Circuit topology and the evolution of robustness in two-gene circadian oscillators

Wagner, Andreas

doi:10.1073/pnas.0501094102

Cited by 141 publications

(129 citation statements)

References 57 publications

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“…A similar definition (favorable outcome divided by total number of simulations) has been used before in related studies (Wagner 2005 …”

Section: Oscillatory Robustnessmentioning

confidence: 99%

“…Several studies have made contributions towards identifying some design principles for oscillatory behaviour. Network motifs-recurrent patterns of interactions believed to form the building blocks of any complex network (Milo et al 2002;Yeger-Lotem et al 2004;Barabasi and Oltvai 2004;Alon 2007;Kim et al 2010)-have also been highlighted to some extent for oscillators (Ferrell et al 2011;Wagner 2005;Novak and Tyson 2008;Tsai et al 2008;Burda et al 2011;Lomnitz and Savageau 2014;Noman et al 2015;Semenov et al 2015).…”

Section: Introductionmentioning

confidence: 99%

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Design principles for robust oscillatory behavior

2015

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Oscillatory responses are ubiquitous in regulatory networks of living organisms, a fact that has led to extensive efforts to study and replicate the circuits involved. However, to date, design principles that underlie the robustness of natural oscillators are not completely known. Here we study a three-component enzymatic network model in order to determine the topological requirements for robust oscillation. First, by simulating every possible topological arrangement and varying their parameter values, we demonstrate that robust oscillators can be obtained by augmenting the number of both negative feedback loops and positive autoregulations while maintaining an appropriate balance of positive and negative interactions. We then identify network motifs, whose presence in more complex topologies is a necessary condition for obtaining oscillatory responses. Finally, we pinpoint a series of simple architectural patterns that progressively render more robust oscillators. Together, these findings can help in the design of more reliable synthetic biomolecular networks and may also have implications in the understanding of other oscillatory systems.

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“…A similar definition (favorable outcome divided by total number of simulations) has been used before in related studies (Wagner 2005 …”

Section: Oscillatory Robustnessmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design principles for robust oscillatory behavior

2015

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“…From a network perspective, there may be no strong constraint on individual regulatory links (Wagner 2005;Ciliberti et al 2007a, b). A configuration of a transcription network with defined links between genes is referred to as the network ''genotype.''…”

Section: Mirnas and Expression Buffering-evidence From Evolutionary Cmentioning

confidence: 99%

Evolution under canalization and the dual roles of microRNAs—A hypothesis

Wu¹,

Shen²,

Tang³

2009

Genome Res.

147

160

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Canalization refers to the process by which phenotypes are stabilized within species. Evolution by natural selection can proceed efficiently only when phenotypes are canalized. The existence and identity of canalizing genes have thus been an important, but controversial topic. Recent evidence has increasingly hinted that microRNAs may be involved in canalizing gene expression. Their paradoxical properties (e.g., strongly conserved but functionally dispensable) suggest unconventional regulatory roles. We synthesized published and unpublished results and hypothesize that miRNAs may have dual functions-in gene expression tuning and in expression buffering. In tuning, miRNAs modify the mean expression level of their targets, but in buffering they merely reduce the variance around a preset mean. In light of the constant emergence of new miRNAs, we further discuss the relative importance of these two functions in evolution.Living organisms process as much information and execute as many instructions as any high-power computers normally do. The difference is that living organisms carry out the tasks under extremely variable environments, whereas no computers are made to withstand even moderate fluctuations in voltage input. Living things must be able to dampen variable inputs (in nutrition, temperature, humidity, genetic background, etc.) to achieve the remarkable stability in the output (development, physiological responses, gene expression, etc.). This phenomenon is the essence of biological homeostasis.Darwin's The Origin of Species (Darwin 1859), on the other hand, is about evolutionary changes. On the surface, evolutionary changes and phenotypic stability may seem like antithetical concepts, but in the language of genetics they are really two sides of the same issue. Evolution by natural selection can proceed only when biological systems are reasonably stable. Imagine a system in which phenotypic manifestation is highly variable. In it natural selection cannot easily distinguish one genotype from another and would have low efficacy. The original nongenetic version of the Darwinian theory encountered many difficulties. One of them was the blending of genetic materials, potentially leading to the homogenization of phenotypes. The other resulted from the ignorance of the difference between genotype and phenotype and the puzzle over the existence of phenotypic variation. Fisher (1930) pointed out that, in Darwin's thinking, phenotypic variation should have been purged by natural selection-only the fittest should have remained.The relationship between evolution and biological homeostasis can best be expressed in quantitative genetic terms. Let V g and V e denote phenotypic variance due to genotypic and environmental effect, respectively. V g 3 e is the interactive term between gene and environment. The total phenotypic variance, V t , can be expressed as follows:For simplicity, we assume no dominance here, but the general principle is the same in more complex systems. Any mechanism of biological homeostasis should redu...

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“…In a transcriptional regulation circuit, for example, any one gene X can have an activating, repressing, or no effect on the expression of another gene Y, as determined by regulatory DNA sequences near gene Y. The space of all circuits then comprises all possible pairwise patterns of interactions between a given set of genes [14][15][16]48,49]. These interactions give rise to the gene expression phenotype of a circuit.…”

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confidence: 99%

Genotype networks shed light on evolutionary constraints

Wagner

2011

Trends in Ecology & Evolution

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An evolutionary constraint is a bias or limitation in phenotypic variation that a biological system produces. One can distinguish physicochemical, selective, genetic and developmental causes of such constraints. Here, I discuss these causes in three classes of system that bring forth many phenotypic traits and evolutionary innovations: regulatory circuits, macromolecules and metabolic networks. In these systems, genotypes with the same phenotype form large genotype networks that extend throughout a vast genotype space. Such genotype networks can help unify different causes of evolutionary constraints. They can show that these causes ultimately emerge from the process of development; that is, how phenotypes form from genotypes. Furthermore, they can explain important consequences of constraints, such as punctuated stasis and canalization. An evolutionary constraint is a bias or limitation in phenotypic variation that a biological system produces. One can distinguish physicochemical, selective, genetic and developmental causes of such constraints. Here, I discuss these causes in three classes of systems that bring forth many phenotypic traits and evolutionary innovations: regulatory circuits, macromolecules and metabolic networks. In these systems, genotypes with the same phenotype form large genotype networks that extend throughout a vast genotype space. Such genotype networks can help unify different causes of evolutionary constraints. They can show that these causes ultimately emerge from the process of development: that is, how phenotypes form from genotypes. Furthermore, they can explain important consequences of constraints, such as punctuated stasis and canalization. Evolutionary constraintsNo organism or species can produce every conceivable kind of phenotypic variation. This limitation is encapsulated in the notion of constraints on phenotypic evolution [1]. An evolutionary constraint is a bias or limitation in phenotypic variation that a biological system produces. Extreme examples include the absence of photosynthesis in higher animals, the general lack of teeth in the lower jaw of frogs, the absence of palm trees in cold climates and the absence of birds that give birth to live young instead of to eggs [1,2]. In these examples, a trait is completely absent. More subtle constraints manifest themselves as correlations among different characters. A paradigmatic case is allometric scaling [2]. Here, the value of one quantitative trait constrains that of another, typically via a specific nonlinear relationship, For example, metabolic rate is proportional to body mass m raised to approximately three quarter power (m 0.75 ) across many differentIt is not hard to see that constraints can influence the spectrum of evolutionary adaptations and innovations that are accessible to living things. For this reason, questions about the causes and consequences of constrained evolution have attracted much attention [1,[4][5][6][7][8][9][10]. There are multiple kinds and causes of phenotypic constraints (Box 1),...

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Circuit topology and the evolution of robustness in two-gene circadian oscillators

Cited by 141 publications

References 57 publications

Design principles for robust oscillatory behavior

Design principles for robust oscillatory behavior

Evolution under canalization and the dual roles of microRNAs—A hypothesis

Genotype networks shed light on evolutionary constraints

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