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Zimm R, Berio F, Debiais-Thibaud M, Goudemand N. A shark-inspired general model of tooth morphogenesis unveils developmental asymmetries in phenotype transitions. Proc Natl Acad Sci U S A 2023; 120:e2216959120. [PMID: 37027430 PMCID: PMC10104537 DOI: 10.1073/pnas.2216959120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 02/07/2023] [Indexed: 04/08/2023] Open
Abstract
Developmental complexity stemming from the dynamic interplay between genetic and biomechanic factors canalizes the ways genotypes and phenotypes can change in evolution. As a paradigmatic system, we explore how changes in developmental factors generate typical tooth shape transitions. Since tooth development has mainly been researched in mammals, we contribute to a more general understanding by studying the development of tooth diversity in sharks. To this end, we build a general, but realistic, mathematical model of odontogenesis. We show that it reproduces key shark-specific features of tooth development as well as real tooth shape variation in small-spotted catsharks Scyliorhinus canicula. We validate our model by comparison with experiments in vivo. Strikingly, we observe that developmental transitions between tooth shapes tend to be highly degenerate, even for complex phenotypes. We also discover that the sets of developmental parameters involved in tooth shape transitions tend to depend asymmetrically on the direction of that transition. Together, our findings provide a valuable base for furthering our understanding of how developmental changes can lead to both adaptive phenotypic change and trait convergence in complex, phenotypically highly diverse, structures.
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Affiliation(s)
- Roland Zimm
- Institut de Génomique Fonctionnelle de Lyon, Ecole Normale Supérieure de Lyon, Université Claude Bernard Lyon 1, CNRS UMR 5242, Lyon Cedex07 69364, France
| | - Fidji Berio
- Institut de Génomique Fonctionnelle de Lyon, Ecole Normale Supérieure de Lyon, Université Claude Bernard Lyon 1, CNRS UMR 5242, Lyon Cedex07 69364, France
- Institut des Sciences de l’Evolution de Montpellier, University of Montpellier, CNRS, Institut de la Recherche pour le Développement, Montpellier34095, France
| | - Mélanie Debiais-Thibaud
- Institut des Sciences de l’Evolution de Montpellier, University of Montpellier, CNRS, Institut de la Recherche pour le Développement, Montpellier34095, France
| | - Nicolas Goudemand
- Institut de Génomique Fonctionnelle de Lyon, Ecole Normale Supérieure de Lyon, Université Claude Bernard Lyon 1, CNRS UMR 5242, Lyon Cedex07 69364, France
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2
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Li Y, Brinkworth A, Green E, Oyston J, Wills M, Ruta M. Divergent vertebral formulae shape the evolution of axial complexity in mammals. Nat Ecol Evol 2023; 7:367-381. [PMID: 36878987 PMCID: PMC9998275 DOI: 10.1038/s41559-023-01982-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 01/03/2023] [Indexed: 03/08/2023]
Abstract
Complexity, defined as the number of parts and their degree of differentiation, is a poorly explored aspect of macroevolutionary dynamics. The maximum anatomical complexity of organisms has undoubtedly increased through evolutionary time. However, it is unclear whether this increase is a purely diffusive process or whether it is at least partly driven, occurring in parallel in most or many lineages and with increases in the minima as well as the means. Highly differentiated and serially repeated structures, such as vertebrae, are useful systems with which to investigate these patterns. We focus on the serial differentiation of the vertebral column in 1,136 extant mammal species, using two indices that quantify complexity as the numerical richness and proportional distribution of vertebrae across presacral regions and a third expressing the ratio between thoracic and lumbar vertebrae. We address three questions. First, we ask whether the distribution of complexity values in major mammal groups is similar or whether clades have specific signatures associated with their ecology. Second, we ask whether changes in complexity throughout the phylogeny are biased towards increases and whether there is evidence of driven trends. Third, we ask whether evolutionary shifts in complexity depart from a uniform Brownian motion model. Vertebral counts, but not complexity indices, differ significantly between major groups and exhibit greater within-group variation than recognized hitherto. We find strong evidence of a trend towards increasing complexity, where higher values propagate further increases in descendant lineages. Several increases are inferred to have coincided with major ecological or environmental shifts. We find support for multiple-rate models of evolution for all complexity metrics, suggesting that increases in complexity occurred in stepwise shifts, with evidence for widespread episodes of recent rapid divergence. Different subclades evolve more complex vertebral columns in different configurations and probably under different selective pressures and constraints, with widespread convergence on the same formulae. Further work should therefore focus on the ecological relevance of differences in complexity and a more detailed understanding of historical patterns.
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Affiliation(s)
- Yimeng Li
- Milner Centre for Evolution, Department of Biology and Biochemistry, University of Bath, Bath, UK.,Nanjing Institute of Geology and Palaeontology, CAS, Nanjing, China
| | - Andrew Brinkworth
- Milner Centre for Evolution, Department of Biology and Biochemistry, University of Bath, Bath, UK
| | - Emily Green
- Joseph Banks Laboratories, Department of Life Sciences, University of Lincoln, Lincoln, UK
| | - Jack Oyston
- Milner Centre for Evolution, Department of Biology and Biochemistry, University of Bath, Bath, UK
| | - Matthew Wills
- Milner Centre for Evolution, Department of Biology and Biochemistry, University of Bath, Bath, UK.
| | - Marcello Ruta
- Joseph Banks Laboratories, Department of Life Sciences, University of Lincoln, Lincoln, UK.
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3
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Salazar-Ciudad I, Cano-Fernández H. Evo-devo beyond development: Generalizing evo-devo to all levels of the phenotypic evolution. Bioessays 2023; 45:e2200205. [PMID: 36739577 DOI: 10.1002/bies.202200205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 12/25/2022] [Accepted: 01/12/2023] [Indexed: 02/06/2023]
Abstract
A foundational idea of evo-devo is that morphological variation is not isotropic, that is, it does not occur in all directions. Instead, some directions of morphological variation are more likely than others from DNA-level variation and these largely depend on development. We argue that this evo-devo perspective should apply not only to morphology but to evolution at all phenotypic levels. At other phenotypic levels there is no development, but there are processes that can be seen, in analogy to development, as constructing the phenotype (e.g., protein folding, learning for behavior, etc.). We argue that to explain the direction of evolution two types of arguments need to be combined: generative arguments about which phenotypic variation arises in each generation and selective arguments about which of it passes to the next generation. We explain how a full consideration of the two types of arguments improves the explanatory power of evolutionary theory. Also see the video abstract here: https://youtu.be/Egbvma_uaKc.
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Affiliation(s)
- Isaac Salazar-Ciudad
- Centre de Recerca Matemàtica, Cerdanyola del Vallès, Spain.,Genomics, Bioinformatics and Evolution, Departament de Genètica i Microbiologia, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Hugo Cano-Fernández
- Genomics, Bioinformatics and Evolution, Departament de Genètica i Microbiologia, Universitat Autònoma de Barcelona, Barcelona, Spain
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4
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Dingle K, Novev JK, Ahnert SE, Louis AA. Predicting phenotype transition probabilities via conditional algorithmic probability approximations. J R Soc Interface 2022; 19:20220694. [PMID: 36514888 PMCID: PMC9748496 DOI: 10.1098/rsif.2022.0694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Unravelling the structure of genotype-phenotype (GP) maps is an important problem in biology. Recently, arguments inspired by algorithmic information theory (AIT) and Kolmogorov complexity have been invoked to uncover simplicity bias in GP maps, an exponentially decaying upper bound in phenotype probability with the increasing phenotype descriptional complexity. This means that phenotypes with many genotypes assigned via the GP map must be simple, while complex phenotypes must have few genotypes assigned. Here, we use similar arguments to bound the probability P(x → y) that phenotype x, upon random genetic mutation, transitions to phenotype y. The bound is [Formula: see text], where [Formula: see text] is the estimated conditional complexity of y given x, quantifying how much extra information is required to make y given access to x. This upper bound is related to the conditional form of algorithmic probability from AIT. We demonstrate the practical applicability of our derived bound by predicting phenotype transition probabilities (and other related quantities) in simulations of RNA and protein secondary structures. Our work contributes to a general mathematical understanding of GP maps and may facilitate the prediction of transition probabilities directly from examining phenotype themselves, without utilizing detailed knowledge of the GP map.
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Affiliation(s)
- Kamaludin Dingle
- Department of Chemical Engineering and Biotechnology, Cambridge University, Cambridge CB2 1TN, UK,Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA 91125, USA,Department of Mathematics and Natural Sciences, Centre for Applied Mathematics and Bioinformatics (CAMB), Gulf University for Science and Technology, 32093, Kuwait
| | - Javor K. Novev
- Department of Chemical Engineering and Biotechnology, Cambridge University, Cambridge CB2 1TN, UK
| | - Sebastian E. Ahnert
- Department of Chemical Engineering and Biotechnology, Cambridge University, Cambridge CB2 1TN, UK
| | - Ard A. Louis
- Department of Physics, Rudolf Peierls Centre for Theoretical Physics, Oxford University, Oxford OX1 2JD, UK
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5
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Genome-Wide Identification and Characterization of the Oat ( Avena sativa L.) WRKY Transcription Factor Family. Genes (Basel) 2022; 13:genes13101918. [PMID: 36292803 PMCID: PMC9601435 DOI: 10.3390/genes13101918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 10/11/2022] [Accepted: 10/20/2022] [Indexed: 11/04/2022] Open
Abstract
The WRKY family is widely involved in the regulation of plant growth and stress response and is one of the largest gene families related to plant environmental adaptation. However, no systematic studies on the WRKY family in oat (Avena sativa L.) have been conducted to date. The recently published complete genome sequence of oat enables the systematic analysis of the AsWRKYs. Based on a genome-wide study of oat, we identified 162 AsWRKYs that were unevenly distributed across 21 chromosomes; a phylogenetic tree of WRKY domains divided these genes into three groups (I, II, and III). We also analyzed the gene duplication events and identified a total of 111 gene pairs that showed strong purifying selection during the evolutionary process. Surprisingly, almost all genes evolved after the completion of subgenomic differentiation of hexaploid oat. Further studies on the functional analysis indicated that AsWRKYs were widely involved in various biological processes. Notably, expression patterns of 16 AsWRKY genes revealed that the response of AsWRKYs were affected by stress level and time. In conclusion, this study provides a reference for further analysis of the role of WRKY transcription factors in species evolution and functional differentiation.
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Reply to Ocklenburg and Mundorf: The interplay of developmental bias and natural selection. Proc Natl Acad Sci U S A 2022; 119:e2205299119. [PMID: 35787035 PMCID: PMC9282226 DOI: 10.1073/pnas.2205299119] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
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7
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Niu B, Nguyen Bach T, Chen X, Raghunath Chandratre K, Isaac Murray J, Zhao Z, Zhang M. Computational modeling and analysis of the morphogenetic domain signaling networks regulating C. elegans embryogenesis. Comput Struct Biotechnol J 2022; 20:3653-3666. [PMID: 35891777 PMCID: PMC9289785 DOI: 10.1016/j.csbj.2022.05.058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 05/29/2022] [Accepted: 05/30/2022] [Indexed: 11/03/2022] Open
Abstract
Caenorhabditis elegans, often referred to as the ‘roundworm’, provides a powerful model for studying cell autonomous and cell–cell interactions through the direct observation of embryonic development in vivo. By leveraging the precisely mapped cell lineage at single cell resolution, we are able to study at a systems level how early embryonic cells communicate across morphogenetic domains for the coordinated processes of gene expressions and collective cellular behaviors that regulate tissue morphogenesis. In this study, we developed a computational framework for the exploration of the morphogenetic domain cell signaling networks that may regulate C. elegans gastrulation and embryonic organogenesis. We demonstrated its utility by producing the following results, i) established a virtual reference model of developing C. elegans embryos through the spatiotemporal alignment of individual embryo cell nuclear imaging samples; ii) integrated the single cell spatiotemporal gene expression profile with the established virtual embryo model by data pooling; iii) trained a Machine Learning model (Random Forest Regression), which predicts accurately the spatial positions of the cells given their gene expression profiles for a given developmental time (e.g. total cell number of the embryo); iv) enabled virtual 4-dimensional tomographic graphical modeling of single cell data; v) inferred the biology signaling pathways that act in each of morphogenetic domains by meta-data analysis. It is intriguing that the morphogenetic domain cell signaling network seems to involve some crosstalk of multiple biology signaling pathways during the formation of tissue boundary pattern. Lastly, we developed the Software tool ‘Embryo aligner version 1.0’ and provided it as an Open Source program to the research community for virtual embryo modeling, and phenotype perturbation analyses (https://github.com/csniuben/embryo_aligner/wiki and https://bioinfo89.github.io/C.elegansEmbryonicOrganogenesisweb/).
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8
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Symmetry and simplicity spontaneously emerge from the algorithmic nature of evolution. Proc Natl Acad Sci U S A 2022; 119:e2113883119. [PMID: 35275794 PMCID: PMC8931234 DOI: 10.1073/pnas.2113883119] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
SignificanceWhy does evolution favor symmetric structures when they only represent a minute subset of all possible forms? Just as monkeys randomly typing into a computer language will preferentially produce outputs that can be generated by shorter algorithms, so the coding theorem from algorithmic information theory predicts that random mutations, when decoded by the process of development, preferentially produce phenotypes with shorter algorithmic descriptions. Since symmetric structures need less information to encode, they are much more likely to appear as potential variation. Combined with an arrival-of-the-frequent mechanism, this algorithmic bias predicts a much higher prevalence of low-complexity (high-symmetry) phenotypes than follows from natural selection alone and also explains patterns observed in protein complexes, RNA secondary structures, and a gene regulatory network.
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9
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Milocco L, Salazar-Ciudad I. Evolution of the G Matrix under Nonlinear Genotype-Phenotype Maps. Am Nat 2022; 199:420-435. [DOI: 10.1086/717814] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Affiliation(s)
- Lisandro Milocco
- Institute of Biotechnology, University of Helsinki, Helsinki, Finland
| | - Isaac Salazar-Ciudad
- Institute of Biotechnology, University of Helsinki, Helsinki, Finland
- Centre de Recerca Matemàtica, Barcelona, Spain; and Genomics, Bioinformatics, and Evolution, Departament de Genètica i Microbiologia, Universitat Autònoma de Barcelona, Barcelona, Spain
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10
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Hagolani PF, Zimm R, Vroomans R, Salazar-Ciudad I. Correction: On the evolution and development of morphological complexity: A view from gene regulatory networks. PLoS Comput Biol 2021; 17:e1009686. [PMID: 34890397 PMCID: PMC8664187 DOI: 10.1371/journal.pcbi.1009686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
[This corrects the article DOI: 10.1371/journal.pcbi.1008570.].
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11
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Lafuma F, Corfe IJ, Clavel J, Di-Poï N. Multiple evolutionary origins and losses of tooth complexity in squamates. Nat Commun 2021; 12:6001. [PMID: 34650041 PMCID: PMC8516937 DOI: 10.1038/s41467-021-26285-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 09/27/2021] [Indexed: 11/21/2022] Open
Abstract
Teeth act as tools for acquiring and processing food, thus holding a prominent role in vertebrate evolution. In mammals, dental-dietary adaptations rely on tooth complexity variations controlled by cusp number and pattern. Complexity increase through cusp addition has dominated the diversification of mammals. However, studies of Mammalia alone cannot reveal patterns of tooth complexity conserved throughout vertebrate evolution. Here, we use morphometric and phylogenetic comparative methods across fossil and extant squamates to show they also repeatedly evolved increasingly complex teeth, but with more flexibility than mammals. Since the Late Jurassic, multiple-cusped teeth evolved over 20 times independently from a single-cusped common ancestor. Squamates frequently lost cusps and evolved varied multiple-cusped morphologies at heterogeneous rates. Tooth complexity evolved in correlation with changes in plant consumption, resulting in several major increases in speciation. Complex teeth played a critical role in vertebrate evolution outside Mammalia, with squamates exemplifying a more labile system of dental-dietary evolution.
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Affiliation(s)
- Fabien Lafuma
- Institute of Biotechnology, Helsinki Institute of Life Science, University of Helsinki, FI-00014, Helsinki, Finland.
| | - Ian J Corfe
- Institute of Biotechnology, Helsinki Institute of Life Science, University of Helsinki, FI-00014, Helsinki, Finland.
- Geological Survey of Finland, FI-02150, Espoo, Finland.
| | - Julien Clavel
- Department of Life Sciences, The Natural History Museum, London, SW7 5BD, UK
- Univ. Lyon, Université Claude Bernard Lyon 1, CNRS, ENTPE, UMR 5023 LEHNA, F-69622, Villeurbanne, France
| | - Nicolas Di-Poï
- Institute of Biotechnology, Helsinki Institute of Life Science, University of Helsinki, FI-00014, Helsinki, Finland.
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12
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Chevin LM, Leung C, Le Rouzic A, Uller T. Using phenotypic plasticity to understand the structure and evolution of the genotype-phenotype map. Genetica 2021; 150:209-221. [PMID: 34617196 DOI: 10.1007/s10709-021-00135-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 09/22/2021] [Indexed: 10/20/2022]
Abstract
Deciphering the genotype-phenotype map necessitates relating variation at the genetic level to variation at the phenotypic level. This endeavour is inherently limited by the availability of standing genetic variation, the rate of spontaneous mutation to novo genetic variants, and possible biases associated with induced mutagenesis. An interesting alternative is to instead rely on the environment as a source of variation. Many phenotypic traits change plastically in response to the environment, and these changes are generally underlain by changes in gene expression. Relating gene expression plasticity to the phenotypic plasticity of more integrated organismal traits thus provides useful information about which genes influence the development and expression of which traits, even in the absence of genetic variation. We here appraise the prospects and limits of such an environment-for-gene substitution for investigating the genotype-phenotype map. We review models of gene regulatory networks, and discuss the different ways in which they can incorporate the environment to mechanistically model phenotypic plasticity and its evolution. We suggest that substantial progress can be made in deciphering this genotype-environment-phenotype map, by connecting theory on gene regulatory network to empirical patterns of gene co-expression, and by more explicitly relating gene expression to the expression and development of phenotypes, both theoretically and empirically.
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Affiliation(s)
- Luis-Miguel Chevin
- CEFE, Univ Montpellier, CNRS, EPHE, IRD, Univ Paul Valéry Montpellier 3, Montpellier, France.
| | - Christelle Leung
- CEFE, Univ Montpellier, CNRS, EPHE, IRD, Univ Paul Valéry Montpellier 3, Montpellier, France
| | - Arnaud Le Rouzic
- Laboratoire Évolution, Génomes, Comportement, Écologie, CNRS, IRD, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Tobias Uller
- Department of Biology, Lund University, Lund, Sweden
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13
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Manrubia S, Cuesta JA, Aguirre J, Ahnert SE, Altenberg L, Cano AV, Catalán P, Diaz-Uriarte R, Elena SF, García-Martín JA, Hogeweg P, Khatri BS, Krug J, Louis AA, Martin NS, Payne JL, Tarnowski MJ, Weiß M. From genotypes to organisms: State-of-the-art and perspectives of a cornerstone in evolutionary dynamics. Phys Life Rev 2021; 38:55-106. [PMID: 34088608 DOI: 10.1016/j.plrev.2021.03.004] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 03/01/2021] [Indexed: 12/21/2022]
Abstract
Understanding how genotypes map onto phenotypes, fitness, and eventually organisms is arguably the next major missing piece in a fully predictive theory of evolution. We refer to this generally as the problem of the genotype-phenotype map. Though we are still far from achieving a complete picture of these relationships, our current understanding of simpler questions, such as the structure induced in the space of genotypes by sequences mapped to molecular structures, has revealed important facts that deeply affect the dynamical description of evolutionary processes. Empirical evidence supporting the fundamental relevance of features such as phenotypic bias is mounting as well, while the synthesis of conceptual and experimental progress leads to questioning current assumptions on the nature of evolutionary dynamics-cancer progression models or synthetic biology approaches being notable examples. This work delves with a critical and constructive attitude into our current knowledge of how genotypes map onto molecular phenotypes and organismal functions, and discusses theoretical and empirical avenues to broaden and improve this comprehension. As a final goal, this community should aim at deriving an updated picture of evolutionary processes soundly relying on the structural properties of genotype spaces, as revealed by modern techniques of molecular and functional analysis.
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Affiliation(s)
- Susanna Manrubia
- Department of Systems Biology, Centro Nacional de Biotecnología (CSIC), Madrid, Spain; Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid, Spain.
| | - José A Cuesta
- Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid, Spain; Departamento de Matemáticas, Universidad Carlos III de Madrid, Leganés, Spain; Instituto de Biocomputación y Física de Sistemas Complejos (BiFi), Universidad de Zaragoza, Spain; UC3M-Santander Big Data Institute (IBiDat), Getafe, Madrid, Spain
| | - Jacobo Aguirre
- Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid, Spain; Centro de Astrobiología, CSIC-INTA, ctra. de Ajalvir km 4, 28850 Torrejón de Ardoz, Madrid, Spain
| | - Sebastian E Ahnert
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, UK; The Alan Turing Institute, British Library, 96 Euston Road, London NW1 2DB, UK
| | | | - Alejandro V Cano
- Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Pablo Catalán
- Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid, Spain; Departamento de Matemáticas, Universidad Carlos III de Madrid, Leganés, Spain
| | - Ramon Diaz-Uriarte
- Department of Biochemistry, Universidad Autónoma de Madrid, Madrid, Spain; Instituto de Investigaciones Biomédicas "Alberto Sols" (UAM-CSIC), Madrid, Spain
| | - Santiago F Elena
- Instituto de Biología Integrativa de Sistemas, I(2)SysBio (CSIC-UV), València, Spain; The Santa Fe Institute, Santa Fe, NM, USA
| | | | - Paulien Hogeweg
- Theoretical Biology and Bioinformatics Group, Utrecht University, the Netherlands
| | - Bhavin S Khatri
- The Francis Crick Institute, London, UK; Department of Life Sciences, Imperial College London, London, UK
| | - Joachim Krug
- Institute for Biological Physics, University of Cologne, Köln, Germany
| | - Ard A Louis
- Rudolf Peierls Centre for Theoretical Physics, University of Oxford, Oxford, UK
| | - Nora S Martin
- Theory of Condensed Matter Group, Cavendish Laboratory, University of Cambridge, Cambridge, UK; Sainsbury Laboratory, University of Cambridge, Cambridge, UK
| | - Joshua L Payne
- Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | | | - Marcel Weiß
- Theory of Condensed Matter Group, Cavendish Laboratory, University of Cambridge, Cambridge, UK; Sainsbury Laboratory, University of Cambridge, Cambridge, UK
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