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Evolution & Development DOI: 10.1111/ede.12315 RESEARCH Developmental structuring of phenotypic variation: A case study with a cellular automata model of ontogeny Wim Hordijk1 | Lee Altenberg2 1Konrad Lorenz Institute for Evolution and Cognition Research, Klosterneuburg, Abstract Austria Developmental mechanisms not only produce an organismal phenotype, but 2University of Hawai‘iatMānoa, they also structure the way genetic variation maps to phenotypic variation. ‘ Honolulu, Hawai i Here, we revisit a computational model for the evolution of ontogeny based on Correspondence cellular automata, in which evolution regularly discovered two alternative Wim Hordijk, Konrad Lorenz Institute for mechanisms for achieving a selected phenotype, one showing high modularity, Evolution and Cognition Research, the other showing morphological integration. We measure a primary variational Martinstrasse 12, 3400 Klosterneuburg, Austria. property of the systems, their distribution of fitness effects of mutation. We find Email: [email protected] that the modular ontogeny shows the evolution of mutational robustness and ontogenic simplification, while the integrated ontogeny does not. We discuss the wider use of this methodology on other computational models of development as well as real organisms. 1 | INTRODUCTION summarize, “almost anything can be changed if it shows phenotypic variation” and “combinations of traits, even The idea that phenotypic variation could ever be those unfavorably correlated, can be changed”). Once the “unbiased” is a historical artifact coming from two main premises of this “pan‐variationism” are accepted, pans- sources: First were the early characterizations of quanti- electionism is the natural conclusion—in other words, if tative genetic variation for single traits or small numbers variation is diffusing in every phenotypic direction, the of traits. Such measurements are actually projections of only source of information that shapes an organism’s the extremely high dimensionality of organismal proper- phenotype is natural selection. ties into very few dimensions. When the additive genetic Breaking away from the pan‐variationist paradigm has variance was measured for these low‐dimensional been a prolonged process. Rupert Riedl’sanalysiswas projections, positive additive genetic variance was dis- pioneering in this regard (Riedl, 1975, 1977, 1978), pointing covered to be ubiquitous, and this finding was confirmed out that the very ability to infer phylogenies based on by selective breeding experiments that showed popula- phenotypes requires that natural selection be limited in its tions were widely responsive to artificial selection, so ability to erase the phenotypes of the past. Riedl proposed, these trait values could be changed. The second step was furthermore, that complex phenotypes which require multi- the extrapolation of these low‐dimensional observations ple simultaneous trait changes would often be impossible to to the entire organism, resulting in the adoption of the evolve without the advent of genes that happen to make the classical infinitesimal model of quantitative genetics right pleiotropic combination of multiple trait changes, and (Barton, Etheridge, & Véber, 2017; Fisher, 1919) as a that such genes produce a genotype–phenotype map “in plausible model for the entire, high‐dimensional orga- which gene interactions have imitated the patterns of nismal phenotype, in which genetic variation is essen- functional interactions in the phenome” (Riedl, 1977). tially a gas that can fill any selective phenotypic bottle In the 1980s came the identification of “developmen- (enunciated by Hill & Kirkpatrick (2010) who tal constraints” as sources of information other than Evolution & Development. 2019;1–15. wileyonlinelibrary.com/journal/cae © 2019 Wiley Periodicals, Inc. | 1 2 | HORDIJK AND ALTENBERG natural selection that shape evolution (Bonner, 1982). The dynamics of epigenesis and ontogeny result from Subsequently, it was recognized that development also the complex interactions of the genome, oocyte cytoplas- focuses phenotypic variation along certain dimensions, mic structure, and multicellular interactions once embry- thus enhancing the likelihood of evolution taking these ogenesis has begun. This entire process we can refer to as paths (Roth & Wake, 1985). Together these have been the generative properties of the genotype–phenotype map. combined into the concept of “developmental bias” The problem of concern here—how changes in the (Yampolsky & Stoltzfus, 2001). genome map to changes in the phenotype—can be This term “bias,” however, still makes reference to the referred to as the variational properties of the genotype pan‐variationist frame (Fillmore, 1976; Lakoff, 2014) that —phenotype map. This distinction, introduced in Alten- poses “unbiased” phenotypic variation as somehow a berg (1995), has been found to be useful in a number of viable concept, and even perhaps a most‐parsimonious studies (cf. de la Rosa 2014; Jernvall & Jung, 2000; null hypothesis. Laubichler, Prohaska, & Stadler, 2018; Porto, Schmelter, When viewed from the vantage point of develop- VandeBerg, Marroig, & Cheverud, 2016; Reiss et al., 2008; mental mechanisms, it is clear that the processes that Salazar‐Ciudad, 2006). The way the phenotype is produce the physical structures of organisms will also generated from the genotype during development is structure the phenotypic variation that results from clearly what structures phenotypic variation, so the genetic and environmental variation. The idea of variational properties are derived from the generative. “unbiased” phenotypic variation is, from this vantage There is yet no general theory as to how evolution point, as sensible as the idea of “unstructured develop- may shape the variational structure of phenotypic ment”—which of course, makes no sense at all. variation. Many computational models have been ex- The question we need to be asking, then, is not how plored to try to tease out the regularities which may be development biases phenotypic variation, but rather, how eventually guide us to a general theory (Dong & Liu, development structures it. This is the framework that we 2017; Scholes, DePace, & Sánchez, 2017; Tsuchiya, adopt here. It is hypothesized in Altenberg (2005) (and Giuliani, Hashimoto, Erenpreisa, & Yoshikawa, 2016). more recently Runcie & Mukherjee (2013)) that organis- Here, we revisit one such computational model which mal variation actually lies on very low‐dimensional has produced intriguing results: the cellular automaton manifolds embedded within the high‐dimensional trait model of development, EvCA. space. Within this framework, a principal open problem The cellular automaton is, as the name suggests, an in evolutionary theory is to understand in what ways the autonomous dynamical system. Its dynamics are speci- developmental structuring of phenotypic variation may fied by initial conditions of its multivariable state, and itself be systematically shaped by the evolutionary rules which determine the changes of the state from one process. That is the question pursued here. time to the next. Cellular automata (CAs) exhibit complex attractors and domains of attraction, and more- over, allow one to watch their development progress. 1.1 Ontogeny as a dynamical system | Here, we will define a task that the CA must achieve to Ontogeny in many animals exhibits several properties of stay “alive,” and model a population of evolving CAs an autonomous dynamical system, in which the trajec- whose determining rules are mutated and recombined tory of the system is set by the initial conditions. In and subject to selection. common animal development, the mother creates the initial conditions for the zygote (often some form of egg), 1.1.1 The distribution of fitness effects and development occurs in isolation from environmental | of mutation interaction, until the organism “hatches.” Development may be less autonomous in placental mammals because Traditionally, the quantity of interest in evolutionary of continuing material inputs from the mother, but most simulations has been the fitness of the digital organisms. of the dynamical interactions of development occur Here our focus is on the variational properties of these internally within the embryo and fetus itself. organisms, and we investigate a principal quantification It is a generic property of dynamical systems that the of how genetic variation maps to phenotypic variation, trajectories converge toward attractors. Attractors may be the distribution of fitness effects (DFE) of mutation, which fixed points, cycles, or strange attractors. Whole subsets is the probability distribution over the changes in fitness of different initial conditions will converge toward the caused by mutation of a given genotype. same attractor, comprising its domain of attraction. The DFE of mutation provides a quantitative way to Different domains of attraction will converge toward characterize the widely used concepts of mutational different attractors. robustness, deleterious mutation, and evolvability. It is HORDIJK AND ALTENBERG | 3 the probability distribution of mutant offspring’s fitnesses explicitly observed, and how it can be analyzed in this relative to the parent’s fitness. The distribution can be model. It is our hope that these results and this divided into three regions: (a) the lower tail, comprising methodology could lead to suggestions for actual experi- deleterious mutations; (b) the measure near 1 (the ments and predictions on the
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