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The Emergence of Complexity and Restricted Pleiotropy in Adapting Networks

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The Emergence of Complexity and Restricted Pleiotropy in Adapting Networks The emergence of complexity and restricted pleiotropy in adapting networks. Hervé Le Nagard, Lin Chao, Olivier Tenaillon To cite this version: Hervé Le Nagard, Lin Chao, Olivier Tenaillon. The emergence of complexity and restricted pleiotropy in adapting networks.. BMC Evolutionary Biology, BioMed Central, 2011, 11 (1), pp.326. 10.1186/1471-2148-11-326. inserm-00643376 HAL Id: inserm-00643376 https://www.hal.inserm.fr/inserm-00643376 Submitted on 21 Nov 2011 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. Le Nagard et al. BMC Evolutionary Biology 2011, 11:326 http://www.biomedcentral.com/1471-2148/11/326 RESEARCHARTICLE Open Access The emergence of complexity and restricted pleiotropy in adapting networks Hervé Le Nagard1,2,3,4,5*, Lin Chao6 and Olivier Tenaillon1,3* Abstract Background: The emergence of organismal complexity has been a difficult subject for researchers because it is not readily amenable to investigation by experimental approaches. Complexity has a myriad of untested definitions and our understanding of its evolution comes primarily from static snapshots gleaned from organisms ranked on an intuitive scale. Fisher’s geometric model of adaptation, which defines complexity as the number of phenotypes an organism exposes to natural selection, provides a theoretical framework to study complexity. Yet investigations of this model reveal phenotypic complexity as costly and therefore unlikely to emerge. Results: We have developed a computational approach to study the emergence of complexity by subjecting neural networks to adaptive evolution in environments exacting different levels of demands. We monitored complexity by a variety of metrics. Top down metrics derived from Fisher’s geometric model correlated better with the environmental demands than bottom up ones such as network size. Phenotypic complexity was found to increase towards an environment-dependent level through the emergence of restricted pleiotropy. Such pleiotropy, which confined the action of mutations to only a subset of traits, better tuned phenotypes in challenging environments. However, restricted pleiotropy also came at a cost in the form of a higher genetic load, as it required the maintenance by natural selection of more independent traits. Consequently, networks of different sizes converged in complexity when facing similar environment. Conclusions: Phenotypic complexity evolved as a function of the demands of the selective pressures, rather than the physical properties of the network architecture, such as functional size. Our results show that complexity may be more predictable, and understandable, if analyzed from the perspective of the integrated task the organism performs, rather than the physical architecture used to accomplish such tasks. Thus, top down metrics emphasizing selection may be better for describing biological complexity than bottom up ones representing size and other physical attributes. Background coupled to some quantitative methods of estimation. The evolution of the complexity of organisms has been a Initial estimates of complexity have been based on the challenge for Darwinian theories of evolution [1]. How number of nucleotides, genes or cell types in a genome, does evolution produce complex organs, when the func- but such bottom up estimates often fail to have useful tioning of such organs requires the successful interaction properties [4,5]. For instance, the multicellular green of many components? Despite the recent proliferation of algae Volvox carteri has the same number of genes as its large nucleotide, proteomic, and metabolic databases, it unicellular relative, Chlamydomonas reinhardtii [6], remains difficult to define the complexity of organisms even though the evolution of multicellularity is one of [2,3], and even more to understand the determinants the major transitions affecting organismal complexity. underlying the emergence of complexity. To overcome possible problems associated with the pre- Any attempt to understand the evolution of complex- viously mentioned bottom up metrics of complexity, ity must rely on a meaningful definition of complexity recent studies have shifted to a more top down approach by incorporating population genetics [7], * Correspondence: [email protected]; [email protected] quantitative genetics [8] and ecology [9] and quantifying 1INSERM, UMR-S 722, Paris, F-75018, France Full list of author information is available at the end of the article © 2011 Le Nagard et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Le Nagard et al. BMC Evolutionary Biology 2011, 11:326 Page 2 of 15 http://www.biomedcentral.com/1471-2148/11/326 complexity from the perspective of natural selection act- to the initial chemical concentration is propagated ing on the organism as a whole [10-12]. through the network to the final node/gene. This node/ The most integrated vision of complexity comes from gene is assumed to be directly linked to fitness such Fisher’s geometric model of adaptation [13]. Fitness in that the quantitative value of its output/expression can the model is a function of the phenotype of the organ- compared to some expected value to estimate fitness. In ism. Individual organisms are depicted as points in a other words, for a given concentration of a chemical, a multidimensional space and each axis corresponds to an given expression level of the final gene is expected and independent phenotype under selection. The total num- any deviation from that value will reduce the fitness of ber of independent phenotypes,i.e.thedimensionality the network. To go further, rather than evaluating the of the phenotypic space, is taken to represent phenoty- fitness of a network base only on its response to a single pic complexity. This model has received much attention input value or concentration, we assess it on a linear in the last decade and has provided many qualitative gradient of concentrations. For 100 different concentra- predictions [7,11,12,14-18] that have been validated tions spread between 0 and 1, we expect 100 different experimentally [12,19,20]. levels of expression of the output node. This means that Using Fisher geometric model of adaptation, several fitness is defined as a goodness of fit between the output theoretical studies have also analyzed the consequences of the networks and a reference function. By allowing of phenotypic complexity on evolution. All of them selection to operate through the fitness of the individual found higher complexity to be costly. The cost results networks, the population was allowed to evolve. from the difficulty of having to optimize many pheno- We used as the reference function Legendre polyno- types simultaneously and it is manifested by the decreas- mials. These functions were chosen because they could ing fraction of beneficial mutations as dimensionality be readily ranked in term of complexity by the Order of increases [11,13,15,21]. As a result, the rate of adapta- the Legendre Polynomial (OLP). Higher OLP’sboth tion decreases [7,11,21,22] and the drift load increases require more parameters and have a higher Kolmogorov [12,16,20,23-25]. Drift load represents the loss of fitness complexity [28] (it requires a longer source code to be due the effects of genetic drift on the fixation rate of implemented, the size of the code increasing linearly beneficial and deleterious mutations. with the OLP). Biologically, this means that a high OLP Although previous studies have used Fisher’sgeome- environment will select for networks whose response to trical model to examine the effect of complexity on evo- a linear gradient of concentration is complex. For lution, none have allowed dimensionality to change as a instance, if the expression of the key gene determines result of evolution and adaptation. To characterize both the state of the cell depending on a threshold level, as the selective forces acting on the emergence of complex- observed during development, an OLP of p selects for ity and the underlying mechanisms, we have designed an networks performing p transitions from one state to evolutionary system in which complexity was free to another along the full gradient. We note that while the emerge depending on its costs and benefits. Although reference function, and hence the environmental chal- experimental studies of complexity with real biological lenge could also be described as having either high or organisms are possible [11,12], a systematic investigation low complexity, we have chosen to restrict our use of is still difficult. We chose therefore to use computational the term complexity to describe only networks. Environ- models employing artificial neural networks evolving mental challenges will be described as having different asexually under a mutation-selection-drift process as
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