Robustness Can Evolve Gradually in Complex Regulatory Gene Networks with Varying Topology

Ciliberti, Stefano; Martin, O. C.; Wagner, Andreas

doi:10.1371/journal.pcbi.0030015

Cited by 348 publications

(479 citation statements)

References 68 publications

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“…1 b and c) in this space. We had previously called this graph a metagraph (a graph of graphs) because each network can be viewed itself as a graph (31). However, for consistency with established terminology, we here refer to this graph as a neutral network (15).…”

Section: Resultsmentioning

confidence: 99%

Innovation and robustness in complex regulatory gene networks

Ciliberti

Martin

Wagner

2007

Proc. Natl. Acad. Sci. U.S.A.

329

400

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The history of life involves countless evolutionary innovations, a steady stream of ingenuity that has been flowing for more than 3 billion years. Very little is known about the principles of biological organization that allow such innovation. Here, we examine these principles for evolutionary innovation in gene expression patterns. To this end, we study a model for the transcriptional regulation networks that are at the heart of embryonic development. A genotype corresponds to a regulatory network of a given topology, and a phenotype corresponds to a steady-state gene expression pattern. Networks with the same phenotype form a connected graph in genotype space, where two networks are immediate neighbors if they differ by one regulatory interaction. We show that an evolutionary search on this graph can reach genotypes that are as different from each other as if they were chosen at random in genotype space, allowing evolutionary access to different kinds of innovation while staying close to a viable phenotype. Thus, although robustness to mutations may hinder innovation in the short term, we conclude that long-term innovation in gene expression patterns can only emerge in the presence of the robustness caused by connected genotype graphs.evolutionary novelty ͉ evolvability ͉ genotype-phenotype maps L ife's enormous creativity is evident from earth's millions of species with unique life styles, from dazzlingly different modes of development to macromolecules, like proteins and RNA, in which many different molecular functions (catalysis, support, and communication) have evolved. There are many wonderful case studies of individual evolutionary innovations, from the beaks of Darwin's finches (1) to the biochemical innovations represented by the highly refractory eye lens proteins derived from various enzymes (2, 3). These and all other evolutionary innovations are produced by a combination of mutation and natural selection, without apparent foresight and planning. However, mutation and selection do not automatically produce evolutionary innovation. For instance, man-made systems, such as computer hardware and software, seem to be outright incapable of innovation through mutation and selection. Those complex systems exhibit brittleness: Modifying one component often leads to disastrous failure. Diligent research in areas such as ''evolvable hardware'' (4-6) is needed to understand how complex functionalities can be rendered insensitive to individual component changes, thereby facilitating innovation. It is important to discover what renders living beings so capable of innovation, partly because the lessons learned could be applied to the design of complex systems with specific functions.Biologists increasingly realize that genetic systems need to be robust to both genetic and nongenetic change (7-14). Robustness means that a system keeps performing its function in the face of perturbations. For example, many proteins can continue to catalyze chemical reactions, regulate transcription, communicate signals, and serve ot...

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Section: Resultsmentioning

confidence: 99%

Innovation and robustness in complex regulatory gene networks

Ciliberti

Martin

Wagner

2007

Proc. Natl. Acad. Sci. U.S.A.

329

400

View full text Add to dashboard Cite

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“…Here, however, we need to consider the dynamics to shape the phenotype (ii) [5,[13][14][15]. If we adopt statistical-mechanical formulation, we need two 'energy'-like functions, one for fitness and the other for Hamiltonian for the development dynamics, as is formulated by two-temperature statistical physics [33,34].…”

Section: Dynamical-systems Approach To Evolution-development Relationmentioning

confidence: 99%

“…Robustness, on the other hand, is defined as the ability to function against possible disturbances in the system [7][8][9][10][11][12][13]. Such disturbances have two distinct origins: non-genetic and genetic.…”

Section: Introductionmentioning

confidence: 99%

Evolution of Robustness and Plasticity under Environmental Fluctuation: Formulation in Terms of Phenotypic Variances

Kaneko

2012

J Stat Phys

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The characterization of plasticity, robustness, and evolvability, an important issue in biology, is studied in terms of phenotypic fluctuations. By numerically evolving gene regulatory networks, the proportionality between the phenotypic variances of epigenetic and genetic origins is confirmed. The former is given by the variance of the phenotypic fluctuation due to noise in the developmental process; and the latter, by the variance of the phenotypic fluctuation due to genetic mutation. The relationship suggests a link between robustness to noise and to mutation, since robustness can be defined by the sharpness of the distribution of the phenotype. Next, the proportionality between the variances is demonstrated to also hold over expressions of different genes (phenotypic traits) when the system acquires robustness through the evolution. Then, evolution under environmental variation is numerically investigated and it is found that both the adaptability to a novel environment and the robustness are made compatible when a certain degree of phenotypic fluctuations exists due to noise. The highest adaptability is achieved at a certain noise level at which the gene expression dynamics are near the critical state to lose the robustness. Based on our results, we revisit Waddington's canalization and genetic assimilation with regard to the two types of phenotypic fluctuations.

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“…Much of this work is based on statistical genetics (Via and Lande 1985;Wagner et al 1997;Rice 1998;Gibson and Wagner 2000;Kawecki 2000;Lande 2009), or uses simulations with small networks (Wagner 1996;Frank 1999;Becskei and Serrano 2000;Omholt et al 2000;Gibson 2002;Meir et al 2002;Flatt 2005;Ciliberti et al 2007) to deduce general conditions, like network topology, degree of modularity, nonlinear interaction, fluctuating environment, and variance-covariance structure, under which selection could lead to phenotypic stability or plasticity.…”

Section: Introduction: Robustness and Plasticitymentioning

confidence: 99%

Systems Biology of Phenotypic Robustness and Plasticity

Nijhout

Sadre-Marandi

Best

et al. 2017

Integrative and Comparative Biology

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Synopsis Gene regulatory networks, cellular biochemistry, tissue function, and whole body physiology are imbued with myriad overlapping and interacting homeostatic mechanisms that ensure that many phenotypes are robust to genetic and environmental variation. Animals also often have plastic responses to environmental variables, which means that many different phenotypes can correspond to a single genotype. Since natural selection acts on phenotypes, this raises the question of how selection can act on the genome if genotypes are decoupled from phenotypes by robustness and plasticity mechanisms. The answer can be found in the systems biology of the homeostatic mechanisms themselves. First, all such mechanisms operate over a limited range and outside that range the controlled variable changes rapidly allowing natural selection to act. Second, mutations and environmental stressors can disrupt homeostatic mechanisms, exposing cryptic genetic variation and allowing natural selection to act. We illustrate these ideas by examining the systems biology of four specific examples. We show how it is possible to analyze and visualize the roles of specific genes and specific polymorphisms in robustness in the context of large and realistic nonlinear systems. We also describe a new method, system population models, that allows one to connect causal dynamics to the variable outcomes that one sees in biological populations with large variation.

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Robustness Can Evolve Gradually in Complex Regulatory Gene Networks with Varying Topology

Cited by 348 publications

References 68 publications

Innovation and robustness in complex regulatory gene networks

Innovation and robustness in complex regulatory gene networks

Evolution of Robustness and Plasticity under Environmental Fluctuation: Formulation in Terms of Phenotypic Variances

Systems Biology of Phenotypic Robustness and Plasticity

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