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Alarcón T, Sardanyés J, Guillamon A, Menendez JA. Bivalent chromatin as a therapeutic target in cancer: An in silico predictive approach for combining epigenetic drugs. PLoS Comput Biol 2021; 17:e1008408. [PMID: 34153035 PMCID: PMC8248646 DOI: 10.1371/journal.pcbi.1008408] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2020] [Revised: 07/01/2021] [Accepted: 04/26/2021] [Indexed: 11/28/2022] Open
Abstract
Tumour cell heterogeneity is a major barrier for efficient design of targeted anti-cancer therapies. A diverse distribution of phenotypically distinct tumour-cell subpopulations prior to drug treatment predisposes to non-uniform responses, leading to the elimination of sensitive cancer cells whilst leaving resistant subpopulations unharmed. Few strategies have been proposed for quantifying the variability associated to individual cancer-cell heterogeneity and minimizing its undesirable impact on clinical outcomes. Here, we report a computational approach that allows the rational design of combinatorial therapies involving epigenetic drugs against chromatin modifiers. We have formulated a stochastic model of a bivalent transcription factor that allows us to characterise three different qualitative behaviours, namely: bistable, high- and low-gene expression. Comparison between analytical results and experimental data determined that the so-called bistable and high-gene expression behaviours can be identified with undifferentiated and differentiated cell types, respectively. Since undifferentiated cells with an aberrant self-renewing potential might exhibit a cancer/metastasis-initiating phenotype, we analysed the efficiency of combining epigenetic drugs against the background of heterogeneity within the bistable sub-ensemble. Whereas single-targeted approaches mostly failed to circumvent the therapeutic problems represented by tumour heterogeneity, combinatorial strategies fared much better. Specifically, the more successful combinations were predicted to involve modulators of the histone H3K4 and H3K27 demethylases KDM5 and KDM6A/UTX. Those strategies involving the H3K4 and H3K27 methyltransferases MLL2 and EZH2, however, were predicted to be less effective. Our theoretical framework provides a coherent basis for the development of an in silico platform capable of identifying the epigenetic drugs combinations best-suited to therapeutically manage non-uniform responses of heterogenous cancer cell populations.
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Affiliation(s)
- Tomás Alarcón
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
- Centre de Recerca Matemàtica, Cerdanyola del Vallès, Spain
- Departament de Matemàtiques, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain
| | | | - Antoni Guillamon
- Centre de Recerca Matemàtica, Cerdanyola del Vallès, Spain
- Departament de Matemàtiques, EPSEB, Universitat Politècnica de Catalunya, Barcelona, Spain
- Institut de Matemàtiques de la UPC-BarcelonaTech (IMTech), Universitat Politècnica de Catalunya, Barcelona, Spain
| | - Javier A. Menendez
- Program Against Cancer Therapeutic Resistance (ProCURE), Metabolism and Cancer Group, Catalan Institute of Oncology, Girona, Spain
- Girona Biomedical Research Institute, Salt, Girona, Spain
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Gualtieri CT. Genomic Variation, Evolvability, and the Paradox of Mental Illness. Front Psychiatry 2021; 11:593233. [PMID: 33551865 PMCID: PMC7859268 DOI: 10.3389/fpsyt.2020.593233] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Accepted: 11/27/2020] [Indexed: 12/30/2022] Open
Abstract
Twentieth-century genetics was hard put to explain the irregular behavior of neuropsychiatric disorders. Autism and schizophrenia defy a principle of natural selection; they are highly heritable but associated with low reproductive success. Nevertheless, they persist. The genetic origins of such conditions are confounded by the problem of variable expression, that is, when a given genetic aberration can lead to any one of several distinct disorders. Also, autism and schizophrenia occur on a spectrum of severity, from mild and subclinical cases to the overt and disabling. Such irregularities reflect the problem of missing heritability; although hundreds of genes may be associated with autism or schizophrenia, together they account for only a small proportion of cases. Techniques for higher resolution, genomewide analysis have begun to illuminate the irregular and unpredictable behavior of the human genome. Thus, the origins of neuropsychiatric disorders in particular and complex disease in general have been illuminated. The human genome is characterized by a high degree of structural and behavioral variability: DNA content variation, epistasis, stochasticity in gene expression, and epigenetic changes. These elements have grown more complex as evolution scaled the phylogenetic tree. They are especially pertinent to brain development and function. Genomic variability is a window on the origins of complex disease, neuropsychiatric disorders, and neurodevelopmental disorders in particular. Genomic variability, as it happens, is also the fuel of evolvability. The genomic events that presided over the evolution of the primate and hominid lineages are over-represented in patients with autism and schizophrenia, as well as intellectual disability and epilepsy. That the special qualities of the human genome that drove evolution might, in some way, contribute to neuropsychiatric disorders is a matter of no little interest.
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Ligon RA, Diaz CD, Morano JL, Troscianko J, Stevens M, Moskeland A, Laman TG, Scholes E. Evolution of correlated complexity in the radically different courtship signals of birds-of-paradise. PLoS Biol 2018; 16:e2006962. [PMID: 30457985 PMCID: PMC6245505 DOI: 10.1371/journal.pbio.2006962] [Citation(s) in RCA: 58] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Accepted: 10/17/2018] [Indexed: 12/25/2022] Open
Abstract
Ornaments used in courtship often vary wildly among species, reflecting the evolutionary interplay between mate preference functions and the constraints imposed by natural selection. Consequently, understanding the evolutionary dynamics responsible for ornament diversification has been a longstanding challenge in evolutionary biology. However, comparing radically different ornaments across species, as well as different classes of ornaments within species, is a profound challenge to understanding diversification of sexual signals. Using novel methods and a unique natural history dataset, we explore evolutionary patterns of ornament evolution in a group—the birds-of-paradise—exhibiting dramatic phenotypic diversification widely assumed to be driven by sexual selection. Rather than the tradeoff between ornament types originally envisioned by Darwin and Wallace, we found positive correlations among cross-modal (visual/acoustic) signals indicating functional integration of ornamental traits into a composite unit—the “courtship phenotype.” Furthermore, given the broad theoretical and empirical support for the idea that systemic robustness—functional overlap and interdependency—promotes evolutionary innovation, we posit that birds-of-paradise have radiated extensively through ornamental phenotype space as a consequence of the robustness in the courtship phenotype that we document at a phylogenetic scale. We suggest that the degree of robustness in courtship phenotypes among taxa can provide new insights into the relative influence of sexual and natural selection on phenotypic radiations. Animals frequently vary widely in ornamentation, even among closely related species. Understanding the patterns that underlie this variation is a significant challenge, requiring comparisons among drastically different traits—like comparing apples to oranges. Here, we use novel analytical approaches to quantify variation in ornamental diversity and richness across the wildly divergent birds-of-paradise, a textbook example of how sexual selection can profoundly shape organismal phenotypes. We find that color and acoustic complexity, along with behavior and acoustic complexity, are positively correlated across evolutionary timescales. Positive links among ornament classes suggests that selection is acting on correlated suites of traits—a composite courtship phenotype—and this integration may be partially responsible for the extreme variation in signal form that we see in birds-of-paradise.
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Affiliation(s)
- Russell A. Ligon
- Cornell Lab of Ornithology, Cornell University, Ithaca, New York, United States of America
- Department of Neurobiology and Behavior, Cornell University, Ithaca, New York, United States of America
- * E-mail:
| | - Christopher D. Diaz
- Cornell Lab of Ornithology, Cornell University, Ithaca, New York, United States of America
| | - Janelle L. Morano
- Cornell Lab of Ornithology, Cornell University, Ithaca, New York, United States of America
| | - Jolyon Troscianko
- Centre for Ecology and Conservation, College of Life and Environmental Science, University of Exeter, Penryn, United Kingdom
| | - Martin Stevens
- Centre for Ecology and Conservation, College of Life and Environmental Science, University of Exeter, Penryn, United Kingdom
| | - Annalyse Moskeland
- Cornell Lab of Ornithology, Cornell University, Ithaca, New York, United States of America
| | - Timothy G. Laman
- Museum of Comparative Zoology, Harvard University, Cambridge, Massachusetts, United States of America
| | - Edwin Scholes
- Cornell Lab of Ornithology, Cornell University, Ithaca, New York, United States of America
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García-Martín JA, Catalán P, Manrubia S, Cuesta JA. Statistical theory of phenotype abundance distributions: A test through exact enumeration of genotype spaces. ACTA ACUST UNITED AC 2018. [DOI: 10.1209/0295-5075/123/28001] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Aguirre J, Catalán P, Cuesta JA, Manrubia S. On the networked architecture of genotype spaces and its critical effects on molecular evolution. Open Biol 2018; 8:180069. [PMID: 29973397 PMCID: PMC6070719 DOI: 10.1098/rsob.180069] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2018] [Accepted: 06/12/2018] [Indexed: 12/26/2022] Open
Abstract
Evolutionary dynamics is often viewed as a subtle process of change accumulation that causes a divergence among organisms and their genomes. However, this interpretation is an inheritance of a gradualistic view that has been challenged at the macroevolutionary, ecological and molecular level. Actually, when the complex architecture of genotype spaces is taken into account, the evolutionary dynamics of molecular populations becomes intrinsically non-uniform, sharing deep qualitative and quantitative similarities with slowly driven physical systems: nonlinear responses analogous to critical transitions, sudden state changes or hysteresis, among others. Furthermore, the phenotypic plasticity inherent to genotypes transforms classical fitness landscapes into multiscapes where adaptation in response to an environmental change may be very fast. The quantitative nature of adaptive molecular processes is deeply dependent on a network-of-networks multilayered structure of the map from genotype to function that we begin to unveil.
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Affiliation(s)
- Jacobo Aguirre
- Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid, Spain
- Programa de Biología de Sistemas, Centro Nacional de Biotecnología (CSIC), Madrid, Spain
| | - Pablo Catalán
- Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid, Spain
- Departamento de Matemáticas, Universidad Carlos III de Madrid, Leganés, Madrid, Spain
| | - José A Cuesta
- Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid, Spain
- Departamento de Matemáticas, Universidad Carlos III de Madrid, Leganés, Madrid, Spain
- Instituto de Biocomputación y Física de Sistemas Complejos (BIFI), Universidad de Zaragoza, Zaragoza, Spain
- UC3M-BS Institute of Financial Big Data (IFiBiD), Universidad Carlos III de Madrid, Getafe, Madrid, Spain
| | - Susanna Manrubia
- Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid, Spain
- Programa de Biología de Sistemas, Centro Nacional de Biotecnología (CSIC), Madrid, Spain
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Ahnert SE. Structural properties of genotype-phenotype maps. J R Soc Interface 2018; 14:rsif.2017.0275. [PMID: 28679667 DOI: 10.1098/rsif.2017.0275] [Citation(s) in RCA: 59] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2017] [Accepted: 06/06/2017] [Indexed: 12/21/2022] Open
Abstract
The map between genotype and phenotype is fundamental to biology. Biological information is stored and passed on in the form of genotypes, and expressed in the form of phenotypes. A growing body of literature has examined a wide range of genotype-phenotype (GP) maps and has established a number of properties that appear to be shared by many GP maps. These properties are 'structural' in the sense that they are properties of the distribution of phenotypes across the point-mutation network of genotypes. They include: a redundancy of genotypes, meaning that many genotypes map to the same phenotypes, a highly non-uniform distribution of the number of genotypes per phenotype, a high robustness of phenotypes and the ability to reach a large number of new phenotypes within a small number of mutational steps. A further important property is that the robustness and evolvability of phenotypes are positively correlated. In this review, I give an overview of the study of GP maps with particular emphasis on these structural properties, and discuss a model that attempts to explain why these properties arise, as well as some of the fundamental ways in which the structure of GP maps can affect evolutionary outcomes.
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Affiliation(s)
- S E Ahnert
- Sainsbury Laboratory, University of Cambridge, Bateman Street, Cambridge CB2 1LR, UK .,Theory of Condensed Matter Group, Cavendish Laboratory, University of Cambridge, JJ Thomson Avenue, Cambridge CB3 0HE, UK
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Ibáñez-Marcelo E, Alarcón T. Evolutionary escape on complex genotype-phenotype networks. J Theor Biol 2016; 394:18-31. [PMID: 26802479 DOI: 10.1016/j.jtbi.2015.12.033] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2015] [Revised: 12/22/2015] [Accepted: 12/25/2015] [Indexed: 10/22/2022]
Abstract
We study the problem of evolutionary escape that is the process whereby a population under sudden changes in the selective pressures acting upon it try to evade extinction by evolving from previously well-adapted phenotypes to those that are favoured by the new selective pressure. We perform a comparative analysis between results obtained by modelling genotype space as a regular hypercube (H-graphs), which is the scenario considered in previous work on the subject, to those corresponding to a complex genotype-phenotype network (B-graphs). In order to analyse the properties of the escape process on both these graphs, we apply a general theory based on multi-type branching processes to compute the evolutionary dynamics and probability of escape. We show that the distribution of distances between phenotypes in B-graphs exhibits a much larger degree of heterogeneity than in H-graphs. This property, one of the main structural differences between both types of graphs, causes heterogeneous behaviour in all results associated to the escape problem. We further show that, due to the heterogeneity characterising escape on B-graphs, escape probability can be underestimated by assuming a regular hypercube genotype network, even if we compare phenotypes at the same distance in H-graphs. Similarly, it appears that the complex structure of B-graphs slows down the rate of escape.
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Affiliation(s)
- Esther Ibáñez-Marcelo
- Centre de Recerca Matemàtica, Edifici C, Campus de Bellaterra, 08193 Bellaterra (Barcelona), Spain; Departament de Matemàtica Aplicada I, Universitat Politècnica de Catalunya, 08028 (Barcelona), Spain.
| | - Tomás Alarcón
- ICREA (Institució Catalana de Recerca i Estudis Avançats), Spain; Centre de Recerca Matemàtica, Edifici C, Campus de Bellaterra, 08193 Bellaterra (Barcelona), Spain; Departament de Matemàtiques, Universitat Autònoma de Barcelona, 08193 Bellaterra (Barcelona), Spain; Barcelona Graduate School of Mathematics (BGSMath), (Barcelona), Spain
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Tarapore D, Mouret JB. Evolvability signatures of generative encodings: Beyond standard performance benchmarks. Inf Sci (N Y) 2015. [DOI: 10.1016/j.ins.2015.03.046] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Ibáñez-Marcelo E, Alarcón T. Surviving evolutionary escape on complex genotype-phenotype networks. J Math Biol 2015; 72:623-47. [PMID: 26001745 DOI: 10.1007/s00285-015-0896-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2014] [Revised: 02/18/2015] [Indexed: 10/23/2022]
Abstract
We study the problem of evolutionary escape and survival of cell populations with a genotype-phenotype structure. We refer to evolutionary escape as the process where a cell of a given ill-adapted population to reach a well-adapted phenotype. Similarly, survival refers to the dynamics of the population once the escape phenotype has been reached. The aim of this paper is to analyse the influence of topological properties associated to robustness and evolvability on the probability of escape and on the probability of survival. In order to explore these issues, we formulate a population dynamics model, consisting of a multi-type time-continuous branching process, where types are associated to genotypes and their birth and death probabilities depend on the associated phenotype (non-escape or escape). We exploit the separation of time scales introduced by the the difference in reproductive ratios between the ill-adapted phenotypes and the escape phenotype. Two dynamical regimes emerge: a fast-decaying regime associated to the escape process itself, and a slow regime which corresponds to the survival dynamics of the population once the escape phenotype has been reached. We exploit this separation of time scales to analyse the topological factors which determine escape and survival probabilities. We show that, while the escape probability depends on the degree of escape phenotype, the probability of survival is essentially determined by its robustness, measured in terms of a weighted clustering coefficient.
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Affiliation(s)
- Esther Ibáñez-Marcelo
- Centre de Recerca Matemàtica, Campus de Bellaterra, Edifici C, Bellaterra, 08193, Barcelona, Spain. .,Departament de Matemàtica Aplicada I, Universitat Politècnica de Catalunya, 08028, Barcelona, Spain.
| | - Tomás Alarcón
- Centre de Recerca Matemàtica, Campus de Bellaterra, Edifici C, Bellaterra, 08193, Barcelona, Spain. .,Departament de Matemàtiques, Universitat Atonòma de Barcelona, Bellaterra, 08193, Barcelona, Spain.
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Dall'Olio GM, Vahdati AR, Bertranpetit J, Wagner A, Laayouni H. VCF2Networks: applying genotype networks to single-nucleotide variants data. ACTA ACUST UNITED AC 2014; 31:438-9. [PMID: 25282646 DOI: 10.1093/bioinformatics/btu650] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
SUMMARY A wealth of large-scale genome sequencing projects opens the doors to new approaches to study the relationship between genotype and phenotype. One such opportunity is the possibility to apply genotype networks analysis to population genetics data. Genotype networks are a representation of the set of genotypes associated with a single phenotype, and they allow one to estimate properties such as the robustness of the phenotype to mutations, and the ability of its associated genotypes to evolve new adaptations. So far, though, genotype networks analysis has rarely been applied to population genetics data. To help fill this gap, here we present VCF2Networks, a tool to determine and study genotype network structure from single-nucleotide variant data. AVAILABILITY AND IMPLEMENTATION VCF2Networks is available at https://bitbucket.org/dalloliogm/vcf2networks. CONTACT giovanni.dallolio@kcl.ac.uk SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Giovanni Marco Dall'Olio
- Department of Experimental and Health Sciences, Institut de Biologia Evolutiva (CSIC-Universitat Pompeu Fabra), 08003 Barcelona, Catalonia, Spain, Institute of Evolutionary Biology and Environmental Studies/Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland, SIB, CIG Quartier Sorge, bâtiment Génopode 1015 Lausanne, Switzerland, The Santa Fe Institute, 1399 Hyde Parke Road, 87501 Santa Fe, New Mexico, USA and Departament de Genetica i de Microbiologia, Grup de Biologia Evolutiva (GBE), Universitat Autonoma de Barcelona, 08913 Bellaterra, Barcelona
| | - Ali R Vahdati
- Department of Experimental and Health Sciences, Institut de Biologia Evolutiva (CSIC-Universitat Pompeu Fabra), 08003 Barcelona, Catalonia, Spain, Institute of Evolutionary Biology and Environmental Studies/Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland, SIB, CIG Quartier Sorge, bâtiment Génopode 1015 Lausanne, Switzerland, The Santa Fe Institute, 1399 Hyde Parke Road, 87501 Santa Fe, New Mexico, USA and Departament de Genetica i de Microbiologia, Grup de Biologia Evolutiva (GBE), Universitat Autonoma de Barcelona, 08913 Bellaterra, Barcelona
| | - Jaume Bertranpetit
- Department of Experimental and Health Sciences, Institut de Biologia Evolutiva (CSIC-Universitat Pompeu Fabra), 08003 Barcelona, Catalonia, Spain, Institute of Evolutionary Biology and Environmental Studies/Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland, SIB, CIG Quartier Sorge, bâtiment Génopode 1015 Lausanne, Switzerland, The Santa Fe Institute, 1399 Hyde Parke Road, 87501 Santa Fe, New Mexico, USA and Departament de Genetica i de Microbiologia, Grup de Biologia Evolutiva (GBE), Universitat Autonoma de Barcelona, 08913 Bellaterra, Barcelona
| | - Andreas Wagner
- Department of Experimental and Health Sciences, Institut de Biologia Evolutiva (CSIC-Universitat Pompeu Fabra), 08003 Barcelona, Catalonia, Spain, Institute of Evolutionary Biology and Environmental Studies/Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland, SIB, CIG Quartier Sorge, bâtiment Génopode 1015 Lausanne, Switzerland, The Santa Fe Institute, 1399 Hyde Parke Road, 87501 Santa Fe, New Mexico, USA and Departament de Genetica i de Microbiologia, Grup de Biologia Evolutiva (GBE), Universitat Autonoma de Barcelona, 08913 Bellaterra, Barcelona Department of Experimental and Health Sciences, Institut de Biologia Evolutiva (CSIC-Universitat Pompeu Fabra), 08003 Barcelona, Catalonia, Spain, Institute of Evolutionary Biology and Environmental Studies/Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland, SIB, CIG Quartier Sorge, bâtiment Génopode 1015 Lausanne, Switzerland, The Santa Fe Institute, 1399 Hyde Parke Road, 87501 Santa Fe, New Mexico, USA and Departament de Genetica i de Microbiologia, Grup de Biologia Evolutiva (GBE), Universitat Autonoma de Barcelona, 08913 Bellaterra, Barcelona Department of Experimental and Health Sciences, Institut de Biologia Evolutiva (CSIC-Universitat Pompeu Fabra), 08003 Barcelona, Catalonia, Spain, Institute of Evolutionary Biology and Environmental Studies/Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland, SIB, CIG Quartier Sorge, bâtiment Génopode 1015 Lausanne, Switzerland, The Santa Fe Institute, 1399 Hyde Parke Road, 87501 Santa Fe, New Mexico, USA and Departament de Genetica i de Microbiologia, Grup de Biologia Evolutiva (GBE), Universitat Autonoma de Barcelona, 08913 Bellaterra, Barcelona
| | - Hafid Laayouni
- Department of Experimental and Health Sciences, Institut de Biologia Evolutiva (CSIC-Universitat Pompeu Fabra), 08003 Barcelona, Catalonia, Spain, Institute of Evolutionary Biology and Environmental Studies/Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland, SIB, CIG Quartier Sorge, bâtiment Génopode 1015 Lausanne, Switzerland, The Santa Fe Institute, 1399 Hyde Parke Road, 87501 Santa Fe, New Mexico, USA and Departament de Genetica i de Microbiologia, Grup de Biologia Evolutiva (GBE), Universitat Autonoma de Barcelona, 08913 Bellaterra, Barcelona Department of Experimental and Health Sciences, Institut de Biologia Evolutiva (CSIC-Universitat Pompeu Fabra), 08003 Barcelona, Catalonia, Spain, Institute of Evolutionary Biology and Environmental Studies/Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland, SIB, CIG Quartier Sorge, bâtiment Génopode 1015 Lausanne, Switzerland, The Santa Fe Institute, 1399 Hyde Parke Road, 87501 Santa Fe, New Mexico, USA and Departament de Genetica i de Microbiologia, Grup de Biologia Evolutiva (GBE), Universitat Autonoma de Barcelona, 08913 Bellaterra, Barcelona
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