The Rank Ordering of Genotypic Fitness Values Predicts Genetic Constraint on Natural Selection on Landscapes Lacking Sign Epistasis
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Transformations of Lamarckism Vienna Series in Theoretical Biology Gerd B
Transformations of Lamarckism Vienna Series in Theoretical Biology Gerd B. M ü ller, G ü nter P. Wagner, and Werner Callebaut, editors The Evolution of Cognition , edited by Cecilia Heyes and Ludwig Huber, 2000 Origination of Organismal Form: Beyond the Gene in Development and Evolutionary Biology , edited by Gerd B. M ü ller and Stuart A. Newman, 2003 Environment, Development, and Evolution: Toward a Synthesis , edited by Brian K. Hall, Roy D. Pearson, and Gerd B. M ü ller, 2004 Evolution of Communication Systems: A Comparative Approach , edited by D. Kimbrough Oller and Ulrike Griebel, 2004 Modularity: Understanding the Development and Evolution of Natural Complex Systems , edited by Werner Callebaut and Diego Rasskin-Gutman, 2005 Compositional Evolution: The Impact of Sex, Symbiosis, and Modularity on the Gradualist Framework of Evolution , by Richard A. Watson, 2006 Biological Emergences: Evolution by Natural Experiment , by Robert G. B. Reid, 2007 Modeling Biology: Structure, Behaviors, Evolution , edited by Manfred D. Laubichler and Gerd B. M ü ller, 2007 Evolution of Communicative Flexibility: Complexity, Creativity, and Adaptability in Human and Animal Communication , edited by Kimbrough D. Oller and Ulrike Griebel, 2008 Functions in Biological and Artifi cial Worlds: Comparative Philosophical Perspectives , edited by Ulrich Krohs and Peter Kroes, 2009 Cognitive Biology: Evolutionary and Developmental Perspectives on Mind, Brain, and Behavior , edited by Luca Tommasi, Mary A. Peterson, and Lynn Nadel, 2009 Innovation in Cultural Systems: Contributions from Evolutionary Anthropology , edited by Michael J. O ’ Brien and Stephen J. Shennan, 2010 The Major Transitions in Evolution Revisited , edited by Brett Calcott and Kim Sterelny, 2011 Transformations of Lamarckism: From Subtle Fluids to Molecular Biology , edited by Snait B. -
Evolving Neural Networks That Are Both Modular and Regular: Hyperneat Plus the Connection Cost Technique Joost Huizinga, Jean-Baptiste Mouret, Jeff Clune
Evolving Neural Networks That Are Both Modular and Regular: HyperNeat Plus the Connection Cost Technique Joost Huizinga, Jean-Baptiste Mouret, Jeff Clune To cite this version: Joost Huizinga, Jean-Baptiste Mouret, Jeff Clune. Evolving Neural Networks That Are Both Modular and Regular: HyperNeat Plus the Connection Cost Technique. GECCO ’14: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation, ACM, 2014, Vancouver, Canada. pp.697-704. hal-01300699 HAL Id: hal-01300699 https://hal.archives-ouvertes.fr/hal-01300699 Submitted on 11 Apr 2016 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. To appear in: Proceedings of the Genetic and Evolutionary Computation Conference. 2014 Evolving Neural Networks That Are Both Modular and Regular: HyperNeat Plus the Connection Cost Technique Joost Huizinga Jean-Baptiste Mouret Jeff Clune Evolving AI Lab ISIR, Université Pierre et Evolving AI Lab Department of Computer Marie Curie-Paris 6 Department of Computer Science CNRS UMR 7222 Science University of Wyoming Paris, France University of Wyoming [email protected] [email protected] [email protected] ABSTRACT 1. INTRODUCTION One of humanity’s grand scientific challenges is to create ar- An open and ambitious question in the field of evolution- tificially intelligent robots that rival natural animals in intel- ary robotics is how to produce robots that possess the intel- ligence and agility. -
High Heritability of Telomere Length, but Low Evolvability, and No Significant
bioRxiv preprint doi: https://doi.org/10.1101/2020.12.16.423128; this version posted December 16, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 1 High heritability of telomere length, but low evolvability, and no significant 2 heritability of telomere shortening in wild jackdaws 3 4 Christina Bauch1*, Jelle J. Boonekamp1,2, Peter Korsten3, Ellis Mulder1, Simon Verhulst1 5 6 Affiliations: 7 1 Groningen Institute for Evolutionary Life Sciences, University of Groningen, Nijenborgh 7, 8 9747AG Groningen, The Netherlands 9 2 present address: Institute of Biodiversity Animal Health & Comparative Medicine, College 10 of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G12 8QQ, United 11 Kingdom 12 3 Department of Animal Behaviour, Bielefeld University, Morgenbreede 45, 33615 Bielefeld, 13 Germany 14 15 16 * Correspondence to: 17 [email protected] 18 19 Running head: High heritability of telomere length bioRxiv preprint doi: https://doi.org/10.1101/2020.12.16.423128; this version posted December 16, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 20 Abstract 21 Telomere length (TL) and shortening rate predict survival in many organisms. Evolutionary 22 dynamics of TL in response to survival selection depend on the presence of genetic variation 23 that selection can act upon. However, the amount of standing genetic variation is poorly known 24 for both TL and TL shortening rate, and has not been studied for both traits in combination in 25 a wild vertebrate. -
12 Evolvability of Real Functions
i i i i Evolvability of Real Functions PAUL VALIANT , Brown University We formulate a notion of evolvability for functions with domain and range that are real-valued vectors, a compelling way of expressing many natural biological processes. We show that linear and fixed-degree polynomial functions are evolvable in the following dually-robust sense: There is a single evolution algorithm that, for all convex loss functions, converges for all distributions. It is possible that such dually-robust results can be achieved by simpler and more-natural evolution algorithms. Towards this end, we introduce a simple and natural algorithm that we call wide-scale random noise and prove a corresponding result for the L2 metric. We conjecture that the algorithm works for a more general class of metrics. 12 Categories and Subject Descriptors: F.1.1 [Computation by Abstract Devices]: Models of Computa- tion—Self-modifying machines; G.1.6 [Numerical Analysis]: Optimization—Convex programming; I.2.6 [Artificial Intelligence]: Learning—Concept learning;J.3[Computer Applications]: Life and Medical Sciences—Biology and genetics General Terms: Algorithms, Theory Additional Key Words and Phrases: Evolvability, optimization ACM Reference Format: Valiant, P. 2014. Evolvability of real functions. ACM Trans. Comput. Theory 6, 3, Article 12 (July 2014), 19 pages. DOI:http://dx.doi.org/10.1145/2633598 1. INTRODUCTION Since Darwin first assembled the empirical facts that pointed to evolution as the source of the hugely diverse and yet finely honed mechanisms of life, there has been the mys- tery of how the biochemical mechanisms of evolution (indeed, how any mechanism) can apparently consistently succeed at finding innovative solutions to the unexpected problems of life. -
The University of Chicago Epistasis, Contingency, And
THE UNIVERSITY OF CHICAGO EPISTASIS, CONTINGENCY, AND EVOLVABILITY IN THE SEQUENCE SPACE OF ANCIENT PROTEINS A DISSERTATION SUBMITTED TO THE FACULTY OF THE DIVISION OF THE BIOLOGICAL SCIENCES AND THE PRITZKER SCHOOL OF MEDICINE IN CANDIDACY FOR THE DEGREE OF DOCTOR OF PHILOSOPHY GRADUATE PROGRAM IN BIOCHEMISTRY AND MOLECULAR BIOPHYSICS BY TYLER NELSON STARR CHICAGO, ILLINOIS AUGUST 2018 Table of Contents List of Figures .................................................................................................................... iv List of Tables ..................................................................................................................... vi Acknowledgements ........................................................................................................... vii Abstract .............................................................................................................................. ix Chapter 1 Introduction ......................................................................................................1 1.1 Sequence space and protein evolution .............................................................1 1.2 Deep mutational scanning ................................................................................2 1.3 Epistasis ...........................................................................................................3 1.4 Chance and determinism ..................................................................................4 1.5 Evolvability ......................................................................................................6 -
What Can We Learn from Fitness Landscapes?
What can we learn from fitness landscapes? The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Hartl, Daniel L. 2014. “What Can We Learn from Fitness Landscapes?” Current Opinion in Microbiology 21 (October): 51–57. doi:10.1016/j.mib.2014.08.001. Published Version 10.1016/j.mib.2014.08.001 Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:22898356 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Open Access Policy Articles, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#OAP Elsevier Editorial System(tm) for Current Opinion in Microbiology Manuscript Draft Manuscript Number: Title: What Can We Learn From Fitness Landscapes? Article Type: 22 Growth&Develop: prokaryotes (2014 Corresponding Author: Dr. Daniel Hartl, Corresponding Author's Institution: First Author: Daniel Hartl Order of Authors: Daniel Hartl Manuscript Click here to view linked References What Can We Learn From Fitness Landscapes? Daniel L. Hartl Department of Organismic and Evolutionary Biology Harvard University Cambridge, Massachusetts 02138 USA Contact information: Email: [email protected], TEL: 617-396-3917 In evolutionary biology, the fitness landscape of set of mutants is the mapping of genotypes onto phenotypes when the phenotype is fitness or some proxy for fitness such as growth rate or drug resistance. When the set of mutants is not too large, it is possible to create every possible combination of mutants and map these to fitness. -
1. Basis of Fitness Landscape
Optimisation Origin and definition of fitness landscape Position and goal 1. Basis of fitness landscape Fitness landscape analysis for understanding and designing local search heuristics Sebastien´ Verel LISIC - Universit´edu Littoral C^oted'Opale, Calais, France http://www-lisic.univ-littoral.fr/~verel/ The 51st CREST Open Workshop Tutorial on Landscape Analysis University College London 27th, February, 2017 Optimisation Origin and definition of fitness landscape Position and goal Outline of this part Basis of fitness landscape : introductory example (Done) brief history and background of fitness landscape fundamental definitions Optimisation Origin and definition of fitness landscape Position and goal Mono-objective Optimization Search space : set of candidate solutions X Objective fonction : quality criterion (or non-quality) f : X ! IR X discrete : combinatorial optimization X ⊂ IRn : numerical optimization Solve an optimization problem (maximization) ? X = argmaxX f or find an approximation of X ?. Optimisation Origin and definition of fitness landscape Position and goal Context : black-box optimization x −! −! f (x) No information on the objective definition function f Objective fonction : can be irregular, non continuous, non differentiable, etc. given by a computation or a simulation Optimisation Origin and definition of fitness landscape Position and goal Real-world black-box optimization : first example PhD of Mathieu Muniglia, Saclay Nuclear Research Centre (CEA), Paris x −! −! f (x) (73;:::; 8) −! −! ∆z P Multi-physic simulator Optimisation Origin and definition of fitness landscape Position and goal Search algorithms Principle Enumeration of the search space A lot of ways to enumerate the search space Using exact method : A?, Branch&Bound, etc. Using random sampling : Monte Carlo technics, approx. -
Predictability of a Large Intragenic Fitness Landscape
On the (un)predictability of a large intragenic fitness landscape Claudia Banka,b,c,1, Sebastian Matuszewskib,c,1, Ryan T. Hietpasd,e, and Jeffrey D. Jensenb,c,2,3 aInstituto Gulbenkian de Ciencia,ˆ 2780-156 Oeiras, Portugal; bSchool of Life Sciences, Ecole Polytechnique Fed´ erale´ de Lausanne, 1015 Lausanne, Switzerland; cSwiss Institute of Bioinformatics, 1015 Lausanne, Switzerland; dEli Lilly and Company, Indianapolis, IN 46225; and eDepartment of Biochemistry & Molecular Pharmacology, University of Massachusetts Medical School, Worcester, MA 01605 Edited by Andrew G. Clark, Cornell University, Ithaca, NY, and approved October 11, 2016 (received for review August 2, 2016) The study of fitness landscapes, which aims at mapping geno- of previously observed beneficial mutations or on the dissection types to fitness, is receiving ever-increasing attention. Novel exper- of an observed adaptive walk (i.e., a combination of muta- imental approaches combined with next-generation sequencing tions that have been observed to be beneficial in concert). (NGS) methods enable accurate and extensive studies of the fitness Secondly, theoretical research has proposed different land- effects of mutations, allowing us to test theoretical predictions scape architectures [such as the House-of-Cards (HoC), the and improve our understanding of the shape of the true under- Kauffman NK (NK), and the Rough Mount Fuji (RMF) model], lying fitness landscape and its implications for the predictability studied their respective properties, and developed a number and repeatability of evolution. Here, we present a uniquely large of statistics that characterize the landscape and quantify the multiallelic fitness landscape comprising 640 engineered mutants expected degree of epistasis (i.e., interaction effects between that represent all possible combinations of 13 amino acid-changing mutations) (10–14). -
Reconciling Explanations for the Evolution of Evolvability
Reconciling Explanations for the Evolution of Evolvability Bryan Wilder and Kenneth O. Stanley To Appear In: Adaptive Behavior journal. London: SAGE, 2015. Abstract Evolution's ability to find innovative phenotypes is an important ingredient in the emergence of complexity in nature. A key factor in this capability is evolvability, or the propensity towards phenotypic variation. Numerous explanations for the origins of evolvability have been proposed, often differing in the role that they attribute to adaptive processes. To provide a new perspective on these explanations, experiments in this paper simulate evolution in gene regulatory networks, revealing that the type of evolvability in question significantly impacts the dynamics that follow. In particular, while adaptive processes result in evolvable individuals, processes that are either neutral or that explicitly encourage divergence result in evolvable populations. Furthermore, evolvability at the population level proves the most critical factor in the production of evolutionary innovations, suggesting that nonadaptive mechanisms are the most promising avenue for investigating and understanding evolvability. These results reconcile a large body of work across biology and inform attempts to reproduce evolvability in artificial settings. Keywords: Evolvability, evolutionary computation, gene regulatory networks Introduction Something about the structure of biological systems allows evolution to find new innovations. An important question is whether such evolvability (defined roughly as the propensity to introduce novel 1 phenotypic variation) is itself a product of selection. In essence, is evolvability evolvable (Pigliucci, 2008)? Much prior work has attempted to encourage evolvability in artificial systems in the hope of reproducing the open-ended dynamics of biological evolution (Reisinger & Miikkulainen, 2006; Grefenstette, 1999; Bedau et al., 2000; Channon, 2006; Spector, Klein, & Feinstein, 2007; Standish, 2003). -
Evolutionary Developmental Biology 573
EVOC20 29/08/2003 11:15 AM Page 572 Evolutionary 20 Developmental Biology volutionary developmental biology, now often known Eas “evo-devo,” is the study of the relation between evolution and development. The relation between evolution and development has been the subject of research for many years, and the chapter begins by looking at some classic ideas. However, the subject has been transformed in recent years as the genes that control development have begun to be identified. This chapter looks at how changes in these developmental genes, such as changes in their spatial or temporal expression in the embryo, are associated with changes in adult morphology. The origin of a set of genes controlling development may have opened up new and more flexible ways in which evolution could occur: life may have become more “evolvable.” EVOC20 29/08/2003 11:15 AM Page 573 CHAPTER 20 / Evolutionary Developmental Biology 573 20.1 Changes in development, and the genes controlling development, underlie morphological evolution Morphological structures, such as heads, legs, and tails, are produced in each individual organism by development. The organism begins life as a single cell. The organism grows by cell division, and the various cell types (bone cells, skin cells, and so on) are produced by differentiation within dividing cell lines. When one species evolves into Morphological evolution is driven another, with a changed morphological form, the developmental process must have by developmental evolution changed too. If the descendant species has longer legs, it is because the developmental process that produces legs has been accelerated, or extended over time. -
Cell Biology, Molecular Embryology, Lamarckian and Darwinian Selection As Evolvability
Evolvability of multicellular systems 7 Cell biology, molecular embryology, Lamarckian and Darwinian selection as evolvability H. Hoenigsberg Instituto de Genética Evolutiva y Biologia Molecular, Universidad Manuela Beltrán, Bogotá, DC, Colombia Corresponding author: H. Hoenigsberg E-mail: [email protected] Genet. Mol. Res. 2 (1): 7-28 (2003) Received July 31, 2002 Published February 6, 2003 ABSTRACT. The evolvability of vertebrate systems involves various mechanisms that eventually generate cooperative and nonlethal functional variation on which Darwinian selection can operate. It is a truism that to get vertebrate animals to develop a coherent machine they first had to inherit the right multicellular ontogeny. The ontogeny of a metazoan involves cell lineages that progressively deny their own capacity for increase and for totipotency in benefit of the collective interest of the individual. To achieve such cell altruism Darwinian dynamics rescinded its original unicellular mandate to reproduce. The distinction between heritability at the level of the cell lineage and at the level of the individual is crucial. However, its implications have seldom been explored in depth. While all out reproduction is the Darwinian measure of success among unicellular organisms, a high replication rate of cell lineages within the organism may be deleterious to the individual as a functional unit. If a harmoniously functioning unit is to evolve, mechanisms must have evolved whereby variants that increase their own replication rate by failing to accept their own somatic duties are controlled. For questions involving organelle origins, see Godelle and Reboud, 1995 and Hoekstra, 1990. In other words, modifiers of conflict that control cell lineages with conflicting genes and new mutant replication rates that deviate from their somatic duties had to evolve. -
Myths and Legends of the Baldwin Effect
Myths and Legends of the Baldwin Effect Peter Turney Institute for Information Technology National Research Council Canada Ottawa, Ontario, Canada, K1A 0R6 [email protected] Abstract ary computation when there is an evolving population of learning individuals (Ackley and Littman, 1991; Belew, This position paper argues that the Baldwin effect 1989; Belew et al., 1991; French and Messinger, 1994; is widely misunderstood by the evolutionary Hart, 1994; Hart and Belew, 1996; Hightower et al., 1996; computation community. The misunderstandings Whitley and Gruau, 1993; Whitley et al., 1994). This syn- appear to fall into two general categories. Firstly, ergetic effect is usually called the Baldwin effect. This has it is commonly believed that the Baldwin effect is produced the misleading impression that there is nothing concerned with the synergy that results when more to the Baldwin effect than synergy. A myth or legend there is an evolving population of learning indi- has arisen that the Baldwin effect is simply a special viduals. This is only half of the story. The full instance of synergy. One of the goals of this paper is to story is more complicated and more interesting. dispel this myth. The Baldwin effect is concerned with the costs Roughly speaking (we will be more precise later), the and benefits of lifetime learning by individuals in Baldwin effect has two aspects. First, lifetime learning in an evolving population. Several researchers have individuals can, in some situations, accelerate evolution. focussed exclusively on the benefits, but there is Second, learning is expensive. Therefore, in relatively sta- much to be gained from attention to the costs.