions are ubiquitous and essential for all cellular processes. High-resolution X-ray crystallographic structures of protein complexes can reveal the details of their function and provide a basis for many computational and experimental approaches. Differentiation between biological and non-biological contacts and reconstruction of the intact complex is a challenging computational problem. A successful solution can provide additional insights into the fundamental principles of biological recognition and reduce errors in many algorithms and databases utilizing interaction information extracted from the Protein Data Bank (PDB). We have developed a method for identifying protein complexes in the PDB X-ray structures by a four step procedure: (1) comprehensively collecting all protein-protein interfaces; (2) clustering similar protein-protein interfaces together; (3) estimating the probability that each cluster is relevant based on a diverse set of properties; and (4) combining these scores for each PDB entry in order to predict the complex structure. The resulting clusters of biologically relevant interfaces provide a reliable catalog of evolutionary conserved protein-protein interactions. These interfaces, as well as the predicted protein complexes, are available from the Protein Interface Server (PInS) website (see Availability and requirements section). Our method demonstrates an almost two-fold reduction of the annotation error rate as evaluated on a large benchmark set of complexes validated from the literature. We also estimate relative contributions of each interface property to the accurate discrimination of biologically relevant interfaces and discuss possible directions for further improving the prediction method.

Evolutionary Models for Simple Biosystems

NASA Astrophysics Data System (ADS)

Bagnoli, Franco

The concept of evolutionary development of structures constituted a real revolution in biology: it was possible to understand how the very complex structures of life can arise in an out-of-equilibrium system. The investigation of such systems has shown that indeed, systems under a flux of energy or matter can self-organize into complex patterns, think for instance to Rayleigh-Bernard convection, Liesegang rings, patterns formed by granular systems under shear. Following this line, one could characterize life as a state of matter, characterized by the slow, continuous process that we call evolution. In this paper we try to identify the organizational level of life, that spans several orders of magnitude from the elementary constituents to whole ecosystems. Although similar structures can be found in other contexts like ideas (memes) in neural systems and self-replicating elements (computer viruses, worms, etc.) in computer systems, we shall concentrate on biological evolutionary structure, and try to put into evidence the role and the emergence of network structure in such systems.

Artificial intelligence in peer review: How can evolutionary computation support journal editors?

PubMed Central

Fronczak, Piotr; Fronczak, Agata; Ausloos, Marcel; Nedic, Olgica

2017-01-01

With the volume of manuscripts submitted for publication growing every year, the deficiencies of peer review (e.g. long review times) are becoming more apparent. Editorial strategies, sets of guidelines designed to speed up the process and reduce editors’ workloads, are treated as trade secrets by publishing houses and are not shared publicly. To improve the effectiveness of their strategies, editors in small publishing groups are faced with undertaking an iterative trial-and-error approach. We show that Cartesian Genetic Programming, a nature-inspired evolutionary algorithm, can dramatically improve editorial strategies. The artificially evolved strategy reduced the duration of the peer review process by 30%, without increasing the pool of reviewers (in comparison to a typical human-developed strategy). Evolutionary computation has typically been used in technological processes or biological ecosystems. Our results demonstrate that genetic programs can improve real-world social systems that are usually much harder to understand and control than physical systems. PMID:28931033

Improving protein-protein interaction prediction using evolutionary information from low-quality MSAs.

PubMed

Várnai, Csilla; Burkoff, Nikolas S; Wild, David L

2017-01-01

Evolutionary information stored in multiple sequence alignments (MSAs) has been used to identify the interaction interface of protein complexes, by measuring either co-conservation or co-mutation of amino acid residues across the interface. Recently, maximum entropy related correlated mutation measures (CMMs) such as direct information, decoupling direct from indirect interactions, have been developed to identify residue pairs interacting across the protein complex interface. These studies have focussed on carefully selected protein complexes with large, good-quality MSAs. In this work, we study protein complexes with a more typical MSA consisting of fewer than 400 sequences, using a set of 79 intramolecular protein complexes. Using a maximum entropy based CMM at the residue level, we develop an interface level CMM score to be used in re-ranking docking decoys. We demonstrate that our interface level CMM score compares favourably to the complementarity trace score, an evolutionary information-based score measuring co-conservation, when combined with the number of interface residues, a knowledge-based potential and the variability score of individual amino acid sites. We also demonstrate, that, since co-mutation and co-complementarity in the MSA contain orthogonal information, the best prediction performance using evolutionary information can be achieved by combining the co-mutation information of the CMM with co-conservation information of a complementarity trace score, predicting a near-native structure as the top prediction for 41% of the dataset. The method presented is not restricted to small MSAs, and will likely improve interface prediction also for complexes with large and good-quality MSAs.

Evolutionary Diversification of Prey and Predator Species Facilitated by Asymmetric Interactions

PubMed Central

Zu, Jian; Wang, Jinliang; Huang, Gang

2016-01-01

We investigate the influence of asymmetric interactions on coevolutionary dynamics of a predator-prey system by using the theory of adaptive dynamics. We assume that the defense ability of prey and the attack ability of predators all can adaptively evolve, either caused by phenotypic plasticity or by behavioral choice, but there are certain costs in terms of their growth rate or death rate. The coevolutionary model is constructed from a deterministic approximation of random mutation-selection process. To sum up, if prey’s trade-off curve is globally weakly concave, then five outcomes of coevolution are demonstrated, which depend on the intensity and shape of asymmetric predator-prey interactions and predator’s trade-off shape. Firstly, we find that if there is a weakly decelerating cost and a weakly accelerating benefit for predator species, then evolutionary branching in the predator species may occur, but after branching further coevolution may lead to extinction of the predator species with a larger trait value. However, if there is a weakly accelerating cost and a weakly accelerating benefit for predator species, then evolutionary branching in the predator species is also possible and after branching the dimorphic predator can evolutionarily stably coexist with a monomorphic prey species. Secondly, if the asymmetric interactions become a little strong, then prey and predators will evolve to an evolutionarily stable equilibrium, at which they can stably coexist on a long-term timescale of evolution. Thirdly, if there is a weakly accelerating cost and a relatively strongly accelerating benefit for prey species, then evolutionary branching in the prey species is possible and the finally coevolutionary outcome contains a dimorphic prey and a monomorphic predator species. Fourthly, if the asymmetric interactions become more stronger, then predator-prey coevolution may lead to cycles in both traits and equilibrium population densities. The Red Queen dynamic is a possible outcome under asymmetric predator-prey interactions. PMID:27685540

Evolutionary Diversification of Prey and Predator Species Facilitated by Asymmetric Interactions.

PubMed

Zu, Jian; Wang, Jinliang; Huang, Gang

We investigate the influence of asymmetric interactions on coevolutionary dynamics of a predator-prey system by using the theory of adaptive dynamics. We assume that the defense ability of prey and the attack ability of predators all can adaptively evolve, either caused by phenotypic plasticity or by behavioral choice, but there are certain costs in terms of their growth rate or death rate. The coevolutionary model is constructed from a deterministic approximation of random mutation-selection process. To sum up, if prey's trade-off curve is globally weakly concave, then five outcomes of coevolution are demonstrated, which depend on the intensity and shape of asymmetric predator-prey interactions and predator's trade-off shape. Firstly, we find that if there is a weakly decelerating cost and a weakly accelerating benefit for predator species, then evolutionary branching in the predator species may occur, but after branching further coevolution may lead to extinction of the predator species with a larger trait value. However, if there is a weakly accelerating cost and a weakly accelerating benefit for predator species, then evolutionary branching in the predator species is also possible and after branching the dimorphic predator can evolutionarily stably coexist with a monomorphic prey species. Secondly, if the asymmetric interactions become a little strong, then prey and predators will evolve to an evolutionarily stable equilibrium, at which they can stably coexist on a long-term timescale of evolution. Thirdly, if there is a weakly accelerating cost and a relatively strongly accelerating benefit for prey species, then evolutionary branching in the prey species is possible and the finally coevolutionary outcome contains a dimorphic prey and a monomorphic predator species. Fourthly, if the asymmetric interactions become more stronger, then predator-prey coevolution may lead to cycles in both traits and equilibrium population densities. The Red Queen dynamic is a possible outcome under asymmetric predator-prey interactions.

Evolution of SH2 domains and phosphotyrosine signalling networks

PubMed Central

Liu, Bernard A.; Nash, Piers D.

2012-01-01

Src homology 2 (SH2) domains mediate selective protein–protein interactions with tyrosine phosphorylated proteins, and in doing so define specificity of phosphotyrosine (pTyr) signalling networks. SH2 domains and protein-tyrosine phosphatases expand alongside protein-tyrosine kinases (PTKs) to coordinate cellular and organismal complexity in the evolution of the unikont branch of the eukaryotes. Examination of conserved families of PTKs and SH2 domain proteins provides fiduciary marks that trace the evolutionary landscape for the development of complex cellular systems in the proto-metazoan and metazoan lineages. The evolutionary provenance of conserved SH2 and PTK families reveals the mechanisms by which diversity is achieved through adaptations in tissue-specific gene transcription, altered ligand binding, insertions of linear motifs and the gain or loss of domains following gene duplication. We discuss mechanisms by which pTyr-mediated signalling networks evolve through the development of novel and expanded families of SH2 domain proteins and the elaboration of connections between pTyr-signalling proteins. These changes underlie the variety of general and specific signalling networks that give rise to tissue-specific functions and increasingly complex developmental programmes. Examination of SH2 domains from an evolutionary perspective provides insight into the process by which evolutionary expansion and modification of molecular protein interaction domain proteins permits the development of novel protein-interaction networks and accommodates adaptation of signalling networks. PMID:22889907

Differential Adhesion between Moving Particles as a Mechanism for the Evolution of Social Groups

PubMed Central

Garcia, Thomas; Brunnet, Leonardo Gregory; De Monte, Silvia

2014-01-01

The evolutionary stability of cooperative traits, that are beneficial to other individuals but costly to their carrier, is considered possible only through the establishment of a sufficient degree of assortment between cooperators. Chimeric microbial populations, characterized by simple interactions between unrelated individuals, restrain the applicability of standard mechanisms generating such assortment, in particular when cells disperse between successive reproductive events such as happens in Dicyostelids and Myxobacteria. In this paper, we address the evolutionary dynamics of a costly trait that enhances attachment to others as well as group cohesion. By modeling cells as self-propelled particles moving on a plane according to local interaction forces and undergoing cycles of aggregation, reproduction and dispersal, we show that blind differential adhesion provides a basis for assortment in the process of group formation. When reproductive performance depends on the social context of players, evolution by natural selection can lead to the success of the social trait, and to the concomitant emergence of sizeable groups. We point out the conditions on the microscopic properties of motion and interaction that make such evolutionary outcome possible, stressing that the advent of sociality by differential adhesion is restricted to specific ecological contexts. Moreover, we show that the aggregation process naturally implies the existence of non-aggregated particles, and highlight their crucial evolutionary role despite being largely neglected in theoretical models for the evolution of sociality. PMID:24586133

InterEvDock: a docking server to predict the structure of protein–protein interactions using evolutionary information

PubMed Central

Yu, Jinchao; Vavrusa, Marek; Andreani, Jessica; Rey, Julien; Tufféry, Pierre; Guerois, Raphaël

2016-01-01

The structural modeling of protein–protein interactions is key in understanding how cell machineries cross-talk with each other. Molecular docking simulations provide efficient means to explore how two unbound protein structures interact. InterEvDock is a server for protein docking based on a free rigid-body docking strategy. A systematic rigid-body docking search is performed using the FRODOCK program and the resulting models are re-scored with InterEvScore and SOAP-PP statistical potentials. The InterEvScore potential was specifically designed to integrate co-evolutionary information in the docking process. InterEvDock server is thus particularly well suited in case homologous sequences are available for both binding partners. The server returns 10 structures of the most likely consensus models together with 10 predicted residues most likely involved in the interface. In 91% of all complexes tested in the benchmark, at least one residue out of the 10 predicted is involved in the interface, providing useful guidelines for mutagenesis. InterEvDock is able to identify a correct model among the top10 models for 49% of the rigid-body cases with evolutionary information, making it a unique and efficient tool to explore structural interactomes under an evolutionary perspective. The InterEvDock web interface is available at http://bioserv.rpbs.univ-paris-diderot.fr/services/InterEvDock/. PMID:27131368

Marr's levels and the minimalist program.

PubMed

Johnson, Mark

2017-02-01

A simple change to a cognitive system at Marr's computational level may entail complex changes at the other levels of description of the system. The implementational level complexity of a change, rather than its computational level complexity, may be more closely related to the plausibility of a discrete evolutionary event causing that change. Thus the formal complexity of a change at the computational level may not be a good guide to the plausibility of an evolutionary event introducing that change. For example, while the Minimalist Program's Merge is a simple formal operation (Berwick & Chomsky, 2016), the computational mechanisms required to implement the language it generates (e.g., to parse the language) may be considerably more complex. This has implications for the theory of grammar: theories of grammar which involve several kinds of syntactic operations may be no less evolutionarily plausible than a theory of grammar that involves only one. A deeper understanding of human language at the algorithmic and implementational levels could strengthen Minimalist Program's account of the evolution of language.

Soft computing approach to 3D lung nodule segmentation in CT.

PubMed

Badura, P; Pietka, E

2014-10-01

This paper presents a novel, multilevel approach to the segmentation of various types of pulmonary nodules in computed tomography studies. It is based on two branches of computational intelligence: the fuzzy connectedness (FC) and the evolutionary computation. First, the image and auxiliary data are prepared for the 3D FC analysis during the first stage of an algorithm - the masks generation. Its main goal is to process some specific types of nodules connected to the pleura or vessels. It consists of some basic image processing operations as well as dedicated routines for the specific cases of nodules. The evolutionary computation is performed on the image and seed points in order to shorten the FC analysis and improve its accuracy. After the FC application, the remaining vessels are removed during the postprocessing stage. The method has been validated using the first dataset of studies acquired and described by the Lung Image Database Consortium (LIDC) and by its latest release - the LIDC-IDRI (Image Database Resource Initiative) database. Copyright © 2014 Elsevier Ltd. All rights reserved.

Evolutionary plasticity of plasma membrane interaction in DREPP family proteins.

PubMed

Vosolsobě, Stanislav; Petrášek, Jan; Schwarzerová, Kateřina

2017-05-01

The plant-specific DREPP protein family comprises proteins that were shown to regulate the actin and microtubular cytoskeleton in a calcium-dependent manner. Our phylogenetic analysis showed that DREPPs first appeared in ferns and that DREPPs have a rapid and plastic evolutionary history in plants. Arabidopsis DREPP paralogues called AtMDP25/PCaP1 and AtMAP18/PCaP2 are N-myristoylated, which has been reported as a key factor in plasma membrane localization. Here we show that N-myristoylation is neither conserved nor ancestral for the DREPP family. Instead, by using confocal microscopy and a new method for quantitative evaluation of protein membrane localization, we show that DREPPs rely on two mechanisms ensuring their plasma membrane localization. These include N-myristoylation and electrostatic interaction of a polybasic amino acid cluster. We propose that various plasma membrane association mechanisms resulting from the evolutionary plasticity of DREPPs are important for refining plasma membrane interaction of these signalling proteins under various conditions and in various cells. Copyright © 2017 Elsevier B.V. All rights reserved.

Artificial Exo-Society Modeling: a New Tool for SETI Research

NASA Astrophysics Data System (ADS)

Gardner, James N.

2002-01-01

One of the newest fields of complexity research is artificial society modeling. Methodologically related to artificial life research, artificial society modeling utilizes agent-based computer simulation tools like SWARM and SUGARSCAPE developed by the Santa Fe Institute, Los Alamos National Laboratory and the Bookings Institution in an effort to introduce an unprecedented degree of rigor and quantitative sophistication into social science research. The broad aim of artificial society modeling is to begin the development of a more unified social science that embeds cultural evolutionary processes in a computational environment that simulates demographics, the transmission of culture, conflict, economics, disease, the emergence of groups and coadaptation with an environment in a bottom-up fashion. When an artificial society computer model is run, artificial societal patterns emerge from the interaction of autonomous software agents (the "inhabitants" of the artificial society). Artificial society modeling invites the interpretation of society as a distributed computational system and the interpretation of social dynamics as a specialized category of computation. Artificial society modeling techniques offer the potential of computational simulation of hypothetical alien societies in much the same way that artificial life modeling techniques offer the potential to model hypothetical exobiological phenomena. NASA recently announced its intention to begin exploring the possibility of including artificial life research within the broad portfolio of scientific fields comprised by the interdisciplinary astrobiology research endeavor. It may be appropriate for SETI researchers to likewise commence an exploration of the possible inclusion of artificial exo-society modeling within the SETI research endeavor. Artificial exo-society modeling might be particularly useful in a post-detection environment by (1) coherently organizing the set of data points derived from a detected ETI signal, (2) mapping trends in the data points over time (assuming receipt of an extended ETI signal), and (3) projecting such trends forward to derive alternative cultural evolutionary scenarios for the exo-society under analysis. The latter exercise might be particularly useful to compensate for the inevitable time lag between generation of an ETI signal and receipt of an ETI signal on Earth. For this reason, such an exercise might be a helpful adjunct to the decisional process contemplated by Paragraph 9 of the Declaration of Principles Concerning Activities Following the Detection of Extraterrestrial Intelligence.

Ancient human miRNAs are more likely to have broad functions and disease associations than young miRNAs.

PubMed

Patel, Vir D; Capra, John A

2017-08-31

microRNAs (miRNAs) are essential to the regulation of gene expression in eukaryotes, and improper expression of miRNAs contributes to hundreds of diseases. Despite the essential functions of miRNAs, the evolutionary dynamics of how they are integrated into existing gene regulatory and functional networks is not well understood. Knowledge of the origin and evolutionary history a gene has proven informative about its functions and disease associations; we hypothesize that incorporating the evolutionary origins of miRNAs into analyses will help resolve differences in their functional dynamics and how they influence disease. We computed the phylogenetic age of miRNAs across 146 species and quantified the relationship between human miRNA age and several functional attributes. Older miRNAs are significantly more likely to be associated with disease than younger miRNAs, and the number of associated diseases increases with age. As has been observed for genes, the miRNAs associated with different diseases have different age profiles. For example, human miRNAs implicated in cancer are enriched for origins near the dawn of animal multicellularity. Consistent with the increasing contribution of miRNAs to disease with age, older miRNAs target more genes than younger miRNAs, and older miRNAs are expressed in significantly more tissues. Furthermore, miRNAs of all ages exhibit a strong preference to target older genes; 93% of validated miRNA gene targets were in existence at the origin of the targeting miRNA. Finally, we find that human miRNAs in evolutionarily related families are more similar in their targets and expression profiles than unrelated miRNAs. Considering the evolutionary origin and history of a miRNA provides useful context for the analysis of its function. Consistent with recent work in Drosophila, our results support a model in which miRNAs increase their expression and functional regulatory interactions over evolutionary time, and thus older miRNAs have increased potential to cause disease. We anticipate that these patterns hold across mammalian species; however, comprehensively evaluating them will require refining miRNA annotations across species and collecting functional data in non-human systems.

The consequences of lifetime and evolutionary exposure to toxic prey: changes in avoidance behaviour through ontogeny.

PubMed

Robbins, T R; Langkilde, T

2012-10-01

Responses to novel threats (e.g. invasive species) can involve genetic changes or plastic shifts in phenotype. There is controversy over the relative importance of these processes for species survival of such perturbations, but we are realizing they are not mutually exclusive. Native eastern fence lizards (Sceloporus undulatus) have adapted to top-down predation pressure imposed by the invasive red imported fire ant (Solenopsis invicta) via changes in adult (but not juvenile) lizard antipredator behaviour. Here, we examine the largely ignored, but potentially equally important, bottom-up effect of fire ants as toxic prey for lizards. We test how fire ant consumption (or avoidance) is affected by lifetime (via plasticity) and evolutionary (via natural selection) exposure to fire ants by comparing field-caught and laboratory-reared lizards, respectively, from fire ant-invaded and uninvaded populations. More naive juveniles from invaded populations ate fire ants than did adults, reflecting a natural ontogenetic dietary shift away from ants. Laboratory-reared lizards from the invaded site were less likely to eat fire ants than were those from the uninvaded site, suggesting a potential evolutionary shift in feeding behaviour. Lifetime and evolutionary exposure interacted across ontogeny, however, and field-caught lizards from the invaded site exhibited opposite ontogenetic trends; adults were more likely to eat fire ants than were juveniles. Our results suggest that plastic and evolutionary processes may both play important roles in permitting species survival of novel threats. We further reveal how complex interactions can shape adaptive responses to multimodal impacts imposed by invaders: in our system, fire ants impose stronger bottom-up selection than top-down selection, with each selection regime changing differently across lizard ontogeny. © 2012 The Authors. Journal of Evolutionary Biology © 2012 European Society For Evolutionary Biology.

Applications of genetic programming in cancer research.

PubMed

Worzel, William P; Yu, Jianjun; Almal, Arpit A; Chinnaiyan, Arul M

2009-02-01

The theory of Darwinian evolution is the fundamental keystones of modern biology. Late in the last century, computer scientists began adapting its principles, in particular natural selection, to complex computational challenges, leading to the emergence of evolutionary algorithms. The conceptual model of selective pressure and recombination in evolutionary algorithms allow scientists to efficiently search high dimensional space for solutions to complex problems. In the last decade, genetic programming has been developed and extensively applied for analysis of molecular data to classify cancer subtypes and characterize the mechanisms of cancer pathogenesis and development. This article reviews current successes using genetic programming and discusses its potential impact in cancer research and treatment in the near future.

Robust electromagnetically guided endoscopic procedure using enhanced particle swarm optimization for multimodal information fusion.

PubMed

Luo, Xiongbiao; Wan, Ying; He, Xiangjian

2015-04-01

Electromagnetically guided endoscopic procedure, which aims at accurately and robustly localizing the endoscope, involves multimodal sensory information during interventions. However, it still remains challenging in how to integrate these information for precise and stable endoscopic guidance. To tackle such a challenge, this paper proposes a new framework on the basis of an enhanced particle swarm optimization method to effectively fuse these information for accurate and continuous endoscope localization. The authors use the particle swarm optimization method, which is one of stochastic evolutionary computation algorithms, to effectively fuse the multimodal information including preoperative information (i.e., computed tomography images) as a frame of reference, endoscopic camera videos, and positional sensor measurements (i.e., electromagnetic sensor outputs). Since the evolutionary computation method usually limits its possible premature convergence and evolutionary factors, the authors introduce the current (endoscopic camera and electromagnetic sensor's) observation to boost the particle swarm optimization and also adaptively update evolutionary parameters in accordance with spatial constraints and the current observation, resulting in advantageous performance in the enhanced algorithm. The experimental results demonstrate that the authors' proposed method provides a more accurate and robust endoscopic guidance framework than state-of-the-art methods. The average guidance accuracy of the authors' framework was about 3.0 mm and 5.6° while the previous methods show at least 3.9 mm and 7.0°. The average position and orientation smoothness of their method was 1.0 mm and 1.6°, which is significantly better than the other methods at least with (2.0 mm and 2.6°). Additionally, the average visual quality of the endoscopic guidance was improved to 0.29. A robust electromagnetically guided endoscopy framework was proposed on the basis of an enhanced particle swarm optimization method with using the current observation information and adaptive evolutionary factors. The authors proposed framework greatly reduced the guidance errors from (4.3, 7.8) to (3.0 mm, 5.6°), compared to state-of-the-art methods.

Alleles versus genotypes: Genetic interactions and the dynamics of selection in sexual populations

NASA Astrophysics Data System (ADS)

Neher, Richard

2010-03-01

Physical interactions between amino-acids are essential for protein structure and activity, while protein-protein interactions and regulatory interactions are central to cellular function. As a consequence of these interactions, the combined effect of two mutations can differ from the sum of the individual effects of the mutations. This phenomenon of genetic interaction is known as epistasis. However, the importance of epistasis and its effects on evolutionary dynamics are poorly understood, especially in sexual populations where recombination breaks up existing combinations of alleles to produce new ones. Here, we present a computational model of selection dynamics involving many epistatic loci in a recombining population. We demonstrate that a large number of polymorphic interacting loci can, despite frequent recombination, exhibit cooperative behavior that locks alleles into favorable genotypes leading to a population consisting of a set of competing clones. As the recombination rate exceeds a certain critical value this ``genotype selection'' phase disappears in an abrupt transition giving way to ``allele selection'' - the phase where different loci are only weakly correlated as expected in sexually reproducing populations. Clustering of interacting sets of genes on a chromosome leads to the emergence of an intermediate regime, where localized blocks of cooperating alleles lock into genetic modules. Large populations attain highest fitness at a recombination rate just below critical, suggesting that natural selection might tune recombination rates to balance the beneficial aspect of exploration of genotype space with the breaking up of synergistic allele combinations.

Interactive optimization approach for optimal impulsive rendezvous using primer vector and evolutionary algorithms

NASA Astrophysics Data System (ADS)

Luo, Ya-Zhong; Zhang, Jin; Li, Hai-yang; Tang, Guo-Jin

2010-08-01

In this paper, a new optimization approach combining primer vector theory and evolutionary algorithms for fuel-optimal non-linear impulsive rendezvous is proposed. The optimization approach is designed to seek the optimal number of impulses as well as the optimal impulse vectors. In this optimization approach, adding a midcourse impulse is determined by an interactive method, i.e. observing the primer-magnitude time history. An improved version of simulated annealing is employed to optimize the rendezvous trajectory with the fixed-number of impulses. This interactive approach is evaluated by three test cases: coplanar circle-to-circle rendezvous, same-circle rendezvous and non-coplanar rendezvous. The results show that the interactive approach is effective and efficient in fuel-optimal non-linear rendezvous design. It can guarantee solutions, which satisfy the Lawden's necessary optimality conditions.

Reciprocity, Punishment, Institutions: The Streets to Social Collaboration -- New Theories on How Emerging Social Artifacts Control Our Lives in Society

NASA Astrophysics Data System (ADS)

Gerace, Giuliana

What mechanisms induce and support cooperation in social interaction? Traditional rational-choice perspective has resulted ineffective to keep track of complex real-world dynamics of cooperation. On the other hand, perspectives based on the justification of fairness preferences as internalized behavioural forces driving realistic cooperative interactions are notoriously incomplete and rather fuzzy with respect to their theoretical foundations. After considering recognized evolutionary accounts of the emergence and resilience of social standards, we endorse the view according to which the key to understanding evolutionary dynamics of social engagement is to be found in individual motivational attitudes to interaction. But, beyond any psychological implications, we suggest not exiting from the "logic of reciprocity" in considering the rationality of preferences for social interaction. Preliminary supporting experimental evidence is provided.

Evolutionary medicine: its scope, interest and potential

PubMed Central

Stearns, Stephen C.

2012-01-01

This review is aimed at readers seeking an introductory overview, teaching courses and interested in visionary ideas. It first describes the range of topics covered by evolutionary medicine, which include human genetic variation, mismatches to modernity, reproductive medicine, degenerative disease, host–pathogen interactions and insights from comparisons with other species. It then discusses priorities for translational research, basic research and health management. Its conclusions are that evolutionary thinking should not displace other approaches to medical science, such as molecular medicine and cell and developmental biology, but that evolutionary insights can combine with and complement established approaches to reduce suffering and save lives. Because we are on the cusp of so much new research and innovative insights, it is hard to estimate how much impact evolutionary thinking will have on medicine, but it is already clear that its potential is enormous. PMID:22933370

Evolutionary medicine: its scope, interest and potential.

PubMed

Stearns, Stephen C

2012-11-07

This review is aimed at readers seeking an introductory overview, teaching courses and interested in visionary ideas. It first describes the range of topics covered by evolutionary medicine, which include human genetic variation, mismatches to modernity, reproductive medicine, degenerative disease, host-pathogen interactions and insights from comparisons with other species. It then discusses priorities for translational research, basic research and health management. Its conclusions are that evolutionary thinking should not displace other approaches to medical science, such as molecular medicine and cell and developmental biology, but that evolutionary insights can combine with and complement established approaches to reduce suffering and save lives. Because we are on the cusp of so much new research and innovative insights, it is hard to estimate how much impact evolutionary thinking will have on medicine, but it is already clear that its potential is enormous.

Asymmetric ecological conditions favor Red-Queen type of continued evolution over stasis

PubMed Central

Nordbotten, Jan Martin; Stenseth, Nils C.

2016-01-01

Four decades ago, Leigh Van Valen presented the Red Queen’s hypothesis to account for evolution of species within a multispecies ecological community [Van Valen L (1973) Evol Theory 1(1):1–30]. The overall conclusion of Van Valen’s analysis was that evolution would continue even in the absence of abiotic perturbations. Stenseth and Maynard Smith presented in 1984 [Stenseth NC, Maynard Smith J (1984) Evolution 38(4):870–880] a model for the Red Queen’s hypothesis showing that both Red-Queen type of continuous evolution and stasis could result from a model with biotically driven evolution. However, although that contribution demonstrated that both evolutionary outcomes were possible, it did not identify which ecological conditions would lead to each of these evolutionary outcomes. Here, we provide, using a simple, yet general population-biologically founded eco-evolutionary model, such analytically derived conditions: Stasis will predominantly emerge whenever the ecological system contains only symmetric ecological interactions, whereas both Red-Queen and stasis type of evolution may result if the ecological interactions are asymmetrical, and more likely so with increasing degree of asymmetry in the ecological system (i.e., the more trophic interactions, host–pathogen interactions, and the like there are [i.e., +/− type of ecological interactions as well as asymmetric competitive (−/−) and mutualistic (+/+) ecological interactions]). In the special case of no between-generational genetic variance, our results also predict dynamics within these types of purely ecological systems. PMID:26831108

The roles of ecological fitting, phylogeny and physiological equivalence in understanding realized and fundamental host ranges in endoparasitoid wasps.

PubMed

Harvey, J A; Ximénez de Embún, M G; Bukovinszky, T; Gols, R

2012-10-01

Co-evolutionary theory underpins our understanding of interactions in nature involving plant-herbivore and host-parasite interactions. However, many studies that are published in the empirical literature that have explored life history and development strategies between endoparasitoid wasps and their hosts are based on species that have no evolutionary history with one another. Here, we investigated novel associations involving two closely related solitary endoparasitoids that originate from Europe and North America and several of their natural and factitious hosts from both continents. The natural hosts of both species are also closely related, all being members of the same family. We compared development and survival of both parasitoids on the four host species and predicted that parasitoid performance is better on their own natural hosts. In contrast with this expectation, survival, adult size and development time of both parasitoids were similar on all (with one exception) hosts, irrespective as to their geographic origin. Our results show that phylogenetic affinity among the natural and factitious hosts plays an important role in their nutritional suitability for related parasitoids. Evolved traits in parasitoids, such as immune suppression and development, thus enable them to successfully develop in novel host species with which they have no evolutionary history. Our results show that host suitability for specialized organisms like endoparasitoids is closely linked with phylogenetic history and macro-evolution as well as local adaptation and micro-evolution. We argue that the importance of novel interactions and 'ecological fitting' based on phylogeny is a greatly underappreciated concept in many resource-consumer studies. © 2012 The Authors. Journal of Evolutionary Biology © 2012 European Society For Evolutionary Biology.

Computability, Gödel's incompleteness theorem, and an inherent limit on the predictability of evolution

PubMed Central

Day, Troy

2012-01-01

The process of evolutionary diversification unfolds in a vast genotypic space of potential outcomes. During the past century, there have been remarkable advances in the development of theory for this diversification, and the theory's success rests, in part, on the scope of its applicability. A great deal of this theory focuses on a relatively small subset of the space of potential genotypes, chosen largely based on historical or contemporary patterns, and then predicts the evolutionary dynamics within this pre-defined set. To what extent can such an approach be pushed to a broader perspective that accounts for the potential open-endedness of evolutionary diversification? There have been a number of significant theoretical developments along these lines but the question of how far such theory can be pushed has not been addressed. Here a theorem is proven demonstrating that, because of the digital nature of inheritance, there are inherent limits on the kinds of questions that can be answered using such an approach. In particular, even in extremely simple evolutionary systems, a complete theory accounting for the potential open-endedness of evolution is unattainable unless evolution is progressive. The theorem is closely related to Gödel's incompleteness theorem, and to the halting problem from computability theory. PMID:21849390

The influence of space and time on the evolution of altruistic defence: the case of ant slave rebellion.

PubMed

Metzler, D; Jordan, F; Pamminger, T; Foitzik, S

2016-05-01

How can antiparasite defence traits evolve even if they do not directly benefit their carriers? An example of such an indirect defence is rebellion of enslaved Temnothorax longispinosus ant workers against their social parasite Temnothorax americanus, a slavemaking ant. Ant slaves have been observed to kill their oppressors' offspring, a behaviour from which the sterile slaves cannot profit directly. Parasite brood killing could, however, reduce raiding pressure on related host colonies nearby. We analyse with extensive computer simulations for the Temnothorax slavemaker system under what conditions a hypothetical rebel allele could invade a host population, and in particular, how host-parasite dynamics and population structure influence the rebel allele's success. Exploring a wide range of model parameters, we only found a small number of parameter combinations for which kin selection or multilevel selection could allow a slave rebellion allele to spread in the host population. Furthermore, we did not detect any cases in which the reduction of raiding pressure in the close vicinity of the slavemaker nest would substantially contribute to the inclusive fitness of rebels. This suggests that slave rebellion is not costly and perhaps a side-effect of some other beneficial trait. In some of our simulations, however, even a costly rebellion allele could spread in the population. This was possible when host-parasite interactions led to a metapopulation dynamic with frequent local extinctions and recolonizations of demes by the offspring of few immigrants. © 2016 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2016 European Society For Evolutionary Biology.

Divergent and convergent evolution in metastases suggest treatment strategies based on specific metastatic sites

PubMed Central

Cunningham, Jessica J.; Brown, Joel S.; Vincent, Thomas L.

2015-01-01

Background and objective: Systemic therapy for metastatic cancer is currently determined exclusively by the site of tumor origin. Yet, there is increasing evidence that the molecular characteristics of metastases significantly differ from the primary tumor. We define the evolutionary dynamics of metastases that govern this molecular divergence and examine their potential contribution to variations in response to targeted therapies. Methodology: Darwinian interactions of transformed cells with the tissue microenvironments at primary and metastatic sites are analyzed using evolutionary game theory. Computational models simulate responses to targeted therapies in different organs within the same patient. Results: Tumor cells, although maximally fit at their primary site, typically have lower fitness on the adaptive landscapes offered by the metastatic sites due to organ-specific variations in mesenchymal properties and signaling pathways. Clinically evident metastases usually exhibit time-dependent divergence from the phenotypic mean of the primary population as the tumor cells evolve and adapt to their new circumstances. In contrast, tumors from different primary sites evolving on identical metastatic adaptive landscapes exhibit phenotypic convergence. Thus, metastases in the liver from different primary tumors and even in different hosts will evolve toward similar adaptive phenotypes. The combination of evolutionary divergence from the primary cancer phenotype and convergence towards similar adaptive strategies in the same tissue cause significant variations in treatment responses particularly for highly targeted therapies. Conclusion and implications: The results suggest that optimal therapies for disseminated cancer must take into account the site(s) of metastatic growth as well as the primary organ. PMID:25794501

The role of inflammation in depression: from evolutionary imperative to modern treatment target

PubMed Central

Miller, Andrew H.; Raison, Charles L.

2017-01-01

Crosstalk between inflammatory pathways and neurocircuits in the brain can lead to behavioural responses, such as avoidance and alarm, that are likely to have provided early humans with an evolutionary advantage in their interactions with pathogens and predators. However, in modern times, such interactions between inflammation and the brain appear to drive the development of depression and may contribute to non-responsiveness to current antidepressant therapies. Recent data have elucidated the mechanisms by which the innate and adaptive immune systems interact with neurotransmitters and neurocircuits to influence the risk for depression. Here, we detail our current understanding of these pathways and discuss the therapeutic potential of targeting the immune system to treat depression. PMID:26711676

Improving protein-protein interactions prediction accuracy using protein evolutionary information and relevance vector machine model.

PubMed

An, Ji-Yong; Meng, Fan-Rong; You, Zhu-Hong; Chen, Xing; Yan, Gui-Ying; Hu, Ji-Pu

2016-10-01

Predicting protein-protein interactions (PPIs) is a challenging task and essential to construct the protein interaction networks, which is important for facilitating our understanding of the mechanisms of biological systems. Although a number of high-throughput technologies have been proposed to predict PPIs, there are unavoidable shortcomings, including high cost, time intensity, and inherently high false positive rates. For these reasons, many computational methods have been proposed for predicting PPIs. However, the problem is still far from being solved. In this article, we propose a novel computational method called RVM-BiGP that combines the relevance vector machine (RVM) model and Bi-gram Probabilities (BiGP) for PPIs detection from protein sequences. The major improvement includes (1) Protein sequences are represented using the Bi-gram probabilities (BiGP) feature representation on a Position Specific Scoring Matrix (PSSM), in which the protein evolutionary information is contained; (2) For reducing the influence of noise, the Principal Component Analysis (PCA) method is used to reduce the dimension of BiGP vector; (3) The powerful and robust Relevance Vector Machine (RVM) algorithm is used for classification. Five-fold cross-validation experiments executed on yeast and Helicobacter pylori datasets, which achieved very high accuracies of 94.57 and 90.57%, respectively. Experimental results are significantly better than previous methods. To further evaluate the proposed method, we compare it with the state-of-the-art support vector machine (SVM) classifier on the yeast dataset. The experimental results demonstrate that our RVM-BiGP method is significantly better than the SVM-based method. In addition, we achieved 97.15% accuracy on imbalance yeast dataset, which is higher than that of balance yeast dataset. The promising experimental results show the efficiency and robust of the proposed method, which can be an automatic decision support tool for future proteomics research. For facilitating extensive studies for future proteomics research, we developed a freely available web server called RVM-BiGP-PPIs in Hypertext Preprocessor (PHP) for predicting PPIs. The web server including source code and the datasets are available at http://219.219.62.123:8888/BiGP/. © 2016 The Authors Protein Science published by Wiley Periodicals, Inc. on behalf of The Protein Society.

Genomic investigations of evolutionary dynamics and epistasis in microbial evolution experiments.

PubMed

Jerison, Elizabeth R; Desai, Michael M

2015-12-01

Microbial evolution experiments enable us to watch adaptation in real time, and to quantify the repeatability and predictability of evolution by comparing identical replicate populations. Further, we can resurrect ancestral types to examine changes over evolutionary time. Until recently, experimental evolution has been limited to measuring phenotypic changes, or to tracking a few genetic markers over time. However, recent advances in sequencing technology now make it possible to extensively sequence clones or whole-population samples from microbial evolution experiments. Here, we review recent work exploiting these techniques to understand the genomic basis of evolutionary change in experimental systems. We first focus on studies that analyze the dynamics of genome evolution in microbial systems. We then survey work that uses observations of sequence evolution to infer aspects of the underlying fitness landscape, concentrating on the epistatic interactions between mutations and the constraints these interactions impose on adaptation. Copyright © 2015 Elsevier Ltd. All rights reserved.

Newly rare or newly common: evolutionary feedbacks through changes in population density and relative species abundance, and their management implications

PubMed Central

Lankau, Richard A; Strauss, Sharon Y

2011-01-01

Environmental management typically seeks to increase or maintain the population sizes of desirable species and to decrease population sizes of undesirable pests, pathogens, or invaders. With changes in population size come long-recognized changes in ecological processes that act in a density-dependent fashion. While the ecological effects of density dependence have been well studied, the evolutionary effects of changes in population size, via changes in ecological interactions with community members, are underappreciated. Here, we provide examples of changing selective pressures on, or evolution in, species as a result of changes in either density of conspecifics or changes in the frequency of heterospecific versus conspecific interactions. We also discuss the management implications of such evolutionary responses in species that have experienced rapid increases or decreases in density caused by human actions. PMID:25567977

Framework for computationally efficient optimal irrigation scheduling using ant colony optimization

USDA-ARS?s Scientific Manuscript database

A general optimization framework is introduced with the overall goal of reducing search space size and increasing the computational efficiency of evolutionary algorithm application for optimal irrigation scheduling. The framework achieves this goal by representing the problem in the form of a decisi...

Pervasive Computing and Communication Technologies for U-Learning

ERIC Educational Resources Information Center

Park, Young C.

2014-01-01

The development of digital information transfer, storage and communication methods influences a significant effect on education. The assimilation of pervasive computing and communication technologies marks another great step forward, with Ubiquitous Learning (U-learning) emerging for next generation learners. In the evolutionary view the 5G (or…

Duplicate Abalone Egg Coat Proteins Bind Sperm Lysin Similarly, but Evolve Oppositely, Consistent with Molecular Mimicry at Fertilization

PubMed Central

Aagaard, Jan E.; Springer, Stevan A.; Soelberg, Scott D.; Swanson, Willie J.

2013-01-01

Sperm and egg proteins constitute a remarkable paradigm in evolutionary biology: despite their fundamental role in mediating fertilization (suggesting stasis), some of these molecules are among the most rapidly evolving ones known, and their divergence can lead to reproductive isolation. Because of strong selection to maintain function among interbreeding individuals, interacting fertilization proteins should also exhibit a strong signal of correlated divergence among closely related species. We use evidence of such molecular co-evolution to target biochemical studies of fertilization in North Pacific abalone (Haliotis spp.), a model system of reproductive protein evolution. We test the evolutionary rates (d N/d S) of abalone sperm lysin and two duplicated egg coat proteins (VERL and VEZP14), and find a signal of co-evolution specific to ZP-N, a putative sperm binding motif previously identified by homology modeling. Positively selected residues in VERL and VEZP14 occur on the same face of the structural model, suggesting a common mode of interaction with sperm lysin. We test this computational prediction biochemically, confirming that the ZP-N motif is sufficient to bind lysin and that the affinities of VERL and VEZP14 are comparable. However, we also find that on phylogenetic lineages where lysin and VERL evolve rapidly, VEZP14 evolves slowly, and vice versa. We describe a model of sexual conflict that can recreate this pattern of anti-correlated evolution by assuming that VEZP14 acts as a VERL mimic, reducing the intensity of sexual conflict and slowing the co-evolution of lysin and VERL. PMID:23408913

Evolutionary adaptations: theoretical and practical implications for visual ergonomics.

PubMed

Fostervold, Knut Inge; Watten, Reidulf G; Volden, Frode

2014-01-01

The literature discussing visual ergonomics often mention that human vision is adapted to light emitted by the sun. However, theoretical and practical implications of this viewpoint is seldom discussed or taken into account. The paper discusses some of the main theoretical implications of an evolutionary approach to visual ergonomics. Based on interactional theory and ideas from ecological psychology an evolutionary stress model is proposed as a theoretical framework for future research in ergonomics and human factors. The model stresses the importance of developing work environments that fits with our evolutionary adaptations. In accordance with evolutionary psychology, the environment of evolutionary adaptedness (EEA) and evolutionarily-novel environments (EN) are used as key concepts. Using work with visual display units (VDU) as an example, the paper discusses how this knowledge can be utilized in an ergonomic analysis of risk factors in the work environment. The paper emphasises the importance of incorporating evolutionary theory in the field of ergonomics. Further, the paper encourages scientific practices that further our understanding of any phenomena beyond the borders of traditional proximal explanations.

Efficiency of the neighbor-joining method in reconstructing deep and shallow evolutionary relationships in large phylogenies.

PubMed

Kumar, S; Gadagkar, S R

2000-12-01

The neighbor-joining (NJ) method is widely used in reconstructing large phylogenies because of its computational speed and the high accuracy in phylogenetic inference as revealed in computer simulation studies. However, most computer simulation studies have quantified the overall performance of the NJ method in terms of the percentage of branches inferred correctly or the percentage of replications in which the correct tree is recovered. We have examined other aspects of its performance, such as the relative efficiency in correctly reconstructing shallow (close to the external branches of the tree) and deep branches in large phylogenies; the contribution of zero-length branches to topological errors in the inferred trees; and the influence of increasing the tree size (number of sequences), evolutionary rate, and sequence length on the efficiency of the NJ method. Results show that the correct reconstruction of deep branches is no more difficult than that of shallower branches. The presence of zero-length branches in realized trees contributes significantly to the overall error observed in the NJ tree, especially in large phylogenies or slowly evolving genes. Furthermore, the tree size does not influence the efficiency of NJ in reconstructing shallow and deep branches in our simulation study, in which the evolutionary process is assumed to be homogeneous in all lineages.

Spore: Spawning Evolutionary Misconceptions?

NASA Astrophysics Data System (ADS)

Bean, Thomas E.; Sinatra, Gale M.; Schrader, P. G.

2010-10-01

The use of computer simulations as educational tools may afford the means to develop understanding of evolution as a natural, emergent, and decentralized process. However, special consideration of developmental constraints on learning may be necessary when using these technologies. Specifically, the essentialist (biological forms possess an immutable essence), teleological (assignment of purpose to living things and/or parts of living things that may not be purposeful), and intentionality (assumption that events are caused by an intelligent agent) biases may be reinforced through the use of computer simulations, rather than addressed with instruction. We examine the video game Spore for its depiction of evolutionary content and its potential to reinforce these cognitive biases. In particular, we discuss three pedagogical strategies to mitigate weaknesses of Spore and other computer simulations: directly targeting misconceptions through refutational approaches, targeting specific principles of scientific inquiry, and directly addressing issues related to models as cognitive tools.

Parallel Evolutionary Optimization for Neuromorphic Network Training

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schuman, Catherine D; Disney, Adam; Singh, Susheela

One of the key impediments to the success of current neuromorphic computing architectures is the issue of how best to program them. Evolutionary optimization (EO) is one promising programming technique; in particular, its wide applicability makes it especially attractive for neuromorphic architectures, which can have many different characteristics. In this paper, we explore different facets of EO on a spiking neuromorphic computing model called DANNA. We focus on the performance of EO in the design of our DANNA simulator, and on how to structure EO on both multicore and massively parallel computing systems. We evaluate how our parallel methods impactmore » the performance of EO on Titan, the U.S.'s largest open science supercomputer, and BOB, a Beowulf-style cluster of Raspberry Pi's. We also focus on how to improve the EO by evaluating commonality in higher performing neural networks, and present the result of a study that evaluates the EO performed by Titan.« less

Evolutionary relationships can be more important than abiotic conditions in predicting the outcome of plant-plant interactions

PubMed Central

Soliveres, Santiago; Torices, Rubén; Maestre, Fernando T.

2015-01-01

Positive and negative plant-plant interactions are major processes shaping plant communities. They are affected by environmental conditions and evolutionary relationships among the interacting plants. However, the generality of these factors as drivers of pairwise plant interactions and their combined effects remain virtually unknown. We conducted an observational study to assess how environmental conditions (altitude, temperature, irradiance and rainfall), the dispersal mechanism of beneficiary species and evolutionary relationships affected the co-occurrence of pairwise interactions in 11 Stipa tenacissima steppes located along an environmental gradient in Spain. We studied 197 pairwise plant-plant interactions involving the two major nurse plants (the resprouting shrub Quercus coccifera and the tussock grass S. tenacissima) found in these communities. The relative importance of the studied factors varied with the nurse species considered. None of the factors studied were good predictors of the co-ocurrence between S. tenacissima and its neighbours. However, both the dispersal mechanism of the beneficiary species and the phylogenetic distance between interacting species were crucial factors affecting the co-occurrence between Q. coccifera and its neighbours, while climatic conditions (irradiance) played a secondary role. Values of phylogenetic distance between 207-272.8 Myr led to competition, while values outside this range or fleshy-fruitness in the beneficiary species led to positive interactions. The low importance of environmental conditions as a general driver of pairwise interactions was caused by the species-specific response to changes in either rainfall or radiation. This result suggests that factors other than climatic conditions must be included in theoretical models aimed to generally predict the outcome of plant-plant interactions. Our study helps to improve current theory on plant-plant interactions and to understand how these interactions can respond to expected modifications in species composition and climate associated to ongoing global environmental change. PMID:25914426

A crucial step toward realism: responses to climate change from an evolving metacommunity perspective.

PubMed

Urban, Mark C; De Meester, Luc; Vellend, Mark; Stoks, Robby; Vanoverbeke, Joost

2012-02-01

We need to understand joint ecological and evolutionary responses to climate change to predict future threats to biological diversity. The 'evolving metacommunity' framework emphasizes that interactions between ecological and evolutionary mechanisms at both local and regional scales will drive community dynamics during climate change. Theory suggests that ecological and evolutionary dynamics often interact to produce outcomes different from those predicted based on either mechanism alone. We highlight two of these dynamics: (i) species interactions prevent adaptation of nonresident species to new niches and (ii) resident species adapt to changing climates and thereby prevent colonization by nonresident species. The rate of environmental change, level of genetic variation, source-sink structure, and dispersal rates mediate between these potential outcomes. Future models should evaluate multiple species, species interactions other than competition, and multiple traits. Future experiments should manipulate factors such as genetic variation and dispersal to determine their joint effects on responses to climate change. Currently, we know much more about how climates will change across the globe than about how species will respond to these changes despite the profound effects these changes will have on global biological diversity. Integrating evolving metacommunity perspectives into climate change biology should produce more accurate predictions about future changes to species distributions and extinction threats.

A crucial step toward realism: responses to climate change from an evolving metacommunity perspective

PubMed Central

Urban, Mark C; De Meester, Luc; Vellend, Mark; Stoks, Robby; Vanoverbeke, Joost

2012-01-01

We need to understand joint ecological and evolutionary responses to climate change to predict future threats to biological diversity. The ‘evolving metacommunity’ framework emphasizes that interactions between ecological and evolutionary mechanisms at both local and regional scales will drive community dynamics during climate change. Theory suggests that ecological and evolutionary dynamics often interact to produce outcomes different from those predicted based on either mechanism alone. We highlight two of these dynamics: (i) species interactions prevent adaptation of nonresident species to new niches and (ii) resident species adapt to changing climates and thereby prevent colonization by nonresident species. The rate of environmental change, level of genetic variation, source-sink structure, and dispersal rates mediate between these potential outcomes. Future models should evaluate multiple species, species interactions other than competition, and multiple traits. Future experiments should manipulate factors such as genetic variation and dispersal to determine their joint effects on responses to climate change. Currently, we know much more about how climates will change across the globe than about how species will respond to these changes despite the profound effects these changes will have on global biological diversity. Integrating evolving metacommunity perspectives into climate change biology should produce more accurate predictions about future changes to species distributions and extinction threats. PMID:25568038

An Evolutionary Complex Systems Decision-Support Tool for the Management of Operations

NASA Astrophysics Data System (ADS)

Baldwin, J. S.; Allen, P. M.; Ridgway, K.

2011-12-01

This research aimed to add both to the development of complex systems thinking in the subject area of Operations and Production Management and to the limited number of applications of computational models and simulations from the science of complex systems. The latter potentially offer helpful decision-support tools for operations and production managers. A mechanical engineering firm was used as a case study where a combined qualitative and quantitative methodological approach was employed to extract the required data from four senior managers. Company performance measures as well as firm technologies, practices and policies, and their relation and interaction with one another, were elicited. The data were subjected to an evolutionary complex systems model resulting in a series of simulations. The findings included both reassuring and some unexpected results. The simulation based on the CEO's opinions led the most cohesive and synergistic collection of practices describing the firm, closely followed by the Marketing and R&D Managers. The Manufacturing Manager's responses led to the most extreme evolutionary trajectory where the integrity of the entire firm came into question particularly when considering how employees were utilised. By drawing directly from the opinions and views of managers rather than from logical 'if-then' rules and averaged mathematical representations of agents that characterise agent-based and other self-organisational models, this work builds on previous applications by capturing a micro-level description of diversity and a learning effect that has been problematical not only in terms of theory but also in application. This approach can be used as a decision-support tool for operations and other managers providing a forum with which to explore a) the strengths, weaknesses and consequences of different decision-making capacities within the firm; b) the introduction of new manufacturing technologies, practices and policies; and, c) the different evolutionary trajectories that a firm can take.

Architecture of an Antagonistic Tree/Fungus Network: The Asymmetric Influence of Past Evolutionary History

PubMed Central

Vacher, Corinne; Piou, Dominique; Desprez-Loustau, Marie-Laure

2008-01-01

Background Compartmentalization and nestedness are common patterns in ecological networks. The aim of this study was to elucidate some of the processes shaping these patterns in a well resolved network of host/pathogen interactions. Methology/Principal Findings Based on a long-term (1972–2005) survey of forest health at the regional scale (all French forests; 15 million ha), we uncovered an almost fully connected network of 51 tree taxa and 157 parasitic fungal species. Our analyses revealed that the compartmentalization of the network maps out the ancient evolutionary history of seed plants, but not the ancient evolutionary history of fungal species. The very early divergence of the major fungal phyla may account for this asymmetric influence of past evolutionary history. Unlike compartmentalization, nestedness did not reflect any consistent phylogenetic signal. Instead, it seemed to reflect the ecological features of the current species, such as the relative abundance of tree species and the life-history strategies of fungal pathogens. We discussed how the evolution of host range in fungal species may account for the observed nested patterns. Conclusion/Significance Overall, our analyses emphasized how the current complexity of ecological networks results from the diversification of the species and their interactions over evolutionary times. They confirmed that the current architecture of ecological networks is not only dependant on recent ecological processes. PMID:18320058

The Schistosoma mansoni phylome: using evolutionary genomics to gain insight into a parasite's biology.

PubMed

Silva, Larissa Lopes; Marcet-Houben, Marina; Nahum, Laila Alves; Zerlotini, Adhemar; Gabaldón, Toni; Oliveira, Guilherme

2012-11-13

Schistosoma mansoni is one of the causative agents of schistosomiasis, a neglected tropical disease that affects about 237 million people worldwide. Despite recent efforts, we still lack a general understanding of the relevant host-parasite interactions, and the possible treatments are limited by the emergence of resistant strains and the absence of a vaccine. The S. mansoni genome was completely sequenced and still under continuous annotation. Nevertheless, more than 45% of the encoded proteins remain without experimental characterization or even functional prediction. To improve our knowledge regarding the biology of this parasite, we conducted a proteome-wide evolutionary analysis to provide a broad view of the S. mansoni's proteome evolution and to improve its functional annotation. Using a phylogenomic approach, we reconstructed the S. mansoni phylome, which comprises the evolutionary histories of all parasite proteins and their homologs across 12 other organisms. The analysis of a total of 7,964 phylogenies allowed a deeper understanding of genomic complexity and evolutionary adaptations to a parasitic lifestyle. In particular, the identification of lineage-specific gene duplications pointed to the diversification of several protein families that are relevant for host-parasite interaction, including proteases, tetraspanins, fucosyltransferases, venom allergen-like proteins, and tegumental-allergen-like proteins. In addition to the evolutionary knowledge, the phylome data enabled us to automatically re-annotate 3,451 proteins through a phylogenetic-based approach rather than solely sequence similarity searches. To allow further exploitation of this valuable data, all information has been made available at PhylomeDB (http://www.phylomedb.org). In this study, we used an evolutionary approach to assess S. mansoni parasite biology, improve genome/proteome functional annotation, and provide insights into host-parasite interactions. Taking advantage of a proteome-wide perspective rather than focusing on individual proteins, we identified that this parasite has experienced specific gene duplication events, particularly affecting genes that are potentially related to the parasitic lifestyle. These innovations may be related to the mechanisms that protect S. mansoni against host immune responses being important adaptations for the parasite survival in a potentially hostile environment. Continuing this work, a comparative analysis involving genomic, transcriptomic, and proteomic data from other helminth parasites, other parasites, and vectors will supply more information regarding parasite's biology as well as host-parasite interactions.

Inquiry-Based Learning of Molecular Phylogenetics

ERIC Educational Resources Information Center

Campo, Daniel; Garcia-Vazquez, Eva

2008-01-01

Reconstructing phylogenies from nucleotide sequences is a challenge for students because it strongly depends on evolutionary models and computer tools that are frequently updated. We present here an inquiry-based course aimed at learning how to trace a phylogeny based on sequences existing in public databases. Computer tools are freely available…

Revealing evolutionary pathways by fitness landscape reconstruction.

PubMed

Kogenaru, Manjunatha; de Vos, Marjon G J; Tans, Sander J

2009-01-01

The concept of epistasis has since long been used to denote non-additive fitness effects of genetic changes and has played a central role in understanding the evolution of biological systems. Owing to an array of novel experimental methodologies, it has become possible to experimentally determine epistatic interactions as well as more elaborate genotype-fitness maps. These data have opened up the investigation of a host of long-standing questions in evolutionary biology, such as the ruggedness of fitness landscapes and the accessibility of mutational trajectories, the evolution of sex, and the origin of robustness and modularity. Here we review this recent and timely marriage between systems biology and evolutionary biology, which holds the promise to understand evolutionary dynamics in a more mechanistic and predictive manner.

Dynamic multipopulation and density dependent evolutionary games related to replicator dynamics. A metasimplex concept.

PubMed

Argasinski, Krzysztof

2006-07-01

This paper contains the basic extensions of classical evolutionary games (multipopulation and density dependent models). It is shown that classical bimatrix approach is inconsistent with other approaches because it does not depend on proportion between populations. The main conclusion is that interspecific proportion parameter is important and must be considered in multipopulation models. The paper provides a synthesis of both extensions (a metasimplex concept) which solves the problem intrinsic in the bimatrix model. It allows us to model interactions among any number of subpopulations including density dependence effects. We prove that all modern approaches to evolutionary games are closely related. All evolutionary models (except classical bimatrix approaches) can be reduced to a single population general model by a simple change of variables. Differences between classic bimatrix evolutionary games and a new model which is dependent on interspecific proportion are shown by examples.

A Philosophical Perspective on Evolutionary Systems Biology

PubMed Central

Soyer, Orkun S.; Siegal, Mark L.

2015-01-01

Evolutionary systems biology (ESB) is an emerging hybrid approach that integrates methods, models, and data from evolutionary and systems biology. Drawing on themes that arose at a cross-disciplinary meeting on ESB in 2013, we discuss in detail some of the explanatory friction that arises in the interaction between evolutionary and systems biology. These tensions appear because of different modeling approaches, diverse explanatory aims and strategies, and divergent views about the scope of the evolutionary synthesis. We locate these discussions in the context of long-running philosophical deliberations on explanation, modeling, and theoretical synthesis. We show how many of the issues central to ESB’s progress can be understood as general philosophical problems. The benefits of addressing these philosophical issues feed back into philosophy too, because ESB provides excellent examples of scientific practice for the development of philosophy of science and philosophy of biology. PMID:26085823

Pattern Formation on Networks: from Localised Activity to Turing Patterns

PubMed Central

McCullen, Nick; Wagenknecht, Thomas

2016-01-01

Networks of interactions between competing species are used to model many complex systems, such as in genetics, evolutionary biology or sociology and knowledge of the patterns of activity they can exhibit is important for understanding their behaviour. The emergence of patterns on complex networks with reaction-diffusion dynamics is studied here, where node dynamics interact via diffusion via the network edges. Through the application of a generalisation of dynamical systems analysis this work reveals a fundamental connection between small-scale modes of activity on networks and localised pattern formation seen throughout science, such as solitons, breathers and localised buckling. The connection between solutions with a single and small numbers of activated nodes and the fully developed system-scale patterns are investigated computationally using numerical continuation methods. These techniques are also used to help reveal a much larger portion of of the full number of solutions that exist in the system at different parameter values. The importance of network structure is also highlighted, with a key role being played by nodes with a certain so-called optimal degree, on which the interaction between the reaction kinetics and the network structure organise the behaviour of the system. PMID:27273339

Epigenetic game theory: How to compute the epigenetic control of maternal-to-zygotic transition

NASA Astrophysics Data System (ADS)

Wang, Qian; Gosik, Kirk; Xing, Sujuan; Jiang, Libo; Sun, Lidan; Chinchilli, Vernon M.; Wu, Rongling

2017-03-01

Epigenetic reprogramming is thought to play a critical role in maintaining the normal development of embryos. How the methylation state of paternal and maternal genomes regulates embryogenesis depends on the interaction and coordination of the gametes of two sexes. While there is abundant research in exploring the epigenetic interactions of sperms and oocytes, a knowledge gap exists in the mechanistic quantitation of these interactions and their impact on embryo development. This review aims at formulating a modeling framework to address this gap through the integration and synthesis of evolutionary game theory and the latest discoveries of the epigenetic control of embryo development by next-generation sequencing. This framework, named epigenetic game theory or epiGame, views embryogenesis as an ecological system in which two highly distinct and specialized gametes coordinate through either cooperation or competition, or both, to maximize the fitness of embryos under Darwinian selection. By implementing a system of ordinary differential equations, epiGame quantifies the pattern and relative magnitude of the methylation effects on embryogenesis by the mechanisms of cooperation and competition. epiGame may gain new insight into reproductive biology and can be potentially applied to design personalized medicines for genetic disorder intervention.

Building toy models of proteins using coevolutionary information

NASA Astrophysics Data System (ADS)

Cheng, Ryan; Raghunathan, Mohit; Onuchic, Jose

2015-03-01

Recent developments in global statistical methodologies have advanced the analysis of large collections of protein sequences for coevolutionary information. Coevolution between amino acids in a protein arises from compensatory mutations that are needed to maintain the stability or function of a protein over the course of evolution. This gives rise to quantifiable correlations between amino acid positions within the multiple sequence alignment of a protein family. Here, we use Direct Coupling Analysis (DCA) to infer a Potts model Hamiltonian governing the correlated mutations in a protein family to obtain the sequence-dependent interaction energies of a toy protein model. We demonstrate that this methodology predicts residue-residue interaction energies that are consistent with experimental mutational changes in protein stabilities as well as other computational methodologies. Furthermore, we demonstrate with several examples that DCA could be used to construct a structure-based model that quantitatively agrees with experimental data on folding mechanisms. This work serves as a potential framework for generating models of proteins that are enriched by evolutionary data that can potentially be used to engineer key functional motions and interactions in protein systems. This research has been supported by the NSF INSPIRE award MCB-1241332 and by the CTBP sponsored by the NSF (Grant PHY-1427654).

International Conference on Artificial Immune Systems (1st) ICARIS 2002, held on 9, 10, and 11 September 2002

DTIC Science & Technology

2002-03-07

Michalewicz, Eds., Evolutionary Computation 1: Basic Algorithms and Operators, Institute of Physics, Bristol (UK), 2000. [3] David A. Van Veldhuizen ...2000. [4] Carlos A. Coello Coello, David A. Van Veldhuizen , and Gary B. Lamont, Evolutionary Algorithms for Solving Multi-Objective Problems, Kluwer...Academic Publishers, 233 Spring St., New York, NY 10013, 2002. [5] David A. Van Veldhuizen , Multiobjective Evolution- ary Algorithms: Classifications

Using parallel evolutionary development for a biologically-inspired computer vision system for mobile robots.

PubMed

Wright, Cameron H G; Barrett, Steven F; Pack, Daniel J

2005-01-01

We describe a new approach to attacking the problem of robust computer vision for mobile robots. The overall strategy is to mimic the biological evolution of animal vision systems. Our basic imaging sensor is based upon the eye of the common house fly, Musca domestica. The computational algorithms are a mix of traditional image processing, subspace techniques, and multilayer neural networks.

Symbioses: a key driver of insect physiological processes, ecological interactions, evolutionary diversification, and impacts on humans

Treesearch

K.D. Klepzig; A.S. Adams; J. Handelsman; K.F. Raffa

2009-01-01

Symbiosis is receiving increased attention among all aspects of biology because of the unifying themes it helps construct across ecological,evolutionary, developmental, semiochemical, and pest management theory. Insects show a vast array of symbiotic relationships with a wide diversity of microorganisms. These relationships may confer a variety of benefits to the host...

Symbioses: A key driver of insect physiological processes, ecological interactions, evolutionary diversification, and impacts on humans

Treesearch

Kier Klepzig; A.S. Adams; J Handelsman; K.F. Raffa

2009-01-01

Symbiosis is receiving increased attention among all aspects of biology because of the unifying themes it helps construct across ecological, evolutionary, developmental, semiochemical, and pest management theory. Insects show a vast array of symbiotic relationships with a wide diversity of microorganisms. These relationships may confer a variety of benefits to the host...

Chaos and unpredictability in evolution.

PubMed

Doebeli, Michael; Ispolatov, Iaroslav

2014-05-01

The possibility of complicated dynamic behavior driven by nonlinear feedbacks in dynamical systems has revolutionized science in the latter part of the last century. Yet despite examples of complicated frequency dynamics, the possibility of long-term evolutionary chaos is rarely considered. The concept of "survival of the fittest" is central to much evolutionary thinking and embodies a perspective of evolution as a directional optimization process exhibiting simple, predictable dynamics. This perspective is adequate for simple scenarios, when frequency-independent selection acts on scalar phenotypes. However, in most organisms many phenotypic properties combine in complicated ways to determine ecological interactions, and hence frequency-dependent selection. Therefore, it is natural to consider models for evolutionary dynamics generated by frequency-dependent selection acting simultaneously on many different phenotypes. Here we show that complicated, chaotic dynamics of long-term evolutionary trajectories in phenotype space is very common in a large class of such models when the dimension of phenotype space is large, and when there are selective interactions between the phenotypic components. Our results suggest that the perspective of evolution as a process with simple, predictable dynamics covers only a small fragment of long-term evolution. © 2014 The Author(s). Evolution © 2014 The Society for the Study of Evolution.

Epidemiological, evolutionary and co-evolutionary implications of context-dependent parasitism

PubMed Central

Vale, Pedro F.; Wilson, Alastair J.; Best, Alex; Boots, Mike; Little, Tom J.

2013-01-01

Victims of infection are expected to suffer increasingly as parasite population growth increases. Yet, under some conditions, faster growing parasites do not appear to cause more damage and infections can be quite tolerable. We studied these conditions by assessing how the relationship between parasite population growth and host health is sensitive to environmental variation. In experimental infections of the crustacean Daphnia magna and its bacterial parasite Pasteuria ramosa we show how easily an interaction can shift from a severe interaction, i.e. when host fitness declines substantially with each unit of parasite growth, to a tolerable relationship by changing only simple environmental variables: temperature and food availability. We explored the evolutionary and epidemiological implications of such a shift by modelling pathogen evolution and disease spread under different levels of infection severity, and find that environmental shifts that promote tolerance ultimately result in populations harbouring more parasitized individuals. We also find that the opportunity for selection, as indicated by the variance around traits, varied considerably with the environmental treatment. Thus our results suggest two mechanisms that could underlie co-evolutionary hot- and coldspots: spatial variation in tolerance and spatial variation in the opportunity for selection. PMID:21460572

Symbiosis in eukaryotic evolution.

PubMed

López-García, Purificación; Eme, Laura; Moreira, David

2017-12-07

Fifty years ago, Lynn Margulis, inspiring in early twentieth-century ideas that put forward a symbiotic origin for some eukaryotic organelles, proposed a unified theory for the origin of the eukaryotic cell based on symbiosis as evolutionary mechanism. Margulis was profoundly aware of the importance of symbiosis in the natural microbial world and anticipated the evolutionary significance that integrated cooperative interactions might have as mechanism to increase cellular complexity. Today, we have started fully appreciating the vast extent of microbial diversity and the importance of syntrophic metabolic cooperation in natural ecosystems, especially in sediments and microbial mats. Also, not only the symbiogenetic origin of mitochondria and chloroplasts has been clearly demonstrated, but improvement in phylogenomic methods combined with recent discoveries of archaeal lineages more closely related to eukaryotes further support the symbiogenetic origin of the eukaryotic cell. Margulis left us in legacy the idea of 'eukaryogenesis by symbiogenesis'. Although this has been largely verified, when, where, and specifically how eukaryotic cells evolved are yet unclear. Here, we shortly review current knowledge about symbiotic interactions in the microbial world and their evolutionary impact, the status of eukaryogenetic models and the current challenges and perspectives ahead to reconstruct the evolutionary path to eukaryotes. Copyright © 2017 Elsevier Ltd. All rights reserved.

Uncertainty about social interactions leads to the evolution of social heuristics.

PubMed

van den Berg, Pieter; Wenseleers, Tom

2018-05-31

Individuals face many types of social interactions throughout their lives, but they often cannot perfectly assess what the consequences of their actions will be. Although it is known that unpredictable environments can profoundly affect the evolutionary process, it remains unclear how uncertainty about the nature of social interactions shapes the evolution of social behaviour. Here, we present an evolutionary simulation model, showing that even intermediate uncertainty leads to the evolution of simple cooperation strategies that disregard information about the social interaction ('social heuristics'). Moreover, our results show that the evolution of social heuristics can greatly affect cooperation levels, nearly doubling cooperation rates in our simulations. These results provide new insight into why social behaviour, including cooperation in humans, is often observed to be seemingly suboptimal. More generally, our results show that social behaviour that seems maladaptive when considered in isolation may actually be well-adapted to a heterogeneous and uncertain world.

Automated Antenna Design with Evolutionary Algorithms

NASA Technical Reports Server (NTRS)

Hornby, Gregory S.; Globus, Al; Linden, Derek S.; Lohn, Jason D.

2006-01-01

Current methods of designing and optimizing antennas by hand are time and labor intensive, and limit complexity. Evolutionary design techniques can overcome these limitations by searching the design space and automatically finding effective solutions. In recent years, evolutionary algorithms have shown great promise in finding practical solutions in large, poorly understood design spaces. In particular, spacecraft antenna design has proven tractable to evolutionary design techniques. Researchers have been investigating evolutionary antenna design and optimization since the early 1990s, and the field has grown in recent years as computer speed has increased and electromagnetic simulators have improved. Two requirements-compliant antennas, one for ST5 and another for TDRS-C, have been automatically designed by evolutionary algorithms. The ST5 antenna is slated to fly this year, and a TDRS-C phased array element has been fabricated and tested. Such automated evolutionary design is enabled by medium-to-high quality simulators and fast modern computers to evaluate computer-generated designs. Evolutionary algorithms automate cut-and-try engineering, substituting automated search though millions of potential designs for intelligent search by engineers through a much smaller number of designs. For evolutionary design, the engineer chooses the evolutionary technique, parameters and the basic form of the antenna, e.g., single wire for ST5 and crossed-element Yagi for TDRS-C. Evolutionary algorithms then search for optimal configurations in the space defined by the engineer. NASA's Space Technology 5 (ST5) mission will launch three small spacecraft to test innovative concepts and technologies. Advanced evolutionary algorithms were used to automatically design antennas for ST5. The combination of wide beamwidth for a circularly-polarized wave and wide impedance bandwidth made for a challenging antenna design problem. From past experience in designing wire antennas, we chose to constrain the evolutionary design to a monopole wire antenna. The results of the runs produced requirements-compliant antennas that were subsequently fabricated and tested. The evolved antenna has a number of advantages with regard to power consumption, fabrication time and complexity, and performance. Lower power requirements result from achieving high gain across a wider range of elevation angles, thus allowing a broader range of angles over which maximum data throughput can be achieved. Since the evolved antenna does not require a phasing circuit, less design and fabrication work is required. In terms of overall work, the evolved antenna required approximately three person-months to design and fabricate whereas the conventional antenna required about five. Furthermore, when the mission was modified and new orbital parameters selected, a redesign of the antenna to new requirements was required. The evolutionary system was rapidly modified and a new antenna evolved in a few weeks. The evolved antenna was shown to be compliant to the ST5 mission requirements. It has an unusual organic looking structure, one that expert antenna designers would not likely produce. This antenna has been tested, baselined and is scheduled to fly this year. In addition to the ST5 antenna, our laboratory has evolved an S-band phased array antenna element design that meets the requirements for NASA's TDRS-C communications satellite scheduled for launch early next decade. A combination of fairly broad bandwidth, high efficiency and circular polarization at high gain made for another challenging design problem. We chose to constrain the evolutionary design to a crossed-element Yagi antenna. The specification called for two types of elements, one for receive only and one for transmit/receive. We were able to evolve a single element design that meets both specifications thereby simplifying the antenna and reducing testing and integration costs. The highest performance antenna found using a getic algorithm and stochastic hill-climbing has been fabricated and tested. Laboratory results correspond well with simulation. Aerospace component design is an expensive and important step in space development. Evolutionary design can make a significant contribution wherever sufficiently fast, accurate and capable software simulators are available. We have demonstrated successful real-world design in the spacecraft antenna domain; and there is good reason to believe that these results could be replicated in other design spaces.

Hybrid Genetic Agorithms and Line Search Method for Industrial Production Planning with Non-Linear Fitness Function

NASA Astrophysics Data System (ADS)

Vasant, Pandian; Barsoum, Nader

2008-10-01

Many engineering, science, information technology and management optimization problems can be considered as non linear programming real world problems where the all or some of the parameters and variables involved are uncertain in nature. These can only be quantified using intelligent computational techniques such as evolutionary computation and fuzzy logic. The main objective of this research paper is to solve non linear fuzzy optimization problem where the technological coefficient in the constraints involved are fuzzy numbers which was represented by logistic membership functions by using hybrid evolutionary optimization approach. To explore the applicability of the present study a numerical example is considered to determine the production planning for the decision variables and profit of the company.

Hardware platforms for MEMS gyroscope tuning based on evolutionary computation using open-loop and closed -loop frequency response

NASA Technical Reports Server (NTRS)

Keymeulen, Didier; Ferguson, Michael I.; Fink, Wolfgang; Oks, Boris; Peay, Chris; Terrile, Richard; Cheng, Yen; Kim, Dennis; MacDonald, Eric; Foor, David

2005-01-01

We propose a tuning method for MEMS gyroscopes based on evolutionary computation to efficiently increase the sensitivity of MEMS gyroscopes through tuning. The tuning method was tested for the second generation JPL/Boeing Post-resonator MEMS gyroscope using the measurement of the frequency response of the MEMS device in open-loop operation. We also report on the development of a hardware platform for integrated tuning and closed loop operation of MEMS gyroscopes. The control of this device is implemented through a digital design on a Field Programmable Gate Array (FPGA). The hardware platform easily transitions to an embedded solution that allows for the miniaturization of the system to a single chip.

Understanding how animal groups achieve coordinated movement.

PubMed

Herbert-Read, J E

2016-10-01

Moving animal groups display remarkable feats of coordination. This coordination is largely achieved when individuals adjust their movement in response to their neighbours' movements and positions. Recent advancements in automated tracking technologies, including computer vision and GPS, now allow researchers to gather large amounts of data on the movements and positions of individuals in groups. Furthermore, analytical techniques from fields such as statistical physics now allow us to identify the precise interaction rules used by animals on the move. These interaction rules differ not only between species, but also between individuals in the same group. These differences have wide-ranging implications, affecting how groups make collective decisions and driving the evolution of collective motion. Here, I describe how trajectory data can be used to infer how animals interact in moving groups. I give examples of the similarities and differences in the spatial and directional organisations of animal groups between species, and discuss the rules that animals use to achieve this organisation. I then explore how groups of the same species can exhibit different structures, and ask whether this results from individuals adapting their interaction rules. I then examine how the interaction rules between individuals in the same groups can also differ, and discuss how this can affect ecological and evolutionary processes. Finally, I suggest areas of future research. © 2016. Published by The Company of Biologists Ltd.

Integration of Structural Dynamics and Molecular Evolution via Protein Interaction Networks: A New Era in Genomic Medicine

PubMed Central

Kumar, Avishek; Butler, Brandon M.; Kumar, Sudhir; Ozkan, S. Banu

2016-01-01

Summary Sequencing technologies are revealing many new non-synonymous single nucleotide variants (nsSNVs) in each personal exome. To assess their functional impacts, comparative genomics is frequently employed to predict if they are benign or not. However, evolutionary analysis alone is insufficient, because it misdiagnoses many disease-associated nsSNVs, such as those at positions involved in protein interfaces, and because evolutionary predictions do not provide mechanistic insights into functional change or loss. Structural analyses can aid in overcoming both of these problems by incorporating conformational dynamics and allostery in nSNV diagnosis. Finally, protein-protein interaction networks using systems-level methodologies shed light onto disease etiology and pathogenesis. Bridging these network approaches with structurally resolved protein interactions and dynamics will advance genomic medicine. PMID:26684487

Games of life and death: antibiotic resistance and production through the lens of evolutionary game theory.

PubMed

Conlin, Peter L; Chandler, Josephine R; Kerr, Benjamin

2014-10-01

In this review, we demonstrate how game theory can be a useful first step in modeling and understanding interactions among bacteria that produce and resist antibiotics. We introduce the basic features of evolutionary game theory and explore model microbial systems that correspond to some classical games. Each game discussed defines a different category of social interaction with different resulting population dynamics (exclusion, coexistence, bistability, cycling). We then explore how the framework can be extended to incorporate some of the complexity of natural microbial communities. Overall, the game theoretical perspective helps to guide our expectations about the evolution of some forms of antibiotic resistance and production because it makes clear the precise nature of social interaction in this context. Copyright © 2014 Elsevier Ltd. All rights reserved.

Evolutionary multiobjective design of a flexible caudal fin for robotic fish.

PubMed

Clark, Anthony J; Tan, Xiaobo; McKinley, Philip K

2015-11-25

Robotic fish accomplish swimming by deforming their bodies or other fin-like appendages. As an emerging class of embedded computing system, robotic fish are anticipated to play an important role in environmental monitoring, inspection of underwater structures, tracking of hazardous wastes and oil spills, and the study of live fish behaviors. While integration of flexible materials (into the fins and/or body) holds the promise of improved swimming performance (in terms of both speed and maneuverability) for these robots, such components also introduce significant design challenges due to the complex material mechanics and hydrodynamic interactions. The problem is further exacerbated by the need for the robots to meet multiple objectives (e.g., both speed and energy efficiency). In this paper, we propose an evolutionary multiobjective optimization approach to the design and control of a robotic fish with a flexible caudal fin. Specifically, we use the NSGA-II algorithm to investigate morphological and control parameter values that optimize swimming speed and power usage. Several evolved fin designs are validated experimentally with a small robotic fish, where fins of different stiffness values and sizes are printed with a multi-material 3D printer. Experimental results confirm the effectiveness of the proposed design approach in balancing the two competing objectives.

The role of internal duplication in the evolution of multi-domain proteins.

PubMed

Nacher, J C; Hayashida, M; Akutsu, T

2010-08-01

Many proteins consist of several structural domains. These multi-domain proteins have likely been generated by selective genome growth dynamics during evolution to perform new functions as well as to create structures that fold on a biologically feasible time scale. Domain units frequently evolved through a variety of genetic shuffling mechanisms. Here we examine the protein domain statistics of more than 1000 organisms including eukaryotic, archaeal and bacterial species. The analysis extends earlier findings on asymmetric statistical laws for proteome to a wider variety of species. While proteins are composed of a wide range of domains, displaying a power-law decay, the computation of domain families for each protein reveals an exponential distribution, characterizing a protein universe composed of a thin number of unique families. Structural studies in proteomics have shown that domain repeats, or internal duplicated domains, represent a small but significant fraction of genome. In spite of its importance, this observation has been largely overlooked until recently. We model the evolutionary dynamics of proteome and demonstrate that these distinct distributions are in fact rooted in an internal duplication mechanism. This process generates the contemporary protein structural domain universe, determines its reduced thickness, and tames its growth. These findings have important implications, ranging from protein interaction network modeling to evolutionary studies based on fundamental mechanisms governing genome expansion.

An evolutionary switch in ND2 enables Src kinase regulation of NMDA receptors

NASA Astrophysics Data System (ADS)

Scanlon, David P.; Bah, Alaji; Krzeminski, Mickaël; Zhang, Wenbo; Leduc-Pessah, Heather L.; Dong, Yi Na; Forman-Kay, Julie D.; Salter, Michael W.

2017-05-01

The non-receptor tyrosine kinase Src is a key signalling hub for upregulating the function of N-methyl D-aspartate receptors (NMDARs). Src is anchored within the NMDAR complex via NADH dehydrogenase subunit 2 (ND2), a mitochondrially encoded adaptor protein. The interacting regions between Src and ND2 have been broadly identified, but the interaction between ND2 and the NMDAR has remained elusive. Here we generate a homology model of ND2 and dock it onto the NMDAR via the transmembrane domain of GluN1. This interaction is enabled by the evolutionary loss of three helices in bilaterian ND2 proteins compared to their ancestral homologues. We experimentally validate our model and demonstrate that blocking this interaction with an ND2 fragment identified in our experimental studies prevents Src-mediated upregulation of NMDAR currents in neurons. Our findings establish the mode of interaction between an NMDAR accessory protein with one of the core subunits of the receptor.

Predicting protein-protein interactions on a proteome scale by matching evolutionary and structural similarities at interfaces using PRISM.

PubMed

Tuncbag, Nurcan; Gursoy, Attila; Nussinov, Ruth; Keskin, Ozlem

2011-08-11

Prediction of protein-protein interactions at the structural level on the proteome scale is important because it allows prediction of protein function, helps drug discovery and takes steps toward genome-wide structural systems biology. We provide a protocol (termed PRISM, protein interactions by structural matching) for large-scale prediction of protein-protein interactions and assembly of protein complex structures. The method consists of two components: rigid-body structural comparisons of target proteins to known template protein-protein interfaces and flexible refinement using a docking energy function. The PRISM rationale follows our observation that globally different protein structures can interact via similar architectural motifs. PRISM predicts binding residues by using structural similarity and evolutionary conservation of putative binding residue 'hot spots'. Ultimately, PRISM could help to construct cellular pathways and functional, proteome-scale annotation. PRISM is implemented in Python and runs in a UNIX environment. The program accepts Protein Data Bank-formatted protein structures and is available at http://prism.ccbb.ku.edu.tr/prism_protocol/.

Genome-driven evolutionary game theory helps understand the rise of metabolic interdependencies in microbial communities.

PubMed

Zomorrodi, Ali R; Segrè, Daniel

2017-11-16

Metabolite exchanges in microbial communities give rise to ecological interactions that govern ecosystem diversity and stability. It is unclear, however, how the rise of these interactions varies across metabolites and organisms. Here we address this question by integrating genome-scale models of metabolism with evolutionary game theory. Specifically, we use microbial fitness values estimated by metabolic models to infer evolutionarily stable interactions in multi-species microbial "games". We first validate our approach using a well-characterized yeast cheater-cooperator system. We next perform over 80,000 in silico experiments to infer how metabolic interdependencies mediated by amino acid leakage in Escherichia coli vary across 189 amino acid pairs. While most pairs display shared patterns of inter-species interactions, multiple deviations are caused by pleiotropy and epistasis in metabolism. Furthermore, simulated invasion experiments reveal possible paths to obligate cross-feeding. Our study provides genomically driven insight into the rise of ecological interactions, with implications for microbiome research and synthetic ecology.

The REH theory of protein and nucleic acid divergence - A retrospective update. [Random Evolutionary Hits

NASA Technical Reports Server (NTRS)

Holmquist, R.

1978-01-01

The random evolutionary hits (REH) theory of evolutionary divergence, originally proposed in 1972, is restated with attention to certain aspects of the theory that have caused confusion. The theory assumes that natural selection and stochastic processes interact and that natural selection restricts those codon sites which may fix mutations. The predicted total number of fixed nucleotide replacements agrees with data for cytochrome c, a-hemoglobin, beta-hemoglobin, and myoglobin. The restatement analyzes the magnitude of possible sources of errors and simplifies calculational methodology by supplying polynomial expressions to replace tables and graphs.

Evolution of Collective Behaviour in an Artificial World Using Linguistic Fuzzy Rule-Based Systems

PubMed Central

Lebar Bajec, Iztok

2017-01-01

Collective behaviour is a fascinating and easily observable phenomenon, attractive to a wide range of researchers. In biology, computational models have been extensively used to investigate various properties of collective behaviour, such as: transfer of information across the group, benefits of grouping (defence against predation, foraging), group decision-making process, and group behaviour types. The question ‘why,’ however remains largely unanswered. Here the interest goes into which pressures led to the evolution of such behaviour, and evolutionary computational models have already been used to test various biological hypotheses. Most of these models use genetic algorithms to tune the parameters of previously presented non-evolutionary models, but very few attempt to evolve collective behaviour from scratch. Of these last, the successful attempts display clumping or swarming behaviour. Empirical evidence suggests that in fish schools there exist three classes of behaviour; swarming, milling and polarized. In this paper we present a novel, artificial life-like evolutionary model, where individual agents are governed by linguistic fuzzy rule-based systems, which is capable of evolving all three classes of behaviour. PMID:28045964

An improved shuffled frog leaping algorithm based evolutionary framework for currency exchange rate prediction

NASA Astrophysics Data System (ADS)

Dash, Rajashree

2017-11-01

Forecasting purchasing power of one currency with respect to another currency is always an interesting topic in the field of financial time series prediction. Despite the existence of several traditional and computational models for currency exchange rate forecasting, there is always a need for developing simpler and more efficient model, which will produce better prediction capability. In this paper, an evolutionary framework is proposed by using an improved shuffled frog leaping (ISFL) algorithm with a computationally efficient functional link artificial neural network (CEFLANN) for prediction of currency exchange rate. The model is validated by observing the monthly prediction measures obtained for three currency exchange data sets such as USD/CAD, USD/CHF, and USD/JPY accumulated within same period of time. The model performance is also compared with two other evolutionary learning techniques such as Shuffled frog leaping algorithm and Particle Swarm optimization algorithm. Practical analysis of results suggest that, the proposed model developed using the ISFL algorithm with CEFLANN network is a promising predictor model for currency exchange rate prediction compared to other models included in the study.

Evolution of Collective Behaviour in an Artificial World Using Linguistic Fuzzy Rule-Based Systems.

PubMed

Demšar, Jure; Lebar Bajec, Iztok

2017-01-01

Collective behaviour is a fascinating and easily observable phenomenon, attractive to a wide range of researchers. In biology, computational models have been extensively used to investigate various properties of collective behaviour, such as: transfer of information across the group, benefits of grouping (defence against predation, foraging), group decision-making process, and group behaviour types. The question 'why,' however remains largely unanswered. Here the interest goes into which pressures led to the evolution of such behaviour, and evolutionary computational models have already been used to test various biological hypotheses. Most of these models use genetic algorithms to tune the parameters of previously presented non-evolutionary models, but very few attempt to evolve collective behaviour from scratch. Of these last, the successful attempts display clumping or swarming behaviour. Empirical evidence suggests that in fish schools there exist three classes of behaviour; swarming, milling and polarized. In this paper we present a novel, artificial life-like evolutionary model, where individual agents are governed by linguistic fuzzy rule-based systems, which is capable of evolving all three classes of behaviour.

An Improved Co-evolutionary Particle Swarm Optimization for Wireless Sensor Networks with Dynamic Deployment

PubMed Central

Wang, Xue; Wang, Sheng; Ma, Jun-Jie

2007-01-01

The effectiveness of wireless sensor networks (WSNs) depends on the coverage and target detection probability provided by dynamic deployment, which is usually supported by the virtual force (VF) algorithm. However, in the VF algorithm, the virtual force exerted by stationary sensor nodes will hinder the movement of mobile sensor nodes. Particle swarm optimization (PSO) is introduced as another dynamic deployment algorithm, but in this case the computation time required is the big bottleneck. This paper proposes a dynamic deployment algorithm which is named “virtual force directed co-evolutionary particle swarm optimization” (VFCPSO), since this algorithm combines the co-evolutionary particle swarm optimization (CPSO) with the VF algorithm, whereby the CPSO uses multiple swarms to optimize different components of the solution vectors for dynamic deployment cooperatively and the velocity of each particle is updated according to not only the historical local and global optimal solutions, but also the virtual forces of sensor nodes. Simulation results demonstrate that the proposed VFCPSO is competent for dynamic deployment in WSNs and has better performance with respect to computation time and effectiveness than the VF, PSO and VFPSO algorithms.

Evolutionary Optimization of a Geometrically Refined Truss

NASA Technical Reports Server (NTRS)

Hull, P. V.; Tinker, M. L.; Dozier, G. V.

2007-01-01

Structural optimization is a field of research that has experienced noteworthy growth for many years. Researchers in this area have developed optimization tools to successfully design and model structures, typically minimizing mass while maintaining certain deflection and stress constraints. Numerous optimization studies have been performed to minimize mass, deflection, and stress on a benchmark cantilever truss problem. Predominantly traditional optimization theory is applied to this problem. The cross-sectional area of each member is optimized to minimize the aforementioned objectives. This Technical Publication (TP) presents a structural optimization technique that has been previously applied to compliant mechanism design. This technique demonstrates a method that combines topology optimization, geometric refinement, finite element analysis, and two forms of evolutionary computation: genetic algorithms and differential evolution to successfully optimize a benchmark structural optimization problem. A nontraditional solution to the benchmark problem is presented in this TP, specifically a geometrically refined topological solution. The design process begins with an alternate control mesh formulation, multilevel geometric smoothing operation, and an elastostatic structural analysis. The design process is wrapped in an evolutionary computing optimization toolset.

[The nature of personality: a co-evolutionary perspective].

PubMed

Asendorpf, J B

1996-01-01

Personality psychologists' attempts to explain human diversity have traditionally focused upon processes of person-situation interaction, and genotype-environment interaction. The great variability of genotypes and environments within cultures has remained unexplained in these efforts. Which processes may be responsible for the genetic and environmental variability within cultures? Answers to this question are sought in processes of genetic-cultural coevolution: mutation and sexual recombination of genes, innovation and synthesis of memes (units of cultural transmission), genotype-->environment and meme-->environment effects, and frequency-dependent natural and cultural selection. This twofold evolutionary explanation of personality differences within cultures suggests that a solid foundation of personality psychology requires bridging biology and cultural science.

Cyclic dominance in evolutionary games: a review

PubMed Central

Szolnoki, Attila; Mobilia, Mauro; Jiang, Luo-Luo; Szczesny, Bartosz; Rucklidge, Alastair M.; Perc, Matjaž

2014-01-01

Rock is wrapped by paper, paper is cut by scissors and scissors are crushed by rock. This simple game is popular among children and adults to decide on trivial disputes that have no obvious winner, but cyclic dominance is also at the heart of predator–prey interactions, the mating strategy of side-blotched lizards, the overgrowth of marine sessile organisms and competition in microbial populations. Cyclical interactions also emerge spontaneously in evolutionary games entailing volunteering, reward, punishment, and in fact are common when the competing strategies are three or more, regardless of the particularities of the game. Here, we review recent advances on the rock–paper–scissors (RPS) and related evolutionary games, focusing, in particular, on pattern formation, the impact of mobility and the spontaneous emergence of cyclic dominance. We also review mean-field and zero-dimensional RPS models and the application of the complex Ginzburg–Landau equation, and we highlight the importance and usefulness of statistical physics for the successful study of large-scale ecological systems. Directions for future research, related, for example, to dynamical effects of coevolutionary rules and invasion reversals owing to multi-point interactions, are also outlined. PMID:25232048

Cyclic dominance in evolutionary games: a review.

PubMed

Szolnoki, Attila; Mobilia, Mauro; Jiang, Luo-Luo; Szczesny, Bartosz; Rucklidge, Alastair M; Perc, Matjaž

2014-11-06

Rock is wrapped by paper, paper is cut by scissors and scissors are crushed by rock. This simple game is popular among children and adults to decide on trivial disputes that have no obvious winner, but cyclic dominance is also at the heart of predator-prey interactions, the mating strategy of side-blotched lizards, the overgrowth of marine sessile organisms and competition in microbial populations. Cyclical interactions also emerge spontaneously in evolutionary games entailing volunteering, reward, punishment, and in fact are common when the competing strategies are three or more, regardless of the particularities of the game. Here, we review recent advances on the rock-paper-scissors (RPS) and related evolutionary games, focusing, in particular, on pattern formation, the impact of mobility and the spontaneous emergence of cyclic dominance. We also review mean-field and zero-dimensional RPS models and the application of the complex Ginzburg-Landau equation, and we highlight the importance and usefulness of statistical physics for the successful study of large-scale ecological systems. Directions for future research, related, for example, to dynamical effects of coevolutionary rules and invasion reversals owing to multi-point interactions, are also outlined. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

On the numerical treatment of selected oscillatory evolutionary problems

NASA Astrophysics Data System (ADS)

Cardone, Angelamaria; Conte, Dajana; D'Ambrosio, Raffaele; Paternoster, Beatrice

2017-07-01

We focus on evolutionary problems whose qualitative behaviour is known a-priori and exploited in order to provide efficient and accurate numerical schemes. For classical numerical methods, depending on constant coefficients, the required computational effort could be quite heavy, due to the necessary employ of very small stepsizes needed to accurately reproduce the qualitative behaviour of the solution. In these situations, it may be convenient to use special purpose formulae, i.e. non-polynomially fitted formulae on basis functions adapted to the problem (see [16, 17] and references therein). We show examples of special purpose strategies to solve two families of evolutionary problems exhibiting periodic solutions, i.e. partial differential equations and Volterra integral equations.

Human evolutionary genomics: ethical and interpretive issues.

PubMed

Vitti, Joseph J; Cho, Mildred K; Tishkoff, Sarah A; Sabeti, Pardis C

2012-03-01

Genome-wide computational studies can now identify targets of natural selection. The unique information about humans these studies reveal, and the media attention they attract, indicate the need for caution and precision in communicating results. This need is exacerbated by ways in which evolutionary and genetic considerations have been misapplied to support discriminatory policies, by persistent misconceptions of these fields and by the social sensitivity surrounding discussions of racial ancestry. We discuss the foundations, accomplishments and future directions of human evolutionary genomics, attending to ways in which the interpretation of good science can go awry, and offer suggestions for researchers to prevent misapplication of their work. Copyright © 2011 Elsevier Ltd. All rights reserved.

Inferring population history with DIY ABC: a user-friendly approach to approximate Bayesian computation.

PubMed

Cornuet, Jean-Marie; Santos, Filipe; Beaumont, Mark A; Robert, Christian P; Marin, Jean-Michel; Balding, David J; Guillemaud, Thomas; Estoup, Arnaud

2008-12-01

Genetic data obtained on population samples convey information about their evolutionary history. Inference methods can extract part of this information but they require sophisticated statistical techniques that have been made available to the biologist community (through computer programs) only for simple and standard situations typically involving a small number of samples. We propose here a computer program (DIY ABC) for inference based on approximate Bayesian computation (ABC), in which scenarios can be customized by the user to fit many complex situations involving any number of populations and samples. Such scenarios involve any combination of population divergences, admixtures and population size changes. DIY ABC can be used to compare competing scenarios, estimate parameters for one or more scenarios and compute bias and precision measures for a given scenario and known values of parameters (the current version applies to unlinked microsatellite data). This article describes key methods used in the program and provides its main features. The analysis of one simulated and one real dataset, both with complex evolutionary scenarios, illustrates the main possibilities of DIY ABC. The software DIY ABC is freely available at http://www.montpellier.inra.fr/CBGP/diyabc.

The drug target genes show higher evolutionary conservation than non-target genes.

PubMed

Lv, Wenhua; Xu, Yongdeng; Guo, Yiying; Yu, Ziqi; Feng, Guanglong; Liu, Panpan; Luan, Meiwei; Zhu, Hongjie; Liu, Guiyou; Zhang, Mingming; Lv, Hongchao; Duan, Lian; Shang, Zhenwei; Li, Jin; Jiang, Yongshuai; Zhang, Ruijie

2016-01-26

Although evidence indicates that drug target genes share some common evolutionary features, there have been few studies analyzing evolutionary features of drug targets from an overall level. Therefore, we conducted an analysis which aimed to investigate the evolutionary characteristics of drug target genes. We compared the evolutionary conservation between human drug target genes and non-target genes by combining both the evolutionary features and network topological properties in human protein-protein interaction network. The evolution rate, conservation score and the percentage of orthologous genes of 21 species were included in our study. Meanwhile, four topological features including the average shortest path length, betweenness centrality, clustering coefficient and degree were considered for comparison analysis. Then we got four results as following: compared with non-drug target genes, 1) drug target genes had lower evolutionary rates; 2) drug target genes had higher conservation scores; 3) drug target genes had higher percentages of orthologous genes and 4) drug target genes had a tighter network structure including higher degrees, betweenness centrality, clustering coefficients and lower average shortest path lengths. These results demonstrate that drug target genes are more evolutionarily conserved than non-drug target genes. We hope that our study will provide valuable information for other researchers who are interested in evolutionary conservation of drug targets.

Evolving Non-Dominated Parameter Sets for Computational Models from Multiple Experiments

NASA Astrophysics Data System (ADS)

Lane, Peter C. R.; Gobet, Fernand

2013-03-01

Creating robust, reproducible and optimal computational models is a key challenge for theorists in many sciences. Psychology and cognitive science face particular challenges as large amounts of data are collected and many models are not amenable to analytical techniques for calculating parameter sets. Particular problems are to locate the full range of acceptable model parameters for a given dataset, and to confirm the consistency of model parameters across different datasets. Resolving these problems will provide a better understanding of the behaviour of computational models, and so support the development of general and robust models. In this article, we address these problems using evolutionary algorithms to develop parameters for computational models against multiple sets of experimental data; in particular, we propose the `speciated non-dominated sorting genetic algorithm' for evolving models in several theories. We discuss the problem of developing a model of categorisation using twenty-nine sets of data and models drawn from four different theories. We find that the evolutionary algorithms generate high quality models, adapted to provide a good fit to all available data.

A program to compute the soft Robinson-Foulds distance between phylogenetic networks.

PubMed

Lu, Bingxin; Zhang, Louxin; Leong, Hon Wai

2017-03-14

Over the past two decades, phylogenetic networks have been studied to model reticulate evolutionary events. The relationships among phylogenetic networks, phylogenetic trees and clusters serve as the basis for reconstruction and comparison of phylogenetic networks. To understand these relationships, two problems are raised: the tree containment problem, which asks whether a phylogenetic tree is displayed in a phylogenetic network, and the cluster containment problem, which asks whether a cluster is represented at a node in a phylogenetic network. Both the problems are NP-complete. A fast exponential-time algorithm for the cluster containment problem on arbitrary networks is developed and implemented in C. The resulting program is further extended into a computer program for fast computation of the Soft Robinson-Foulds distance between phylogenetic networks. Two computer programs are developed for facilitating reconstruction and validation of phylogenetic network models in evolutionary and comparative genomics. Our simulation tests indicated that they are fast enough for use in practice. Additionally, the distribution of the Soft Robinson-Foulds distance between phylogenetic networks is demonstrated to be unlikely normal by our simulation data.

Decimetre Waves and Cerebellar Cortex Key Element - Purkinje Cells

NASA Astrophysics Data System (ADS)

Maharramov, Akif A.

2007-04-01

Acute experiments have been carried out on both decerebrated and anaesthetized adult cats exposed to Decimetre Range Microwaves (DRM) (λ=65 cm, duration of exposition -10 min., at least) by the help of a 2 cm radius contact applicator located on the temple part of the head with cerebellum in the centre of projection of irradiation conducted from portable physiotherapeutic apparatus ``Romashka''. Extracelullar registration of Purkinje Cells (PC) impulse activities have been led by glass microelectrodes. Statistical and computational analyses of PC activities have been realized by the help of histograms - the characteristic distribution of the number of interimpulse intervals (II) between electrical discharges of a neuron on the II durations, drawn up by the help of a computer. The results obtained revealed the reaction of PC to DRM as a succession of starting to react of PC electrophysiological parameters, beginning from decreasing of known ``Inhibitory Pause'' duration, and further, at first, ``Simple Spikes'' then ``Complex Spikes'' frequencies' increase, and furthermore, durations' increase in ``Big Interimpulse Intervals'', parameter, introduced for the first time by us, in this way, showing the ``evolutionary'' nature of Electromagnetic Fields and Living object interactions.

Multiobjective Multifactorial Optimization in Evolutionary Multitasking.

PubMed

Gupta, Abhishek; Ong, Yew-Soon; Feng, Liang; Tan, Kay Chen

2016-05-03

In recent decades, the field of multiobjective optimization has attracted considerable interest among evolutionary computation researchers. One of the main features that makes evolutionary methods particularly appealing for multiobjective problems is the implicit parallelism offered by a population, which enables simultaneous convergence toward the entire Pareto front. While a plethora of related algorithms have been proposed till date, a common attribute among them is that they focus on efficiently solving only a single optimization problem at a time. Despite the known power of implicit parallelism, seldom has an attempt been made to multitask, i.e., to solve multiple optimization problems simultaneously. It is contended that the notion of evolutionary multitasking leads to the possibility of automated transfer of information across different optimization exercises that may share underlying similarities, thereby facilitating improved convergence characteristics. In particular, the potential for automated transfer is deemed invaluable from the standpoint of engineering design exercises where manual knowledge adaptation and reuse are routine. Accordingly, in this paper, we present a realization of the evolutionary multitasking paradigm within the domain of multiobjective optimization. The efficacy of the associated evolutionary algorithm is demonstrated on some benchmark test functions as well as on a real-world manufacturing process design problem from the composites industry.

Understanding the mind from an evolutionary perspective: an overview of evolutionary psychology.

PubMed

Shackelford, Todd K; Liddle, James R

2014-05-01

The theory of evolution by natural selection provides the only scientific explanation for the existence of complex adaptations. The design features of the brain, like any organ, are the result of selection pressures operating over deep time. Evolutionary psychology posits that the human brain comprises a multitude of evolved psychological mechanisms, adaptations to specific and recurrent problems of survival and reproduction faced over human evolutionary history. Although some mistakenly view evolutionary psychology as promoting genetic determinism, evolutionary psychologists appreciate and emphasize the interactions between genes and environments. This approach to psychology has led to a richer understanding of a variety of psychological phenomena, and has provided a powerful foundation for generating novel hypotheses. Critics argue that evolutionary psychologists resort to storytelling, but as with any branch of science, empirical testing is a vital component of the field, with hypotheses standing or falling with the weight of the evidence. Evolutionary psychology is uniquely suited to provide a unifying theoretical framework for the disparate subdisciplines of psychology. An evolutionary perspective has provided insights into several subdisciplines of psychology, while simultaneously demonstrating the arbitrary nature of dividing psychological science into such subdisciplines. Evolutionary psychologists have amassed a substantial empirical and theoretical literature, but as a relatively new approach to psychology, many questions remain, with several promising directions for future research. For further resources related to this article, please visit the WIREs website. The authors have declared no conflicts of interest for this article. © 2014 John Wiley & Sons, Ltd.

Evolutionary dynamics of the traveler's dilemma and minimum-effort coordination games on complex networks.

PubMed

Iyer, Swami; Killingback, Timothy

2014-10-01

The traveler's dilemma game and the minimum-effort coordination game are social dilemmas that have received significant attention resulting from the fact that the predictions of classical game theory are inconsistent with the results found when the games are studied experimentally. Moreover, both the traveler's dilemma and the minimum-effort coordination games have potentially important applications in evolutionary biology. Interestingly, standard deterministic evolutionary game theory, as represented by the replicator dynamics in a well-mixed population, is also inadequate to account for the behavior observed in these games. Here we study the evolutionary dynamics of both these games in populations with interaction patterns described by a variety of complex network topologies. We investigate the evolutionary dynamics of these games through agent-based simulations on both model and empirical networks. In particular, we study the effects of network clustering and assortativity on the evolutionary dynamics of both games. In general, we show that the evolutionary behavior of the traveler's dilemma and minimum-effort coordination games on complex networks is in good agreement with that observed experimentally. Thus, formulating the traveler's dilemma and the minimum-effort coordination games on complex networks neatly resolves the paradoxical aspects of these games.

Do arms races punctuate evolutionary stasis? Unified insights from phylogeny, phylogeography and microevolutionary processes.

PubMed

Toju, Hirokazu; Sota, Teiji

2009-09-01

One of the major controversies in evolutionary biology concerns the processes underlying macroevolutionary patterns in which prolonged stasis is disrupted by rapid, short-term evolution that leads species to new adaptive zones. Recent advances in the understanding of contemporary evolution have suggested that such rapid evolution can occur in the wild as a result of environmental changes. Here, we examined a novel hypothesis that evolutionary stasis is punctuated by co-evolutionary arms races, which continuously alter adaptive peaks and landscapes. Based on the phylogeny of long-mouthed weevils in the genus Curculio, likelihood ratio tests showed that the macroevolutionary pattern of the weevils coincides with the punctuational evolution model. A coalescent analysis of a species, Curculio camelliae, the mouthpart of which has diverged considerably among populations because of an arms race with its host plant, further suggested that major evolutionary shifts had occurred within 7000 generations. Through a microevolutionary analysis of the species, we also found that natural selection acting through co-evolutionary interactions is potentially strong enough to drive rapid evolutionary shifts between adaptive zones. Overall, we posit that co-evolution is an important factor driving the history of organismal evolution.

Evolutionary dynamics of the traveler's dilemma and minimum-effort coordination games on complex networks

NASA Astrophysics Data System (ADS)

Iyer, Swami; Killingback, Timothy

2014-10-01

The traveler's dilemma game and the minimum-effort coordination game are social dilemmas that have received significant attention resulting from the fact that the predictions of classical game theory are inconsistent with the results found when the games are studied experimentally. Moreover, both the traveler's dilemma and the minimum-effort coordination games have potentially important applications in evolutionary biology. Interestingly, standard deterministic evolutionary game theory, as represented by the replicator dynamics in a well-mixed population, is also inadequate to account for the behavior observed in these games. Here we study the evolutionary dynamics of both these games in populations with interaction patterns described by a variety of complex network topologies. We investigate the evolutionary dynamics of these games through agent-based simulations on both model and empirical networks. In particular, we study the effects of network clustering and assortativity on the evolutionary dynamics of both games. In general, we show that the evolutionary behavior of the traveler's dilemma and minimum-effort coordination games on complex networks is in good agreement with that observed experimentally. Thus, formulating the traveler's dilemma and the minimum-effort coordination games on complex networks neatly resolves the paradoxical aspects of these games.

The ecology of cancer from an evolutionary game theory perspective.

PubMed

Pacheco, Jorge M; Santos, Francisco C; Dingli, David

2014-08-06

The accumulation of somatic mutations, to which the cellular genome is permanently exposed, often leads to cancer. Analysis of any tumour shows that, besides the malignant cells, one finds other 'supporting' cells such as fibroblasts, immune cells of various types and even blood vessels. Together, these cells generate the microenvironment that enables the malignant cell population to grow and ultimately lead to disease. Therefore, understanding the dynamics of tumour growth and response to therapy is incomplete unless the interactions between the malignant cells and normal cells are investigated in the environment in which they take place. The complex interactions between cells in such an ecosystem result from the exchange of information in the form of cytokines- and adhesion-dependent interactions. Such processes impose costs and benefits to the participating cells that may be conveniently recast in the form of a game pay-off matrix. As a result, tumour progression and dynamics can be described in terms of evolutionary game theory (EGT), which provides a convenient framework in which to capture the frequency-dependent nature of ecosystem dynamics. Here, we provide a tutorial review of the central aspects of EGT, establishing a relation with the problem of cancer. Along the way, we also digress on fitness and of ways to compute it. Subsequently, we show how EGT can be applied to the study of the various manifestations and dynamics of multiple myeloma bone disease and its preceding condition known as monoclonal gammopathy of undetermined significance. We translate the complex biochemical signals into costs and benefits of different cell types, thus defining a game pay-off matrix. Then we use the well-known properties of the EGT equations to reduce the number of core parameters that characterize disease evolution. Finally, we provide an interpretation of these core parameters in terms of what their function is in the ecosystem we are describing and generate predictions on the type and timing of interventions that can alter the natural history of these two conditions.

Replaying evolutionary transitions from the dental fossil record

PubMed Central

Harjunmaa, Enni; Seidel, Kerstin; Häkkinen, Teemu; Renvoisé, Elodie; Corfe, Ian J.; Kallonen, Aki; Zhang, Zhao-Qun; Evans, Alistair R.; Mikkola, Marja L.; Salazar-Ciudad, Isaac; Klein, Ophir D.; Jernvall, Jukka

2014-01-01

The evolutionary relationships of extinct species are ascertained primarily through the analysis of morphological characters. Character inter-dependencies can have a substantial effect on evolutionary interpretations, but the developmental underpinnings of character inter-dependence remain obscure because experiments frequently do not provide detailed resolution of morphological characters. Here we show experimentally and computationally how gradual modification of development differentially affects characters in the mouse dentition. We found that intermediate phenotypes could be produced by gradually adding ectodysplasin A (EDA) protein in culture to tooth explants carrying a null mutation in the tooth-patterning gene Eda. By identifying development-based character interdependencies, we show how to predict morphological patterns of teeth among mammalian species. Finally, in vivo inhibition of sonic hedgehog signalling in Eda null teeth enabled us to reproduce characters deep in the rodent ancestry. Taken together, evolutionarily informative transitions can be experimentally reproduced, thereby providing development-based expectations for character state transitions used in evolutionary studies. PMID:25079326

Selection on Network Dynamics Drives Differential Rates of Protein Domain Evolution

PubMed Central

Mannakee, Brian K.; Gutenkunst, Ryan N.

2016-01-01

The long-held principle that functionally important proteins evolve slowly has recently been challenged by studies in mice and yeast showing that the severity of a protein knockout only weakly predicts that protein’s rate of evolution. However, the relevance of these studies to evolutionary changes within proteins is unknown, because amino acid substitutions, unlike knockouts, often only slightly perturb protein activity. To quantify the phenotypic effect of small biochemical perturbations, we developed an approach to use computational systems biology models to measure the influence of individual reaction rate constants on network dynamics. We show that this dynamical influence is predictive of protein domain evolutionary rate within networks in vertebrates and yeast, even after controlling for expression level and breadth, network topology, and knockout effect. Thus, our results not only demonstrate the importance of protein domain function in determining evolutionary rate, but also the power of systems biology modeling to uncover unanticipated evolutionary forces. PMID:27380265

Incorporating evolutionary processes into population viability models.

PubMed

Pierson, Jennifer C; Beissinger, Steven R; Bragg, Jason G; Coates, David J; Oostermeijer, J Gerard B; Sunnucks, Paul; Schumaker, Nathan H; Trotter, Meredith V; Young, Andrew G

2015-06-01

We examined how ecological and evolutionary (eco-evo) processes in population dynamics could be better integrated into population viability analysis (PVA). Complementary advances in computation and population genomics can be combined into an eco-evo PVA to offer powerful new approaches to understand the influence of evolutionary processes on population persistence. We developed the mechanistic basis of an eco-evo PVA using individual-based models with individual-level genotype tracking and dynamic genotype-phenotype mapping to model emergent population-level effects, such as local adaptation and genetic rescue. We then outline how genomics can allow or improve parameter estimation for PVA models by providing genotypic information at large numbers of loci for neutral and functional genome regions. As climate change and other threatening processes increase in rate and scale, eco-evo PVAs will become essential research tools to evaluate the effects of adaptive potential, evolutionary rescue, and locally adapted traits on persistence. © 2014 Society for Conservation Biology.

Historical Contingency in Controlled Evolution

NASA Astrophysics Data System (ADS)

Schuster, Peter

2014-12-01

A basic question in evolution is dealing with the nature of an evolutionary memory. At thermodynamic equilibrium, at stable stationary states or other stable attractors the memory on the path leading to the long-time solution is erased, at least in part. Similar arguments hold for unique optima. Optimality in biology is discussed on the basis of microbial metabolism. Biology, on the other hand, is characterized by historical contingency, which has recently become accessible to experimental test in bacterial populations evolving under controlled conditions. Computer simulations give additional insight into the nature of the evolutionary memory, which is ultimately caused by the enormous space of possibilities that is so large that it escapes all attempts of visualization. In essence, this contribution is dealing with two questions of current evolutionary theory: (i) Are organisms operating at optimal performance? and (ii) How is the evolutionary memory built up in populations?

The role of mites in insect-fungus associations

Treesearch

R. W. Hofstetter; J. C. Moser

2014-01-01

The interactions among insects, mites, and fungi are diverse and complex but poorly understood in most cases. Associations among insects, mites, and fungi span an almost incomprehensible array of ecological interactions and evolutionary histories. Insects and mites often share habitats and resources and thus interact within communities. Many mites and insects rely on...

Comparative study of the effectiveness and limitations of current methods for detecting sequence coevolution.

PubMed

Mao, Wenzhi; Kaya, Cihan; Dutta, Anindita; Horovitz, Amnon; Bahar, Ivet

2015-06-15

With rapid accumulation of sequence data on several species, extracting rational and systematic information from multiple sequence alignments (MSAs) is becoming increasingly important. Currently, there is a plethora of computational methods for investigating coupled evolutionary changes in pairs of positions along the amino acid sequence, and making inferences on structure and function. Yet, the significance of coevolution signals remains to be established. Also, a large number of false positives (FPs) arise from insufficient MSA size, phylogenetic background and indirect couplings. Here, a set of 16 pairs of non-interacting proteins is thoroughly examined to assess the effectiveness and limitations of different methods. The analysis shows that recent computationally expensive methods designed to remove biases from indirect couplings outperform others in detecting tertiary structural contacts as well as eliminating intermolecular FPs; whereas traditional methods such as mutual information benefit from refinements such as shuffling, while being highly efficient. Computations repeated with 2,330 pairs of protein families from the Negatome database corroborated these results. Finally, using a training dataset of 162 families of proteins, we propose a combined method that outperforms existing individual methods. Overall, the study provides simple guidelines towards the choice of suitable methods and strategies based on available MSA size and computing resources. Software is freely available through the Evol component of ProDy API. © The Author 2015. Published by Oxford University Press.

Adaptive Tracking Control for Robots With an Interneural Computing Scheme.

PubMed

Tsai, Feng-Sheng; Hsu, Sheng-Yi; Shih, Mau-Hsiang

2018-04-01

Adaptive tracking control of mobile robots requires the ability to follow a trajectory generated by a moving target. The conventional analysis of adaptive tracking uses energy minimization to study the convergence and robustness of the tracking error when the mobile robot follows a desired trajectory. However, in the case that the moving target generates trajectories with uncertainties, a common Lyapunov-like function for energy minimization may be extremely difficult to determine. Here, to solve the adaptive tracking problem with uncertainties, we wish to implement an interneural computing scheme in the design of a mobile robot for behavior-based navigation. The behavior-based navigation adopts an adaptive plan of behavior patterns learning from the uncertainties of the environment. The characteristic feature of the interneural computing scheme is the use of neural path pruning with rewards and punishment interacting with the environment. On this basis, the mobile robot can be exploited to change its coupling weights in paths of neural connections systematically, which can then inhibit or enhance the effect of flow elimination in the dynamics of the evolutionary neural network. Such dynamical flow translation ultimately leads to robust sensory-to-motor transformations adapting to the uncertainties of the environment. A simulation result shows that the mobile robot with the interneural computing scheme can perform fault-tolerant behavior of tracking by maintaining suitable behavior patterns at high frequency levels.

The emergence of mind and brain: an evolutionary, computational, and philosophical approach.

PubMed

Mainzer, Klaus

2008-01-01

Modern philosophy of mind cannot be understood without recent developments in computer science, artificial intelligence (AI), robotics, neuroscience, biology, linguistics, and psychology. Classical philosophy of formal languages as well as symbolic AI assume that all kinds of knowledge must explicitly be represented by formal or programming languages. This assumption is limited by recent insights into the biology of evolution and developmental psychology of the human organism. Most of our knowledge is implicit and unconscious. It is not formally represented, but embodied knowledge, which is learnt by doing and understood by bodily interacting with changing environments. That is true not only for low-level skills, but even for high-level domains of categorization, language, and abstract thinking. The embodied mind is considered an emergent capacity of the brain as a self-organizing complex system. Actually, self-organization has been a successful strategy of evolution to handle the increasing complexity of the world. Genetic programs are not sufficient and cannot prepare the organism for all kinds of complex situations in the future. Self-organization and emergence are fundamental concepts in the theory of complex dynamical systems. They are also applied in organic computing as a recent research field of computer science. Therefore, cognitive science, AI, and robotics try to model the embodied mind in an artificial evolution. The paper analyzes these approaches in the interdisciplinary framework of complex dynamical systems and discusses their philosophical impact.

Evolutionary diversification of protein-protein interactions by interface add-ons.

PubMed

Plach, Maximilian G; Semmelmann, Florian; Busch, Florian; Busch, Markus; Heizinger, Leonhard; Wysocki, Vicki H; Merkl, Rainer; Sterner, Reinhard

2017-10-03

Cells contain a multitude of protein complexes whose subunits interact with high specificity. However, the number of different protein folds and interface geometries found in nature is limited. This raises the question of how protein-protein interaction specificity is achieved on the structural level and how the formation of nonphysiological complexes is avoided. Here, we describe structural elements called interface add-ons that fulfill this function and elucidate their role for the diversification of protein-protein interactions during evolution. We identified interface add-ons in 10% of a representative set of bacterial, heteromeric protein complexes. The importance of interface add-ons for protein-protein interaction specificity is demonstrated by an exemplary experimental characterization of over 30 cognate and hybrid glutamine amidotransferase complexes in combination with comprehensive genetic profiling and protein design. Moreover, growth experiments showed that the lack of interface add-ons can lead to physiologically harmful cross-talk between essential biosynthetic pathways. In sum, our complementary in silico, in vitro, and in vivo analysis argues that interface add-ons are a practical and widespread evolutionary strategy to prevent the formation of nonphysiological complexes by specializing protein-protein interactions.

Reciprocity in spatial evolutionary public goods game on double-layered network

NASA Astrophysics Data System (ADS)

Kim, Jinho; Yook, Soon-Hyung; Kim, Yup

2016-08-01

Spatial evolutionary games have mainly been studied on a single, isolated network. However, in real world systems, many interaction topologies are not isolated but many different types of networks are inter-connected to each other. In this study, we investigate the spatial evolutionary public goods game (SEPGG) on double-layered random networks (DRN). Based on the mean-field type arguments and numerical simulations, we find that SEPGG on DRN shows very rich interesting phenomena, especially, depending on the size of each layer, intra-connectivity, and inter-connected couplings, the network reciprocity of SEPGG on DRN can be drastically enhanced through the inter-connected coupling. Furthermore, SEPGG on DRN can provide a more general framework which includes the evolutionary dynamics on multiplex networks and inter-connected networks at the same time.

Reciprocity in spatial evolutionary public goods game on double-layered network

PubMed Central

Kim, Jinho; Yook, Soon-Hyung; Kim, Yup

2016-01-01

Spatial evolutionary games have mainly been studied on a single, isolated network. However, in real world systems, many interaction topologies are not isolated but many different types of networks are inter-connected to each other. In this study, we investigate the spatial evolutionary public goods game (SEPGG) on double-layered random networks (DRN). Based on the mean-field type arguments and numerical simulations, we find that SEPGG on DRN shows very rich interesting phenomena, especially, depending on the size of each layer, intra-connectivity, and inter-connected couplings, the network reciprocity of SEPGG on DRN can be drastically enhanced through the inter-connected coupling. Furthermore, SEPGG on DRN can provide a more general framework which includes the evolutionary dynamics on multiplex networks and inter-connected networks at the same time. PMID:27503801

Evolutionary psychology and intelligence research.

PubMed

Kanazawa, Satoshi

2010-01-01

This article seeks to unify two subfields of psychology that have hitherto stood separately: evolutionary psychology and intelligence research/differential psychology. I suggest that general intelligence may simultaneously be an evolved adaptation and an individual-difference variable. Tooby and Cosmides's (1990a) notion of random quantitative variation on a monomorphic design allows us to incorporate heritable individual differences in evolved adaptations. The Savanna-IQ Interaction Hypothesis, which is one consequence of the integration of evolutionary psychology and intelligence research, can potentially explain why less intelligent individuals enjoy TV more, why liberals are more intelligent than conservatives, and why night owls are more intelligent than morning larks, among many other findings. The general approach proposed here will allow us to integrate evolutionary psychology with any other aspect of differential psychology. Copyright 2010 APA, all rights reserved.

Human vs. Computer Diagnosis of Students' Natural Selection Knowledge: Testing the Efficacy of Text Analytic Software

NASA Astrophysics Data System (ADS)

Nehm, Ross H.; Haertig, Hendrik

2012-02-01

Our study examines the efficacy of Computer Assisted Scoring (CAS) of open-response text relative to expert human scoring within the complex domain of evolutionary biology. Specifically, we explored whether CAS can diagnose the explanatory elements (or Key Concepts) that comprise undergraduate students' explanatory models of natural selection with equal fidelity as expert human scorers in a sample of >1,000 essays. We used SPSS Text Analysis 3.0 to perform our CAS and measure Kappa values (inter-rater reliability) of KC detection (i.e., computer-human rating correspondence). Our first analysis indicated that the text analysis functions (or extraction rules) developed and deployed in SPSSTA to extract individual Key Concepts (KCs) from three different items differing in several surface features (e.g., taxon, trait, type of evolutionary change) produced "substantial" (Kappa 0.61-0.80) or "almost perfect" (0.81-1.00) agreement. The second analysis explored the measurement of human-computer correspondence for KC diversity (the number of different accurate knowledge elements) in the combined sample of all 827 essays. Here we found outstanding correspondence; extraction rules generated using one prompt type are broadly applicable to other evolutionary scenarios (e.g., bacterial resistance, cheetah running speed, etc.). This result is encouraging, as it suggests that the development of new item sets may not necessitate the development of new text analysis rules. Overall, our findings suggest that CAS tools such as SPSS Text Analysis may compensate for some of the intrinsic limitations of currently used multiple-choice Concept Inventories designed to measure student knowledge of natural selection.

Evolutionary perspectives on the links between mitochondrial genotype and disease phenotype.

PubMed

Dowling, Damian K

2014-04-01

Disorders of the mitochondrial respiratory chain are heterogeneous in their symptoms and underlying genetics. Simple links between candidate mutations and expression of disease phenotype typically do not exist. It thus remains unclear how the genetic variation in the mitochondrial genome contributes to the phenotypic expression of complex traits and disease phenotypes. I summarize the basic genetic processes known to underpin mitochondrial disease. I highlight other plausible processes, drawn from the evolutionary biological literature, whose contribution to mitochondrial disease expression remains largely empirically unexplored. I highlight recent advances to the field, and discuss common-ground and -goals shared by researchers across medical and evolutionary domains. Mitochondrial genetic variance is linked to phenotypic variance across a variety of traits (e.g. reproductive function, life expectancy) fundamental to the upkeep of good health. Evolutionary theory predicts that mitochondrial genomes are destined to accumulate male-harming (but female-friendly) mutations, and this prediction has received proof-of-principle support. Furthermore, mitochondrial effects on the phenotype are typically manifested via interactions between mitochondrial and nuclear genes. Thus, whether a mitochondrial mutation is pathogenic in effect can depend on the nuclear genotype in which is it expressed. Many disease phenotypes associated with OXPHOS malfunction might be determined by the outcomes of mitochondrial-nuclear interactions, and by the evolutionary forces that historically shaped mitochondrial DNA (mtDNA) sequences. Concepts and results drawn from the evolutionary sciences can have broad, but currently under-utilized, applicability to the medical sciences and provide new insights into understanding the complex genetics of mitochondrial disease. This article is part of a Special Issue entitled Frontiers of Mitochondrial Research. Copyright © 2013. Published by Elsevier B.V.

Limited gene dispersal and spatial genetic structure as stabilizing factors in an ant-plant mutualism.

PubMed

Malé, P-J G; Leroy, C; Humblot, P; Dejean, A; Quilichini, A; Orivel, J

2016-12-01

Comparative studies of the population genetics of closely associated species are necessary to properly understand the evolution of these relationships because gene flow between populations affects the partners' evolutionary potential at the local scale. As a consequence (at least for antagonistic interactions), asymmetries in the strength of the genetic structures of the partner populations can result in one partner having a co-evolutionary advantage. Here, we assess the population genetic structure of partners engaged in a species-specific and obligatory mutualism: the Neotropical ant-plant, Hirtella physophora, and its ant associate, Allomerus decemarticulatus. Although the ant cannot complete its life cycle elsewhere than on H. physophora and the plant cannot live for long without the protection provided by A. decemarticulatus, these species also have antagonistic interactions: the ants have been shown to benefit from castrating their host plant and the plant is able to retaliate against too virulent ant colonies. We found similar short dispersal distances for both partners, resulting in the local transmission of the association and, thus, inbred populations in which too virulent castrating ants face the risk of local extinction due to the absence of H. physophora offspring. On the other hand, we show that the plant populations probably experienced greater gene flow than did the ant populations, thus enhancing the evolutionary potential of the plants. We conclude that such levels of spatial structure in the partners' populations can increase the stability of the mutualistic relationship. Indeed, the local transmission of the association enables partial alignments of the partners' interests, and population connectivity allows the plant retaliation mechanisms to be locally adapted to the castration behaviour of their symbionts. © 2016 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2016 European Society For Evolutionary Biology.

Relative impacts of environmental variation and evolutionary history on the nestedness and modularity of tree–herbivore networks

PubMed Central

Robinson, Kathryn M; Hauzy, Céline; Loeuille, Nicolas; Albrectsen, Benedicte R

2015-01-01

Nestedness and modularity are measures of ecological networks whose causative effects are little understood. We analyzed antagonistic plant–herbivore bipartite networks using common gardens in two contrasting environments comprised of aspen trees with differing evolutionary histories of defence against herbivores. These networks were tightly connected owing to a high level of specialization of arthropod herbivores that spend a large proportion of the life cycle on aspen. The gardens were separated by ten degrees of latitude with resultant differences in abiotic conditions. We evaluated network metrics and reported similar connectance between gardens but greater numbers of links per species in the northern common garden. Interaction matrices revealed clear nestedness, indicating subsetting of the bipartite interactions into specialist divisions, in both the environmental and evolutionary aspen groups, although nestedness values were only significant in the northern garden. Variation in plant vulnerability, measured as the frequency of herbivore specialization in the aspen population, was significantly partitioned by environment (common garden) but not by evolutionary origin of the aspens. Significant values of modularity were observed in all network matrices. Trait-matching indicated that growth traits, leaf morphology, and phenolic metabolites affected modular structure in both the garden and evolutionary groups, whereas extra-floral nectaries had little influence. Further examination of module configuration revealed that plant vulnerability explained considerable variance in web structure. The contrasting conditions between the two gardens resulted in bottom-up effects of the environment, which most strongly influenced the overall network architecture, however, the aspen groups with dissimilar evolutionary history also showed contrasting degrees of nestedness and modularity. Our research therefore shows that, while evolution does affect the structure of aspen–herbivore bipartite networks, the role of environmental variations is a dominant constraint. PMID:26306175

Population delimitation across contrasting evolutionary clines in deer mice (Peromyscus maniculatus)

PubMed Central

Yang, D-S; Kenagy, G

2011-01-01

Despite current interest in population genetics, a concrete definition of a “population” remains elusive. Multiple ecologically and evolutionarily based definitions of population are in current use, which focus, respectively, on demographic and genetic interactions. Accurate population delimitation is crucial for not only evolutionary and ecological population biology, but also for conservation of threatened populations. Along the Pacific Coast of North America, two contrasting patterns of geographic variation in deer mice (Peromyscus maniculatus) converge within the state of Oregon. Populations of these mice diverge morphologically across an east–west axis, and they diverge in mitochondrial DNA haplotypes across a north–south axis. In this study, we investigate these geographically contrasting patterns of differentiation in the context of ecological and evolutionary definitions (paradigms) of populations. We investigate these patterns using a new and geographically expansive sample that integrates data on morphology, mitochondrial DNA, and nuclear DNA. We found no evidence of nuclear genetic differentiation between the morphologically and mitochondrially distinct populations, thus indicating the occurrence of gene flow across Oregon. Under the evolutionary paradigm, Oregon populations can be considered a single population, whereas morphological and mitochondrial differentiations do not indicate distinct populations. In contrast, under the ecological paradigm morphological differentiation indicates distinct populations based on the low likelihood of demographic interactions between geographically distant individuals. The two sympatric but mitochondrially distinct haplogroups form a single population under the ecological paradigm. Hence, we find that the difference between evolutionary and ecological paradigms is the time-scale of interest, and we believe that the more chronologically inclusive evolutionary paradigm may be preferable except in cases where only a single generation is of interest. PMID:22393480

Evolution of precopulatory and post-copulatory strategies of inbreeding avoidance and associated polyandry.

PubMed

Duthie, A B; Bocedi, G; Germain, R R; Reid, J M

2018-01-01

Inbreeding depression is widely hypothesized to drive adaptive evolution of precopulatory and post-copulatory mechanisms of inbreeding avoidance, which in turn are hypothesized to affect evolution of polyandry (i.e. female multiple mating). However, surprisingly little theory or modelling critically examines selection for precopulatory or post-copulatory inbreeding avoidance, or both strategies, given evolutionary constraints and direct costs, or examines how evolution of inbreeding avoidance strategies might feed back to affect evolution of polyandry. Selection for post-copulatory inbreeding avoidance, but not for precopulatory inbreeding avoidance, requires polyandry, whereas interactions between precopulatory and post-copulatory inbreeding avoidance might cause functional redundancy (i.e. 'degeneracy') potentially generating complex evolutionary dynamics among inbreeding strategies and polyandry. We used individual-based modelling to quantify evolution of interacting precopulatory and post-copulatory inbreeding avoidance and associated polyandry given strong inbreeding depression and different evolutionary constraints and direct costs. We found that evolution of post-copulatory inbreeding avoidance increased selection for initially rare polyandry and that evolution of a costly inbreeding avoidance strategy became negligible over time given a lower-cost alternative strategy. Further, fixed precopulatory inbreeding avoidance often completely precluded evolution of polyandry and hence post-copulatory inbreeding avoidance, but fixed post-copulatory inbreeding avoidance did not preclude evolution of precopulatory inbreeding avoidance. Evolution of inbreeding avoidance phenotypes and associated polyandry is therefore affected by evolutionary feedbacks and degeneracy. All else being equal, evolution of precopulatory inbreeding avoidance and resulting low polyandry is more likely when post-copulatory inbreeding avoidance is precluded or costly, and evolution of post-copulatory inbreeding avoidance greatly facilitates evolution of costly polyandry. © The Authors. Journal of Evolutionary Biology published by John Wiley & Sons Ltd on behalf of European Society for Evolutionary Biology.

Ant aggression and evolutionary stability in plant-ant and plant-pollinator mutualistic interactions.

PubMed

Oña, L; Lachmann, M

2011-03-01

Mutualistic partners derive a benefit from their interaction, but this benefit can come at a cost. This is the case for plant-ant and plant-pollinator mutualistic associations. In exchange for protection from herbivores provided by the resident ants, plants supply various kinds of resources or nests to the ants. Most ant-myrmecophyte mutualisms are horizontally transmitted, and therefore, partners share an interest in growth but not in reproduction. This lack of alignment in fitness interests between plants and ants drives a conflict between them: ants can attack pollinators that cross-fertilize the host plants. Using a mathematical model, we define a threshold in ant aggressiveness determining pollinator survival or elimination on the host plant. In our model we observed that, all else being equal, facultative interactions result in pollinator extinction for lower levels of ant aggressiveness than obligatory interactions. We propose that the capacity to discriminate pollinators from herbivores should not often evolve in ants, and when it does it will be when the plants exhibit limited dispersal in an environment that is not seed saturated so that each seed produced can effectively generate a new offspring or if ants acquire an extra benefit from pollination (e.g. if ants eat fruit). We suggest specific mutualism examples where these hypotheses can be tested empirically. © 2010 The Authors. Journal of Evolutionary Biology © 2010 European Society For Evolutionary Biology.

Gene fusion analysis in the battle against the African endemic sleeping sickness.

PubMed

Trimpalis, Philip; Koumandou, Vassiliki Lila; Pliakou, Evangelia; Anagnou, Nicholas P; Kossida, Sophia

2013-01-01

The protozoan Trypanosoma brucei causes African Trypanosomiasis or sleeping sickness in humans, which can be lethal if untreated. Most available pharmacological treatments for the disease have severe side-effects. The purpose of this analysis was to detect novel protein-protein interactions (PPIs), vital for the parasite, which could lead to the development of drugs against this disease to block the specific interactions. In this work, the Domain Fusion Analysis (Rosetta Stone method) was used to identify novel PPIs, by comparing T. brucei to 19 organisms covering all major lineages of the tree of life. Overall, 49 possible protein-protein interactions were detected, and classified based on (a) statistical significance (BLAST e-value, domain length etc.), (b) their involvement in crucial metabolic pathways, and (c) their evolutionary history, particularly focusing on whether a protein pair is split in T. brucei and fused in the human host. We also evaluated fusion events including hypothetical proteins, and suggest a possible molecular function or involvement in a certain biological process. This work has produced valuable results which could be further studied through structural biology or other experimental approaches so as to validate the protein-protein interactions proposed here. The evolutionary analysis of the proteins involved showed that, gene fusion or gene fission events can happen in all organisms, while some protein domains are more prone to fusion and fission events and present complex evolutionary patterns.

Computer-automated evolution of an X-band antenna for NASA's Space Technology 5 mission.

PubMed

Hornby, Gregory S; Lohn, Jason D; Linden, Derek S

2011-01-01

Whereas the current practice of designing antennas by hand is severely limited because it is both time and labor intensive and requires a significant amount of domain knowledge, evolutionary algorithms can be used to search the design space and automatically find novel antenna designs that are more effective than would otherwise be developed. Here we present our work in using evolutionary algorithms to automatically design an X-band antenna for NASA's Space Technology 5 (ST5) spacecraft. Two evolutionary algorithms were used: the first uses a vector of real-valued parameters and the second uses a tree-structured generative representation for constructing the antenna. The highest-performance antennas from both algorithms were fabricated and tested and both outperformed a hand-designed antenna produced by the antenna contractor for the mission. Subsequent changes to the spacecraft orbit resulted in a change in requirements for the spacecraft antenna. By adjusting our fitness function we were able to rapidly evolve a new set of antennas for this mission in less than a month. One of these new antenna designs was built, tested, and approved for deployment on the three ST5 spacecraft, which were successfully launched into space on March 22, 2006. This evolved antenna design is the first computer-evolved antenna to be deployed for any application and is the first computer-evolved hardware in space.

Evidence for triclosan-induced activation of human and rodent xenobiotic nuclear receptors

EPA Science Inventory

The bacteriostat triclosan (2,4,40-trichloro-20-hydroxydiphenylether) (TCS) decreases rat serum thyroxine via putative nuclear receptor (NR) interaction(s) and subsequent transcriptional up-regulation of hepatic catabolism and clearance. However, due to the evolutionary divergenc...

Improving Search Properties in Genetic Programming

NASA Technical Reports Server (NTRS)

Janikow, Cezary Z.; DeWeese, Scott

1997-01-01

With the advancing computer processing capabilities, practical computer applications are mostly limited by the amount of human programming required to accomplish a specific task. This necessary human participation creates many problems, such as dramatically increased cost. To alleviate the problem, computers must become more autonomous. In other words, computers must be capable to program/reprogram themselves to adapt to changing environments/tasks/demands/domains. Evolutionary computation offers potential means, but it must be advanced beyond its current practical limitations. Evolutionary algorithms model nature. They maintain a population of structures representing potential solutions to the problem at hand. These structures undergo a simulated evolution by means of mutation, crossover, and a Darwinian selective pressure. Genetic programming (GP) is the most promising example of an evolutionary algorithm. In GP, the structures that evolve are trees, which is a dramatic departure from previously used representations such as strings in genetic algorithms. The space of potential trees is defined by means of their elements: functions, which label internal nodes, and terminals, which label leaves. By attaching semantic interpretation to those elements, trees can be interpreted as computer programs (given an interpreter), evolved architectures, etc. JSC has begun exploring GP as a potential tool for its long-term project on evolving dextrous robotic capabilities. Last year we identified representation redundancies as the primary source of inefficiency in GP. Subsequently, we proposed a method to use problem constraints to reduce those redundancies, effectively reducing GP complexity. This method was implemented afterwards at the University of Missouri. This summer, we have evaluated the payoff from using problem constraints to reduce search complexity on two classes of problems: learning boolean functions and solving the forward kinematics problem. We have also developed and implemented methods to use additional problem heuristics to fine-tune the searchable space, and to use typing information to further reduce the search space. Additional improvements have been proposed, but they are yet to be explored and implemented.

Between “design” and “bricolage”: Genetic networks, levels of selection, and adaptive evolution

PubMed Central

Wilkins, Adam S.

2007-01-01

The extent to which “developmental constraints” in complex organisms restrict evolutionary directions remains contentious. Yet, other forms of internal constraint, which have received less attention, may also exist. It will be argued here that a set of partial constraints below the level of phenotypes, those involving genes and molecules, influences and channels the set of possible evolutionary trajectories. At the top-most organizational level there are the genetic network modules, whose operations directly underlie complex morphological traits. The properties of these network modules, however, have themselves been set by the evolutionary history of the component genes and their interactions. Characterization of the components, structures, and operational dynamics of specific genetic networks should lead to a better understanding not only of the morphological traits they underlie but of the biases that influence the directions of evolutionary change. Furthermore, such knowledge may permit assessment of the relative degrees of probability of short evolutionary trajectories, those on the microevolutionary scale. In effect, a “network perspective” may help transform evolutionary biology into a scientific enterprise with greater predictive capability than it has hitherto possessed. PMID:17494754

Between "design" and "bricolage": genetic networks, levels of selection, and adaptive evolution.

PubMed

Wilkins, Adam S

2007-05-15

The extent to which "developmental constraints" in complex organisms restrict evolutionary directions remains contentious. Yet, other forms of internal constraint, which have received less attention, may also exist. It will be argued here that a set of partial constraints below the level of phenotypes, those involving genes and molecules, influences and channels the set of possible evolutionary trajectories. At the top-most organizational level there are the genetic network modules, whose operations directly underlie complex morphological traits. The properties of these network modules, however, have themselves been set by the evolutionary history of the component genes and their interactions. Characterization of the components, structures, and operational dynamics of specific genetic networks should lead to a better understanding not only of the morphological traits they underlie but of the biases that influence the directions of evolutionary change. Furthermore, such knowledge may permit assessment of the relative degrees of probability of short evolutionary trajectories, those on the microevolutionary scale. In effect, a "network perspective" may help transform evolutionary biology into a scientific enterprise with greater predictive capability than it has hitherto possessed.

An evolutionary game approach for determination of the structural conflicts in signed networks

PubMed Central

Tan, Shaolin; Lü, Jinhu

2016-01-01

Social or biochemical networks can often divide into two opposite alliances in response to structural conflicts between positive (friendly, activating) and negative (hostile, inhibiting) interactions. Yet, the underlying dynamics on how the opposite alliances are spontaneously formed to minimize the structural conflicts is still unclear. Here, we demonstrate that evolutionary game dynamics provides a felicitous possible tool to characterize the evolution and formation of alliances in signed networks. Indeed, an evolutionary game dynamics on signed networks is proposed such that each node can adaptively adjust its choice of alliances to maximize its own fitness, which yet leads to a minimization of the structural conflicts in the entire network. Numerical experiments show that the evolutionary game approach is universally efficient in quality and speed to find optimal solutions for all undirected or directed, unweighted or weighted signed networks. Moreover, the evolutionary game approach is inherently distributed. These characteristics thus suggest the evolutionary game dynamic approach as a feasible and effective tool for determining the structural conflicts in large-scale on-line signed networks. PMID:26915581

Coevolution study of mitochondria respiratory chain proteins: toward the understanding of protein--protein interaction.

PubMed

Yang, Ming; Ge, Yan; Wu, Jiayan; Xiao, Jingfa; Yu, Jun

2011-05-20

Coevolution can be seen as the interdependency between evolutionary histories. In the context of protein evolution, functional correlation proteins are ever-present coordinated evolutionary characters without disruption of organismal integrity. As to complex system, there are two forms of protein--protein interactions in vivo, which refer to inter-complex interaction and intra-complex interaction. In this paper, we studied the difference of coevolution characters between inter-complex interaction and intra-complex interaction using "Mirror tree" method on the respiratory chain (RC) proteins. We divided the correlation coefficients of every pairwise RC proteins into two groups corresponding to the binary protein--protein interaction in intra-complex and the binary protein--protein interaction in inter-complex, respectively. A dramatical discrepancy is detected between the coevolution characters of the two sets of protein interactions (Wilcoxon test, p-value = 4.4 × 10(-6)). Our finding reveals some critical information on coevolutionary study and assists the mechanical investigation of protein--protein interaction. Furthermore, the results also provide some unique clue for supramolecular organization of protein complexes in the mitochondrial inner membrane. More detailed binding sites map and genome information of nuclear encoded RC proteins will be extraordinary valuable for the further mitochondria dynamics study. Copyright © 2011. Published by Elsevier Ltd.

Topological enslavement in evolutionary games on correlated multiplex networks

NASA Astrophysics Data System (ADS)

Kleineberg, Kaj-Kolja; Helbing, Dirk

2018-05-01

Governments and enterprises strongly rely on incentives to generate favorable outcomes from social and strategic interactions between individuals. The incentives are usually modeled by payoffs in evolutionary games, such as the prisoners dilemma or the harmony game, with imitation dynamics. Adjusting the incentives by changing the payoff parameters can favor cooperation, as found in the harmony game, over defection, which prevails in the prisoner’s dilemma. Here, we show that this is not always the case if individuals engage in strategic interactions in multiple domains. In particular, we investigate evolutionary games on multiplex networks where individuals obtain an aggregate payoff. We explicitly control the strength of degree correlations between nodes in the different layers of the multiplex. We find that if the multiplex is composed of many layers and degree correlations are strong, the topology of the system enslaves the dynamics and the final outcome, cooperation or defection, becomes independent of the payoff parameters. The fate of the system is then determined by the initial conditions.

Structural dynamic interaction with solar tracking control for evolutionary Space Station concepts

NASA Technical Reports Server (NTRS)

Lim, Tae W.; Cooper, Paul A.; Ayers, J. Kirk

1992-01-01

The sun tracking control system design of the Solar Alpha Rotary Joint (SARJ) and the interaction of the control system with the flexible structure of Space Station Freedom (SSF) evolutionary concepts are addressed. The significant components of the space station pertaining to the SARJ control are described and the tracking control system design is presented. Finite element models representing two evolutionary concepts, enhanced operations capability (EOC) and extended operations capability (XOC), are employed to evaluate the influence of low frequency flexible structure on the control system design and performance. The design variables of the control system are synthesized using a constrained optimization technique to meet design requirements, to provide a given level of control system stability margin, and to achieve the most responsive tracking performance. The resulting SARJ control system design and performance of the EOC and XOC configurations are presented and compared to those of the SSF configuration. Performance limitations caused by the low frequency of the dominant flexible mode are discussed.

SpreaD3: Interactive Visualization of Spatiotemporal History and Trait Evolutionary Processes.

PubMed

Bielejec, Filip; Baele, Guy; Vrancken, Bram; Suchard, Marc A; Rambaut, Andrew; Lemey, Philippe

2016-08-01

Model-based phylogenetic reconstructions increasingly consider spatial or phenotypic traits in conjunction with sequence data to study evolutionary processes. Alongside parameter estimation, visualization of ancestral reconstructions represents an integral part of these analyses. Here, we present a complete overhaul of the spatial phylogenetic reconstruction of evolutionary dynamics software, now called SpreaD3 to emphasize the use of data-driven documents, as an analysis and visualization package that primarily complements Bayesian inference in BEAST (http://beast.bio.ed.ac.uk, last accessed 9 May 2016). The integration of JavaScript D3 libraries (www.d3.org, last accessed 9 May 2016) offers novel interactive web-based visualization capacities that are not restricted to spatial traits and extend to any discrete or continuously valued trait for any organism of interest. © The Author 2016. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

The impact of rapid evolution on population dynamics in the wild: experimental test of eco-evolutionary dynamics.

PubMed

Turcotte, Martin M; Reznick, David N; Hare, J Daniel

2011-11-01

Rapid evolution challenges the assumption that evolution is too slow to impact short-term ecological dynamics. This insight motivates the study of 'Eco-Evolutionary Dynamics' or how evolution and ecological processes reciprocally interact on short time scales. We tested how rapid evolution impacts concurrent population dynamics using an aphid (Myzus persicae) and an undomesticated host (Hirschfeldia incana) in replicated wild populations. We manipulated evolvability by creating non-evolving (single clone) and potentially evolving (two-clone) aphid populations that contained genetic variation in intrinsic growth rate. We observed significant evolution in two-clone populations whether or not they were exposed to predators and competitors. Evolving populations grew up to 42% faster and attained up to 67% higher density, compared with non-evolving control populations but only in treatments exposed to competitors and predators. Increased density also correlates with relative fitness of competing clones suggesting a full eco-evolutionary dynamic cycle defined as reciprocal interactions between evolution and density. © 2011 Blackwell Publishing Ltd/CNRS.

Emergence of a novel prey life history promotes contemporary sympatric diversification in a top predator.

PubMed

Brodersen, Jakob; Howeth, Jennifer G; Post, David M

2015-09-14

Intraspecific phenotypic variation can strongly impact community and ecosystem dynamics. Effects of intraspecific variation in keystone species have been shown to propagate down through the food web by altering the adaptive landscape for other species and creating a cascade of ecological and evolutionary change. However, similar bottom-up eco-evolutionary effects are poorly described. Here we show that life history diversification in a keystone prey species, the alewife (Alosa pseudoharengus), propagates up through the food web to promote phenotypic diversification in its native top predator, the chain pickerel (Esox niger), on contemporary timescales. The landlocking of alewife by human dam construction has repeatedly created a stable open water prey resource, novel to coastal lakes, that has promoted the parallel emergence of a habitat polymorphism in chain pickerel. Understanding how strong interactions propagate through food webs to influence diversification across multiple trophic levels is critical to understand eco-evolutionary interactions in complex natural ecosystems.

Quantitative genetic models of sexual conflict based on interacting phenotypes.

PubMed

Moore, Allen J; Pizzari, Tommaso

2005-05-01

Evolutionary conflict arises between reproductive partners when alternative reproductive opportunities are available. Sexual conflict can generate sexually antagonistic selection, which mediates sexual selection and intersexual coevolution. However, despite intense interest, the evolutionary implications of sexual conflict remain unresolved. We propose a novel theoretical approach to study the evolution of sexually antagonistic phenotypes based on quantitative genetics and the measure of social selection arising from male-female interactions. We consider the phenotype of one sex as both a genetically influenced evolving trait as well as the (evolving) social environment in which the phenotype of the opposite sex evolves. Several important points emerge from our analysis, including the relationship between direct selection on one sex and indirect effects through selection on the opposite sex. We suggest that the proposed approach may be a valuable tool to complement other theoretical approaches currently used to study sexual conflict. Most importantly, our approach highlights areas where additional empirical data can help clarify the role of sexual conflict in the evolutionary process.

Evolutionary Origin and Conserved Structural Building Blocks of Riboswitches and Ribosomal RNAs: Riboswitches as Probable Target Sites for Aminoglycosides Interaction.

PubMed

Mehdizadeh Aghdam, Elnaz; Barzegar, Abolfazl; Hejazi, Mohammad Saeid

2014-01-01

Riboswitches, as noncoding RNA sequences, control gene expression through direct ligand binding. Sporadic reports on the structural relation of riboswitches with ribosomal RNAs (rRNA), raises an interest in possible similarity between riboswitches and rRNAs evolutionary origins. Since aminoglycoside antibiotics affect microbial cells through binding to functional sites of the bacterial rRNA, finding any conformational and functional relation between riboswitches/rRNAs is utmost important in both of medicinal and basic research. Analysis of the riboswitches structures were carried out using bioinformatics and computational tools. The possible functional similarity of riboswitches with rRNAs was evaluated based on the affinity of paromomycin antibiotic (targeting "A site" of 16S rRNA) to riboswitches via docking method. There was high structural similarity between riboswitches and rRNAs, but not any particular sequence based similarity between them was found. The building blocks including "hairpin loop containing UUU", "peptidyl transferase center conserved hairpin A loop"," helix 45" and "S2 (G8) hairpin" as high identical rRNA motifs were detected in all kinds of riboswitches. Surprisingly, binding energies of paromomycin with different riboswitches are considerably better than the binding energy of paromomycin with "16S rRNA A site". Therefore the high affinity of paromomycin to bind riboswitches in comparison with rRNA "A site" suggests a new insight about riboswitches as possible targets for aminoglycoside antibiotics. These findings are considered as a possible supporting evidence for evolutionary origin of riboswitches/rRNAs and also their role in the exertion of antibiotics effects to design new drugs based on the concomitant effects via rRNA/riboswitches.

Elements of decisional dynamics: An agent-based approach applied to artificial financial market

NASA Astrophysics Data System (ADS)

Lucas, Iris; Cotsaftis, Michel; Bertelle, Cyrille

2018-02-01

This paper introduces an original mathematical description for describing agents' decision-making process in the case of problems affected by both individual and collective behaviors in systems characterized by nonlinear, path dependent, and self-organizing interactions. An application to artificial financial markets is proposed by designing a multi-agent system based on the proposed formalization. In this application, agents' decision-making process is based on fuzzy logic rules and the price dynamics is purely deterministic according to the basic matching rules of a central order book. Finally, while putting most parameters under evolutionary control, the computational agent-based system is able to replicate several stylized facts of financial time series (distributions of stock returns showing a heavy tail with positive excess kurtosis, absence of autocorrelations in stock returns, and volatility clustering phenomenon).

Elements of decisional dynamics: An agent-based approach applied to artificial financial market.

PubMed

Lucas, Iris; Cotsaftis, Michel; Bertelle, Cyrille

2018-02-01

This paper introduces an original mathematical description for describing agents' decision-making process in the case of problems affected by both individual and collective behaviors in systems characterized by nonlinear, path dependent, and self-organizing interactions. An application to artificial financial markets is proposed by designing a multi-agent system based on the proposed formalization. In this application, agents' decision-making process is based on fuzzy logic rules and the price dynamics is purely deterministic according to the basic matching rules of a central order book. Finally, while putting most parameters under evolutionary control, the computational agent-based system is able to replicate several stylized facts of financial time series (distributions of stock returns showing a heavy tail with positive excess kurtosis, absence of autocorrelations in stock returns, and volatility clustering phenomenon).

Robust electromagnetically guided endoscopic procedure using enhanced particle swarm optimization for multimodal information fusion

DOE Office of Scientific and Technical Information (OSTI.GOV)

Luo, Xiongbiao, E-mail: xluo@robarts.ca, E-mail: Ying.Wan@student.uts.edu.au; Wan, Ying, E-mail: xluo@robarts.ca, E-mail: Ying.Wan@student.uts.edu.au; He, Xiangjian

Purpose: Electromagnetically guided endoscopic procedure, which aims at accurately and robustly localizing the endoscope, involves multimodal sensory information during interventions. However, it still remains challenging in how to integrate these information for precise and stable endoscopic guidance. To tackle such a challenge, this paper proposes a new framework on the basis of an enhanced particle swarm optimization method to effectively fuse these information for accurate and continuous endoscope localization. Methods: The authors use the particle swarm optimization method, which is one of stochastic evolutionary computation algorithms, to effectively fuse the multimodal information including preoperative information (i.e., computed tomography images) asmore » a frame of reference, endoscopic camera videos, and positional sensor measurements (i.e., electromagnetic sensor outputs). Since the evolutionary computation method usually limits its possible premature convergence and evolutionary factors, the authors introduce the current (endoscopic camera and electromagnetic sensor’s) observation to boost the particle swarm optimization and also adaptively update evolutionary parameters in accordance with spatial constraints and the current observation, resulting in advantageous performance in the enhanced algorithm. Results: The experimental results demonstrate that the authors’ proposed method provides a more accurate and robust endoscopic guidance framework than state-of-the-art methods. The average guidance accuracy of the authors’ framework was about 3.0 mm and 5.6° while the previous methods show at least 3.9 mm and 7.0°. The average position and orientation smoothness of their method was 1.0 mm and 1.6°, which is significantly better than the other methods at least with (2.0 mm and 2.6°). Additionally, the average visual quality of the endoscopic guidance was improved to 0.29. Conclusions: A robust electromagnetically guided endoscopy framework was proposed on the basis of an enhanced particle swarm optimization method with using the current observation information and adaptive evolutionary factors. The authors proposed framework greatly reduced the guidance errors from (4.3, 7.8) to (3.0 mm, 5.6°), compared to state-of-the-art methods.« less

New Genetics

MedlinePlus

... Century-Old Evolutionary Puzzle Computing Genetics Model Organisms RNA Interference The New Genetics is a science education ... the basics of DNA and its molecular cousin RNA, and new directions in genetic research. The New ...

Mechanisms that Underlie Co-variation of the Brain and Face

PubMed Central

Marcucio, Ralph S.; Young, Nathan M.; Hu, Diane; Hallgrimsson, Benedikt

2011-01-01

The effect of the brain on the morphology of the face has long been recognized in both evolutionary biology and clinical medicine. In this paper we describe factors that are active between development of the brain and face and how these might impact craniofacial variation. First, there is the physical influence of the brain, which contributes to overall growth and morphology of the face through direct structural interactions. Second, there is the molecular influence of the brain, which signals to facial tissues to establish signaling centers that regulate patterned growth. Importantly, subtle alterations to these physical or molecular interactions may contribute to both normal and abnormal variation. These interactions are therefore critical to our understanding of how a diversity of facial morphologies can be generated both within species and across evolutionary time. PMID:21381182

Integration of structural dynamics and molecular evolution via protein interaction networks: a new era in genomic medicine.

PubMed

Kumar, Avishek; Butler, Brandon M; Kumar, Sudhir; Ozkan, S Banu

2015-12-01

Sequencing technologies are revealing many new non-synonymous single nucleotide variants (nsSNVs) in each personal exome. To assess their functional impacts, comparative genomics is frequently employed to predict if they are benign or not. However, evolutionary analysis alone is insufficient, because it misdiagnoses many disease-associated nsSNVs, such as those at positions involved in protein interfaces, and because evolutionary predictions do not provide mechanistic insights into functional change or loss. Structural analyses can aid in overcoming both of these problems by incorporating conformational dynamics and allostery in nSNV diagnosis. Finally, protein-protein interaction networks using systems-level methodologies shed light onto disease etiology and pathogenesis. Bridging these network approaches with structurally resolved protein interactions and dynamics will advance genomic medicine. Copyright © 2015 Elsevier Ltd. All rights reserved.

The butterfly plant arms-race escalated by gene and genome duplications

PubMed Central

Edger, Patrick P.; Heidel-Fischer, Hanna M.; Bekaert, Michaël; Rota, Jadranka; Glöckner, Gernot; Platts, Adrian E.; Heckel, David G.; Der, Joshua P.; Wafula, Eric K.; Tang, Michelle; Hofberger, Johannes A.; Smithson, Ann; Hall, Jocelyn C.; Blanchette, Matthieu; Bureau, Thomas E.; Wright, Stephen I.; dePamphilis, Claude W.; Eric Schranz, M.; Barker, Michael S.; Conant, Gavin C.; Wahlberg, Niklas; Vogel, Heiko; Pires, J. Chris; Wheat, Christopher W.

2015-01-01

Coevolutionary interactions are thought to have spurred the evolution of key innovations and driven the diversification of much of life on Earth. However, the genetic and evolutionary basis of the innovations that facilitate such interactions remains poorly understood. We examined the coevolutionary interactions between plants (Brassicales) and butterflies (Pieridae), and uncovered evidence for an escalating evolutionary arms-race. Although gradual changes in trait complexity appear to have been facilitated by allelic turnover, key innovations are associated with gene and genome duplications. Furthermore, we show that the origins of both chemical defenses and of molecular counter adaptations were associated with shifts in diversification rates during the arms-race. These findings provide an important connection between the origins of biodiversity, coevolution, and the role of gene and genome duplications as a substrate for novel traits. PMID:26100883

The butterfly plant arms-race escalated by gene and genome duplications.

PubMed

Edger, Patrick P; Heidel-Fischer, Hanna M; Bekaert, Michaël; Rota, Jadranka; Glöckner, Gernot; Platts, Adrian E; Heckel, David G; Der, Joshua P; Wafula, Eric K; Tang, Michelle; Hofberger, Johannes A; Smithson, Ann; Hall, Jocelyn C; Blanchette, Matthieu; Bureau, Thomas E; Wright, Stephen I; dePamphilis, Claude W; Eric Schranz, M; Barker, Michael S; Conant, Gavin C; Wahlberg, Niklas; Vogel, Heiko; Pires, J Chris; Wheat, Christopher W

2015-07-07

Coevolutionary interactions are thought to have spurred the evolution of key innovations and driven the diversification of much of life on Earth. However, the genetic and evolutionary basis of the innovations that facilitate such interactions remains poorly understood. We examined the coevolutionary interactions between plants (Brassicales) and butterflies (Pieridae), and uncovered evidence for an escalating evolutionary arms-race. Although gradual changes in trait complexity appear to have been facilitated by allelic turnover, key innovations are associated with gene and genome duplications. Furthermore, we show that the origins of both chemical defenses and of molecular counter adaptations were associated with shifts in diversification rates during the arms-race. These findings provide an important connection between the origins of biodiversity, coevolution, and the role of gene and genome duplications as a substrate for novel traits.

An Evolutionary Examination of Telemedicine: A Health and Computer-Mediated Communication Perspective

PubMed Central

Breen, Gerald-Mark; Matusitz, Jonathan

2009-01-01

Telemedicine, the use of advanced communication technologies in the healthcare context, has a rich history and a clear evolutionary course. In this paper, the authors identify telemedicine as operationally defined, the services and technologies it comprises, the direction telemedicine has taken, along with its increased acceptance in the healthcare communities. The authors also describe some of the key pitfalls warred with by researchers and activists to advance telemedicine to its full potential and lead to an unobstructed team of technicians to identify telemedicine’s diverse utilities. A discussion and future directions section is included to provide fresh ideas to health communication and computer-mediated scholars wishing to delve into this area and make a difference to enhance public understanding of this field. PMID:20300559

The Chomsky—Place correspondence 1993–1994

PubMed Central

Chomsky, Noam; Place, Ullin T.

2000-01-01

Edited correspondence between Ullin T. Place and Noam Chomsky, which occurred in 1993–1994, is presented. The principal topics are (a) deep versus surface structure; (b) computer modeling of the brain; (c) the evolutionary origins of language; (d) behaviorism; and (e) a dispositional account of language. This correspondence includes Chomsky's denial that he ever characterized deep structure as innate; Chomsky's critique of computer modeling (both traditional and connectionist) of the brain; Place's critique of Chomsky's alleged failure to provide an adequate account of the evolutionary origins of language, and Chomsky's response that such accounts are “pop-Darwinian fairy tales”; and Place's arguments for, and Chomsky's against, the relevance of behaviorism to linguistic theory, especially the relevance of a behavioral approach to language that is buttressed by a dispositional account of sentence construction. PMID:22477211

The Chomsky-Place correspondence 1993-1994.

PubMed

Chomsky, N; Place, U T

2000-01-01

Edited correspondence between Ullin T. Place and Noam Chomsky, which occurred in 1993-1994, is presented. The principal topics are (a) deep versus surface structure; (b) computer modeling of the brain; (c) the evolutionary origins of language; (d) behaviorism; and (e) a dispositional account of language. This correspondence includes Chomsky's denial that he ever characterized deep structure as innate; Chomsky's critique of computer modeling (both traditional and connectionist) of the brain; Place's critique of Chomsky's alleged failure to provide an adequate account of the evolutionary origins of language, and Chomsky's response that such accounts are "pop-Darwinian fairy tales"; and Place's arguments for, and Chomsky's against, the relevance of behaviorism to linguistic theory, especially the relevance of a behavioral approach to language that is buttressed by a dispositional account of sentence construction.

The development of the red giant branch. I - Theoretical evolutionary sequences

NASA Technical Reports Server (NTRS)

Sweigart, Allen V.; Greggio, Laura; Renzini, Alvio

1989-01-01

A grid of 100 evolutionary sequences extending from the zero-age main sequence to the onset of helium burning has been computed for stellar masses between 1.4 and 3.4 solar masses, helium abundances of 0.20 and 0.30, and heavy-element abundances of 0.004, 0.01, and 0.04. Using these computations the transition in the morphology of the red giant branch (RGB) between low-mass stars, which have an extended and luminous first RGB phase prior to helium ignition, and intermediate-mass stars, which do not, is investigated. Extensive tabulations of the numerical results are provided to aid in applying these sequences. The effects of the first dredge-up on the surface helium and CNO abundances of the sequences is discussed.

Comprehensive inventory of protein complexes in the Protein Data Bank from consistent classification of interfaces

DOE PAGES

Bordner, Andrew J.; Gorin, Andrey A.

2008-05-12

Here, protein-protein interactions are ubiquitous and essential for cellular processes. High-resolution X-ray crystallographic structures of protein complexes can elucidate the details of their function and provide a basis for many computational and experimental approaches. Here we demonstrate that existing annotations of protein complexes, including those provided by the Protein Data Bank (PDB) itself, contain a significant fraction of incorrect annotations. Results: We have developed a method for identifying protein complexes in the PDB X-ray structures by a four step procedure: (1) comprehensively collecting all protein-protein interfaces; (2) clustering similar protein-protein interfaces together; (3) estimating the probability that each cluster ismore » relevant based on a diverse set of properties; and (4) finally combining these scores for each entry in order to predict the complex structure. Unlike previous annotation methods, consistent prediction of complexes with identical or almost identical protein content is insured. The resulting clusters of biologically relevant interfaces provide a reliable catalog of evolutionary conserved protein-protein interactions.« less

Harnessing wake vortices for efficient collective swimming via deep reinfrcement learning

NASA Astrophysics Data System (ADS)

Verma, Siddartha; Novati, Guido; Koumoutsakos, Petros; ChairComputing Science Team

2017-11-01

Collective motion may bestow evolutionary advantages to a number of animal species. Soaring flocks of birds, teeming swarms of insects, and swirling masses of schooling fish, all to some extent enjoy anti-predator benefits, increased foraging success, and enhanced problem-solving abilities. Coordinated activity may also provide energetic benefits, as in the case of large groups of fish where swimmers exploit unsteady flow-patterns generated in the wake. Both experimental and computational investigations of such scenarios are hampered by difficulties associated with studying multiple swimmers. Consequentially, the precise energy-saving mechanisms at play remain largely unknown. We combine high-fidelity numerical simulations of multiple, self propelled swimmers with novel deep reinforcement learning algorithms to discover optimal ways for swimmers to interact with unsteady wakes, in a fully unsupervised manner. We identify optimal flow-interaction strategies devised by the resulting autonomous swimmers, and use it to formulate an effective control-logic. We demonstrate, via 3D simulations of controlled groups that swimmers exploiting the learned strategy exhibit a significant reduction in energy-expenditure. ERC Advanced Investigator Award 341117.

Petri net modeling of high-order genetic systems using grammatical evolution.

PubMed

Moore, Jason H; Hahn, Lance W

2003-11-01

Understanding how DNA sequence variations impact human health through a hierarchy of biochemical and physiological systems is expected to improve the diagnosis, prevention, and treatment of common, complex human diseases. We have previously developed a hierarchical dynamic systems approach based on Petri nets for generating biochemical network models that are consistent with genetic models of disease susceptibility. This modeling approach uses an evolutionary computation approach called grammatical evolution as a search strategy for optimal Petri net models. We have previously demonstrated that this approach routinely identifies biochemical network models that are consistent with a variety of genetic models in which disease susceptibility is determined by nonlinear interactions between two DNA sequence variations. In the present study, we evaluate whether the Petri net approach is capable of identifying biochemical networks that are consistent with disease susceptibility due to higher order nonlinear interactions between three DNA sequence variations. The results indicate that our model-building approach is capable of routinely identifying good, but not perfect, Petri net models. Ideas for improving the algorithm for this high-dimensional problem are presented.

Using THz Spectroscopy, Evolutionary Network Analysis Methods, and MD Simulation to Map the Evolution of Allosteric Communication Pathways in c-Type Lysozymes.

PubMed

Woods, Kristina N; Pfeffer, Juergen

2016-01-01

It is now widely accepted that protein function is intimately tied with the navigation of energy landscapes. In this framework, a protein sequence is not described by a distinct structure but rather by an ensemble of conformations. And it is through this ensemble that evolution is able to modify a protein's function by altering its landscape. Hence, the evolution of protein functions involves selective pressures that adjust the sampling of the conformational states. In this work, we focus on elucidating the evolutionary pathway that shaped the function of individual proteins that make-up the mammalian c-type lysozyme subfamily. Using both experimental and computational methods, we map out specific intermolecular interactions that direct the sampling of conformational states and accordingly, also underlie shifts in the landscape that are directly connected with the formation of novel protein functions. By contrasting three representative proteins in the family we identify molecular mechanisms that are associated with the selectivity of enhanced antimicrobial properties and consequently, divergent protein function. Namely, we link the extent of localized fluctuations involving the loop separating helices A and B with shifts in the equilibrium of the ensemble of conformational states that mediate interdomain coupling and concurrently moderate substrate binding affinity. This work reveals unique insights into the molecular level mechanisms that promote the progression of interactions that connect the immune response to infection with the nutritional properties of lactation, while also providing a deeper understanding about how evolving energy landscapes may define present-day protein function. © The Author 2015. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

Specialized Dynamical Properties of Promiscuous Residues Revealed by Simulated Conformational Ensembles

PubMed Central

2013-01-01

The ability to interact with different partners is one of the most important features in proteins. Proteins that bind a large number of partners (hubs) have been often associated with intrinsic disorder. However, many examples exist of hubs with an ordered structure, and evidence of a general mechanism promoting promiscuity in ordered proteins is still elusive. An intriguing hypothesis is that promiscuous binding sites have specific dynamical properties, distinct from the rest of the interface and pre-existing in the protein isolated state. Here, we present the first comprehensive study of the intrinsic dynamics of promiscuous residues in a large protein data set. Different computational methods, from coarse-grained elastic models to geometry-based sampling methods and to full-atom Molecular Dynamics simulations, were used to generate conformational ensembles for the isolated proteins. The flexibility and dynamic correlations of interface residues with a different degree of binding promiscuity were calculated and compared considering side chain and backbone motions, the latter both on a local and on a global scale. The study revealed that (a) promiscuous residues tend to be more flexible than nonpromiscuous ones, (b) this additional flexibility has a higher degree of organization, and (c) evolutionary conservation and binding promiscuity have opposite effects on intrinsic dynamics. Findings on simulated ensembles were also validated on ensembles of experimental structures extracted from the Protein Data Bank (PDB). Additionally, the low occurrence of single nucleotide polymorphisms observed for promiscuous residues indicated a tendency to preserve binding diversity at these positions. A case study on two ubiquitin-like proteins exemplifies how binding promiscuity in evolutionary related proteins can be modulated by the fine-tuning of the interface dynamics. The interplay between promiscuity and flexibility highlighted here can inspire new directions in protein–protein interaction prediction and design methods. PMID:24250278

Computational Analyses of an Evolutionary Arms Race between Mammalian Immunity Mediated by Immunoglobulin A and Its Subversion by Bacterial Pathogens

PubMed Central

Pinheiro, Ana; Woof, Jenny M.; Abi-Rached, Laurent; Parham, Peter; Esteves, Pedro J.

2013-01-01

IgA is the predominant immunoglobulin isotype in mucosal tissues and external secretions, playing important roles both in defense against pathogens and in maintenance of commensal microbiota. Considering the complexity of its interactions with the surrounding environment, IgA is a likely target for diversifying or positive selection. To investigate this possibility, the action of natural selection on IgA was examined in depth with six different methods: CODEML from the PAML package and the SLAC, FEL, REL, MEME and FUBAR methods implemented in the Datamonkey webserver. In considering just primate IgA, these analyses show that diversifying selection targeted five positions of the Cα1 and Cα2 domains of IgA. Extending the analysis to include other mammals identified 18 positively selected sites: ten in Cα1, five in Cα2 and three in Cα3. All but one of these positions display variation in polarity and charge. Their structural locations suggest they indirectly influence the conformation of sites on IgA that are critical for interaction with host IgA receptors and also with proteins produced by mucosal pathogens that prevent their elimination by IgA-mediated effector mechanisms. Demonstrating the plasticity of IgA in the evolution of different groups of mammals, only two of the eighteen selected positions in all mammals are included in the five selected positions in primates. That IgA residues subject to positive selection impact sites targeted both by host receptors and subversive pathogen ligands highlights the evolutionary arms race playing out between mammals and pathogens, and further emphasizes the importance of IgA in protection against mucosal pathogens. PMID:24019941

Systems level analysis of the Chlamydomonas reinhardtii metabolic network reveals variability in evolutionary co-conservation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chaiboonchoe, Amphun; Ghamsari, Lila; Dohai, Bushra

Metabolic networks, which are mathematical representations of organismal metabolism, are reconstructed to provide computational platforms to guide metabolic engineering experiments and explore fundamental questions on metabolism. Systems level analyses, such as interrogation of phylogenetic relationships within the network, can provide further guidance on the modification of metabolic circuitries. Chlamydomonas reinhardtii, a biofuel relevant green alga that has retained key genes with plant, animal, and protist affinities, serves as an ideal model organism to investigate the interplay between gene function and phylogenetic affinities at multiple organizational levels. Here, using detailed topological and functional analyses, coupled with transcriptomics studies on a metabolicmore » network that we have reconstructed for C. reinhardtii, we show that network connectivity has a significant concordance with the co-conservation of genes; however, a distinction between topological and functional relationships is observable within the network. Dynamic and static modes of co-conservation were defined and observed in a subset of gene-pairs across the network topologically. In contrast, genes with predicted synthetic interactions, or genes involved in coupled reactions, show significant enrichment for both shorter and longer phylogenetic distances. Based on our results, we propose that the metabolic network of C. reinhardtii is assembled with an architecture to minimize phylogenetic profile distances topologically, while it includes an expansion of such distances for functionally interacting genes. This arrangement may increase the robustness of C. reinhardtii's network in dealing with varied environmental challenges that the species may face. As a result, the defined evolutionary constraints within the network, which identify important pairings of genes in metabolism, may offer guidance on synthetic biology approaches to optimize the production of desirable metabolites.« less

Cysteine-rich domains related to Frizzled receptors and Hedgehog-interacting proteins

PubMed Central

Pei, Jimin; Grishin, Nick V

2012-01-01

Frizzled and Smoothened are homologous seven-transmembrane proteins functioning in the Wnt and Hedgehog signaling pathways, respectively. They harbor an extracellular cysteine-rich domain (FZ-CRD), a mobile evolutionary unit that has been found in a number of other metazoan proteins and Frizzled-like proteins in Dictyostelium. Domains distantly related to FZ-CRDs, in Hedgehog-interacting proteins (HHIPs), folate receptors and riboflavin-binding proteins (FRBPs), and Niemann-Pick Type C1 proteins (NPC1s), referred to as HFN-CRDs, exhibit similar structures and disulfide connectivity patterns compared with FZ-CRDs. We used computational analyses to expand the homologous set of FZ-CRDs and HFN-CRDs, providing a better understanding of their evolution and classification. First, FZ-CRD-containing proteins with various domain compositions were identified in several major eukaryotic lineages including plants and Chromalveolata, revealing a wider phylogenetic distribution of FZ-CRDs than previously recognized. Second, two new and distinct groups of highly divergent FZ-CRDs were found by sensitive similarity searches. One of them is present in the calcium channel component Mid1 in fungi and the uncharacterized FAM155 proteins in metazoans. Members of the other new FZ-CRD group occur in the metazoan-specific RECK (reversion-inducing-cysteine-rich protein with Kazal motifs) proteins that are putative tumor suppressors acting as inhibitors of matrix metalloproteases. Finally, sequence and three-dimensional structural comparisons helped us uncover a divergent HFN-CRD in glypicans, which are important morphogen-binding heparan sulfate proteoglycans. Such a finding reinforces the evolutionary ties between the Wnt and Hedgehog signaling pathways and underscores the importance of gene duplications in creating essential signaling components in metazoan evolution. PMID:22693159

Systems level analysis of the Chlamydomonas reinhardtii metabolic network reveals variability in evolutionary co-conservation.

PubMed

Chaiboonchoe, Amphun; Ghamsari, Lila; Dohai, Bushra; Ng, Patrick; Khraiwesh, Basel; Jaiswal, Ashish; Jijakli, Kenan; Koussa, Joseph; Nelson, David R; Cai, Hong; Yang, Xinping; Chang, Roger L; Papin, Jason; Yu, Haiyuan; Balaji, Santhanam; Salehi-Ashtiani, Kourosh

2016-07-19

Metabolic networks, which are mathematical representations of organismal metabolism, are reconstructed to provide computational platforms to guide metabolic engineering experiments and explore fundamental questions on metabolism. Systems level analyses, such as interrogation of phylogenetic relationships within the network, can provide further guidance on the modification of metabolic circuitries. Chlamydomonas reinhardtii, a biofuel relevant green alga that has retained key genes with plant, animal, and protist affinities, serves as an ideal model organism to investigate the interplay between gene function and phylogenetic affinities at multiple organizational levels. Here, using detailed topological and functional analyses, coupled with transcriptomics studies on a metabolic network that we have reconstructed for C. reinhardtii, we show that network connectivity has a significant concordance with the co-conservation of genes; however, a distinction between topological and functional relationships is observable within the network. Dynamic and static modes of co-conservation were defined and observed in a subset of gene-pairs across the network topologically. In contrast, genes with predicted synthetic interactions, or genes involved in coupled reactions, show significant enrichment for both shorter and longer phylogenetic distances. Based on our results, we propose that the metabolic network of C. reinhardtii is assembled with an architecture to minimize phylogenetic profile distances topologically, while it includes an expansion of such distances for functionally interacting genes. This arrangement may increase the robustness of C. reinhardtii's network in dealing with varied environmental challenges that the species may face. The defined evolutionary constraints within the network, which identify important pairings of genes in metabolism, may offer guidance on synthetic biology approaches to optimize the production of desirable metabolites.

Systems level analysis of the Chlamydomonas reinhardtii metabolic network reveals variability in evolutionary co-conservation

DOE PAGES

Chaiboonchoe, Amphun; Ghamsari, Lila; Dohai, Bushra; ...

2016-06-14

Metabolic networks, which are mathematical representations of organismal metabolism, are reconstructed to provide computational platforms to guide metabolic engineering experiments and explore fundamental questions on metabolism. Systems level analyses, such as interrogation of phylogenetic relationships within the network, can provide further guidance on the modification of metabolic circuitries. Chlamydomonas reinhardtii, a biofuel relevant green alga that has retained key genes with plant, animal, and protist affinities, serves as an ideal model organism to investigate the interplay between gene function and phylogenetic affinities at multiple organizational levels. Here, using detailed topological and functional analyses, coupled with transcriptomics studies on a metabolicmore » network that we have reconstructed for C. reinhardtii, we show that network connectivity has a significant concordance with the co-conservation of genes; however, a distinction between topological and functional relationships is observable within the network. Dynamic and static modes of co-conservation were defined and observed in a subset of gene-pairs across the network topologically. In contrast, genes with predicted synthetic interactions, or genes involved in coupled reactions, show significant enrichment for both shorter and longer phylogenetic distances. Based on our results, we propose that the metabolic network of C. reinhardtii is assembled with an architecture to minimize phylogenetic profile distances topologically, while it includes an expansion of such distances for functionally interacting genes. This arrangement may increase the robustness of C. reinhardtii's network in dealing with varied environmental challenges that the species may face. As a result, the defined evolutionary constraints within the network, which identify important pairings of genes in metabolism, may offer guidance on synthetic biology approaches to optimize the production of desirable metabolites.« less

Toward a method for tracking virus evolutionary trajectory applied to the pandemic H1N1 2009 influenza virus.

PubMed

Squires, R Burke; Pickett, Brett E; Das, Sajal; Scheuermann, Richard H

2014-12-01

In 2009 a novel pandemic H1N1 influenza virus (H1N1pdm09) emerged as the first official influenza pandemic of the 21st century. Early genomic sequence analysis pointed to the swine origin of the virus. Here we report a novel computational approach to determine the evolutionary trajectory of viral sequences that uses data-driven estimations of nucleotide substitution rates to track the gradual accumulation of observed sequence alterations over time. Phylogenetic analysis and multiple sequence alignments show that sequences belonging to the resulting evolutionary trajectory of the H1N1pdm09 lineage exhibit a gradual accumulation of sequence variations and tight temporal correlations in the topological structure of the phylogenetic trees. These results suggest that our evolutionary trajectory analysis (ETA) can more effectively pinpoint the evolutionary history of viruses, including the host and geographical location traversed by each segment, when compared against either BLAST or traditional phylogenetic analysis alone. Copyright © 2014 Elsevier B.V. All rights reserved.

An Orthogonal Evolutionary Algorithm With Learning Automata for Multiobjective Optimization.

PubMed

Dai, Cai; Wang, Yuping; Ye, Miao; Xue, Xingsi; Liu, Hailin

2016-12-01

Research on multiobjective optimization problems becomes one of the hottest topics of intelligent computation. In order to improve the search efficiency of an evolutionary algorithm and maintain the diversity of solutions, in this paper, the learning automata (LA) is first used for quantization orthogonal crossover (QOX), and a new fitness function based on decomposition is proposed to achieve these two purposes. Based on these, an orthogonal evolutionary algorithm with LA for complex multiobjective optimization problems with continuous variables is proposed. The experimental results show that in continuous states, the proposed algorithm is able to achieve accurate Pareto-optimal sets and wide Pareto-optimal fronts efficiently. Moreover, the comparison with the several existing well-known algorithms: nondominated sorting genetic algorithm II, decomposition-based multiobjective evolutionary algorithm, decomposition-based multiobjective evolutionary algorithm with an ensemble of neighborhood sizes, multiobjective optimization by LA, and multiobjective immune algorithm with nondominated neighbor-based selection, on 15 multiobjective benchmark problems, shows that the proposed algorithm is able to find more accurate and evenly distributed Pareto-optimal fronts than the compared ones.

Autonomous self-organizing resource manager for multiple networked platforms

NASA Astrophysics Data System (ADS)

Smith, James F., III

2002-08-01

A fuzzy logic based expert system for resource management has been developed that automatically allocates electronic attack (EA) resources in real-time over many dissimilar autonomous naval platforms defending their group against attackers. The platforms can be very general, e.g., ships, planes, robots, land based facilities, etc. Potential foes the platforms deal with can also be general. This paper provides an overview of the resource manager including the four fuzzy decision trees that make up the resource manager; the fuzzy EA model; genetic algorithm based optimization; co-evolutionary data mining through gaming; and mathematical, computational and hardware based validation. Methods of automatically designing new multi-platform EA techniques are considered. The expert system runs on each defending platform rendering it an autonomous system requiring no human intervention. There is no commanding platform. Instead the platforms work cooperatively as a function of battlespace geometry; sensor data such as range, bearing, ID, uncertainty measures for sensor output; intelligence reports; etc. Computational experiments will show the defending networked platform's ability to self- organize. The platforms' ability to self-organize is illustrated through the output of the scenario generator, a software package that automates the underlying data mining problem and creates a computer movie of the platforms' interaction for evaluation.

Insights into the fold organization of TIM barrel from interaction energy based structure networks.

PubMed

Vijayabaskar, M S; Vishveshwara, Saraswathi

2012-01-01

There are many well-known examples of proteins with low sequence similarity, adopting the same structural fold. This aspect of sequence-structure relationship has been extensively studied both experimentally and theoretically, however with limited success. Most of the studies consider remote homology or "sequence conservation" as the basis for their understanding. Recently "interaction energy" based network formalism (Protein Energy Networks (PENs)) was developed to understand the determinants of protein structures. In this paper we have used these PENs to investigate the common non-covalent interactions and their collective features which stabilize the TIM barrel fold. We have also developed a method of aligning PENs in order to understand the spatial conservation of interactions in the fold. We have identified key common interactions responsible for the conservation of the TIM fold, despite high sequence dissimilarity. For instance, the central beta barrel of the TIM fold is stabilized by long-range high energy electrostatic interactions and low-energy contiguous vdW interactions in certain families. The other interfaces like the helix-sheet or the helix-helix seem to be devoid of any high energy conserved interactions. Conserved interactions in the loop regions around the catalytic site of the TIM fold have also been identified, pointing out their significance in both structural and functional evolution. Based on these investigations, we have developed a novel network based phylogenetic analysis for remote homologues, which can perform better than sequence based phylogeny. Such an analysis is more meaningful from both structural and functional evolutionary perspective. We believe that the information obtained through the "interaction conservation" viewpoint and the subsequently developed method of structure network alignment, can shed new light in the fields of fold organization and de novo computational protein design.

Development of an Evolutionary Algorithm for the ab Initio Discovery of Two-Dimensional Materials

NASA Astrophysics Data System (ADS)

Revard, Benjamin Charles

Crystal structure prediction is an important first step on the path toward computational materials design. Increasingly robust methods have become available in recent years for computing many materials properties, but because properties are largely a function of crystal structure, the structure must be known before these methods can be brought to bear. In addition, structure prediction is particularly useful for identifying low-energy structures of subperiodic materials, such as two-dimensional (2D) materials, which may adopt unexpected structures that differ from those of the corresponding bulk phases. Evolutionary algorithms, which are heuristics for global optimization inspired by biological evolution, have proven to be a fruitful approach for tackling the problem of crystal structure prediction. This thesis describes the development of an improved evolutionary algorithm for structure prediction and several applications of the algorithm to predict the structures of novel low-energy 2D materials. The first part of this thesis contains an overview of evolutionary algorithms for crystal structure prediction and presents our implementation, including details of extending the algorithm to search for clusters, wires, and 2D materials, improvements to efficiency when running in parallel, improved composition space sampling, and the ability to search for partial phase diagrams. We then present several applications of the evolutionary algorithm to 2D systems, including InP, the C-Si and Sn-S phase diagrams, and several group-IV dioxides. This thesis makes use of the Cornell graduate school's "papers" option. Chapters 1 and 3 correspond to the first-author publications of Refs. [131] and [132], respectively, and chapter 2 will soon be submitted as a first-author publication. The material in chapter 4 is taken from Ref. [144], in which I share joint first-authorship. In this case I have included only my own contributions.

A Penalized Likelihood Framework For High-Dimensional Phylogenetic Comparative Methods And An Application To New-World Monkeys Brain Evolution.

PubMed

Julien, Clavel; Leandro, Aristide; Hélène, Morlon

2018-06-19

Working with high-dimensional phylogenetic comparative datasets is challenging because likelihood-based multivariate methods suffer from low statistical performances as the number of traits p approaches the number of species n and because some computational complications occur when p exceeds n. Alternative phylogenetic comparative methods have recently been proposed to deal with the large p small n scenario but their use and performances are limited. Here we develop a penalized likelihood framework to deal with high-dimensional comparative datasets. We propose various penalizations and methods for selecting the intensity of the penalties. We apply this general framework to the estimation of parameters (the evolutionary trait covariance matrix and parameters of the evolutionary model) and model comparison for the high-dimensional multivariate Brownian (BM), Early-burst (EB), Ornstein-Uhlenbeck (OU) and Pagel's lambda models. We show using simulations that our penalized likelihood approach dramatically improves the estimation of evolutionary trait covariance matrices and model parameters when p approaches n, and allows for their accurate estimation when p equals or exceeds n. In addition, we show that penalized likelihood models can be efficiently compared using Generalized Information Criterion (GIC). We implement these methods, as well as the related estimation of ancestral states and the computation of phylogenetic PCA in the R package RPANDA and mvMORPH. Finally, we illustrate the utility of the new proposed framework by evaluating evolutionary models fit, analyzing integration patterns, and reconstructing evolutionary trajectories for a high-dimensional 3-D dataset of brain shape in the New World monkeys. We find a clear support for an Early-burst model suggesting an early diversification of brain morphology during the ecological radiation of the clade. Penalized likelihood offers an efficient way to deal with high-dimensional multivariate comparative data.

Energy and time determine scaling in biological and computer designs

PubMed Central

Bezerra, George; Edwards, Benjamin; Brown, James; Forrest, Stephanie

2016-01-01

Metabolic rate in animals and power consumption in computers are analogous quantities that scale similarly with size. We analyse vascular systems of mammals and on-chip networks of microprocessors, where natural selection and human engineering, respectively, have produced systems that minimize both energy dissipation and delivery times. Using a simple network model that simultaneously minimizes energy and time, our analysis explains empirically observed trends in the scaling of metabolic rate in mammals and power consumption and performance in microprocessors across several orders of magnitude in size. Just as the evolutionary transitions from unicellular to multicellular animals in biology are associated with shifts in metabolic scaling, our model suggests that the scaling of power and performance will change as computer designs transition to decentralized multi-core and distributed cyber-physical systems. More generally, a single energy–time minimization principle may govern the design of many complex systems that process energy, materials and information. This article is part of the themed issue ‘The major synthetic evolutionary transitions’. PMID:27431524

Nemo: an evolutionary and population genetics programming framework.

PubMed

Guillaume, Frédéric; Rougemont, Jacques

2006-10-15

Nemo is an individual-based, genetically explicit and stochastic population computer program for the simulation of population genetics and life-history trait evolution in a metapopulation context. It comes as both a C++ programming framework and an executable program file. Its object-oriented programming design gives it the flexibility and extensibility needed to implement a large variety of forward-time evolutionary models. It provides developers with abstract models allowing them to implement their own life-history traits and life-cycle events. Nemo offers a large panel of population models, from the Island model to lattice models with demographic or environmental stochasticity and a variety of already implemented traits (deleterious mutations, neutral markers and more), life-cycle events (mating, dispersal, aging, selection, etc.) and output operators for saving data and statistics. It runs on all major computer platforms including parallel computing environments. The source code, binaries and documentation are available under the GNU General Public License at http://nemo2.sourceforge.net.