Shaping robust system through evolution (original) (raw)
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Evolution of robustness to noise and mutation in gene expression dynamics
PloS one, 2007
Phenotype of biological systems needs to be robust against mutation in order to sustain themselves between generations. On the other hand, phenotype of an individual also needs to be robust against fluctuations of both internal and external origins that are encountered during growth and development. Is there a relationship between these two types of robustness, one during a single generation and the other during evolution? Could stochasticity in gene expression have any relevance to the evolution of these types of robustness? Robustness can be defined by the sharpness of the distribution of phenotype; the variance of phenotype distribution due to genetic variation gives a measure of 'genetic robustness', while that of isogenic individuals gives a measure of 'developmental robustness'. Through simulations of a simple stochastic gene expression network that undergoes mutation and selection, we show that in order for the network to acquire both types of robustness, the ...
Evolution of robustness to noise and mutation in gene expression dynamics’, PLoS
2013
Phenotype of biological systems needs to be robust against mutation in order to sustain themselves between generations. On the other hand, phenotype of an individual also needs to be robust against fluctuations of both internal and external origins that are encountered during growth and development. Is there a relationship between these two types of robustness, one during a single generation and the other during evolution? Could stochasticity in gene expression have any relevance to the evolution of these types of robustness? Robustness can be defined by the sharpness of the distribution of phenotype; the variance of phenotype distribution due to genetic variation gives a measure of ‘genetic robustness’, while that of isogenic individuals gives a measure of ‘developmental robustness’. Through simulations of a simple stochastic gene expression network that undergoes mutation and selection, we show that in order for the network to acquire both types of robustness, the phenotypic varia...
Relevance of phenotypic noise to adaptation and evolution
IET Systems Biology, 2008
Biological processes are inherently noisy, as highlighted in recent measurements of stochasticity in gene expression. Here, the authors show that such phenotypic noise is essential to the adaptation of organisms to a variety of environments and also to the evolution of robustness against mutations. First, the authors show that for any growing cell showing stochastic gene expression, the adaptive cellular state is inevitably selected by noise, without the use of a specific signal transduction network. In general, changes in any protein concentration in a cell are products of its synthesis minus dilution and degradation, both of which are proportional to the rate of cell growth. In an adaptive state, both the synthesis and dilution terms of proteins are large, and so the adaptive state is less affected by stochasticity in gene expression, whereas for a non-adaptive state, both terms are smaller, and so cells are easily knocked out of their original state by noise. This leads to a novel, generic mechanism for the selection of adaptive states. The authors have confirmed this selection by model simulations. Secondly, the authors consider the evolution of gene networks to acquire robustness of the phenotype against noise and mutation. Through simulations using a simple stochastic gene expression network that undergoes mutation and selection, the authors show that a threshold level of noise in gene expression is required for the network to acquire both types of robustness. The results reveal how the noise that cells encounter during growth and development shapes any network's robustness, not only to noise but also to mutations. The authors also establish a relationship between developmental and mutational robustness.
Evolution of gene auto-regulation in the presence of noise
IET Systems Biology, 2009
Auto-regulatory negative feedback loops, where the protein expressed from a gene inhibits its own expression are common gene network motifs within cells. We investigate when will introducing a negative feedback mechanism be beneficial in terms of increasing a fitness function that is given by the probability of maintaining protein numbers above a critical threshold. Our results show the existence of a trade-off as introducing feedback decreases the average number of protein molecules driving this number closer to the critical threshold (which decreases fitness) but also reduces stochastic fluctuations around the mean (which increases fitness). We provide analytical conditions under which a negative feedback mechanism can evolve, i.e., introducing feedback will increase the above fitness. Analyses of these conditions show that negative feedbacks are more likely to evolve when (i) the source of noise in the protein population is extrinsic (i.e., noise is caused by fluctuations in exogenous signals driving the gene network) and not intrinsic (i.e., the randomness associated with mRNA/protein expression and degradation); (ii) the dynamics of the exogenous signal causing extrinsic noise is slower than the protein dynamics; and (iii) the critical threshold level for the protein number is low. We also show that mRNA/protein degradation rates are critical factors in determining whether transcription or translational negative feedback should evolve. In particular, when the mRNA half-life is much shorter than the protein's half-life, then a transcriptional negative feedback mechanism is more likely to evolve. On the other hand, a translational negative feedback mechanism is preferred with more stable mRNAs that have long half-lifes.
Global relationships in fluctuation and response in adaptive evolution
Journal of the Royal Society, Interface / the Royal Society, 2015
Cells change their internal state to adapt to environmental changes, and evolve in response to the new conditions. The phenotype changes first via adaptation in response to environmental changes, and then through mutational changes in the genomic sequence, followed by selection in evolution. Here, we analysed simulated adaptive evolution using a simple cell model consisting of thousands of intracellular components, and found that the changes in their concentrations by adaptation are proportional to those by evolution across all the components, where the proportion coefficient between the two agreed well with the change in the growth rate of a cell. Furthermore, we demonstrate that the phenotypic variance in concentrations of cellular components due to (non-genetic) noise and to genomic alternations is proportional across all components. This implies that the specific phenotypes that are highly evolvable were already given by non-genetic fluctuations. These global relationships in ce...
Robustness Can Evolve Gradually in Complex Regulatory Gene Networks with Varying Topology
PLOS Computational Biology, 2007
The topology of cellular circuits (the who-interacts-with-whom) is key to understand their robustness to both mutations and noise. The reason is that many biochemical parameters driving circuit behavior vary extensively and are thus not fine-tuned. Existing work in this area asks to what extent the function of any one given circuit is robust. But is high robustness truly remarkable, or would it be expected for many circuits of similar topology? And how can high robustness come about through gradual Darwinian evolution that changes circuit topology gradually, one interaction at a time? We here ask these questions for a model of transcriptional regulation networks, in which we explore millions of different network topologies. Robustness to mutations and noise are correlated in these networks. They show a skewed distribution, with a very small number of networks being vastly more robust than the rest. All networks that attain a given gene expression state can be organized into a graph whose nodes are networks that differ in their topology. Remarkably, this graph is connected and can be easily traversed by gradual changes of network topologies. Thus, robustness is an evolvable property. This connectedness and evolvability of robust networks may be a general organizational principle of biological networks. In addition, it exists also for RNA and protein structures, and may thus be a general organizational principle of all biological systems. Citation: Ciliberti S, Martin OC, Wagner A (2007) Robustness can evolve gradually in complex regulatory gene networks with varying topology. PLoS Comput Biol 3(2): e15.
Evolution of gene regulatory networks: Robustness as an emergent property of evolution
Physica A: Statistical Mechanics and its Applications, 2008
Gene Regulatory Networks (GRNs) have become a major focus of interest in recent years. Although much work has been done in elucidating the transcriptional regulatory network, the underlying mechanisms that have possibly influenced the evolution of these GRNs are still debatable. We have developed a framework to analyze the effect of objective functions, input types and starting populations on the evolution of GRNs with a specific emphasis on the robustness of evolved GRNs.
Estimations of intrinsic and extrinsic noise in models of nonlinear genetic networks
Chaos: An Interdisciplinary Journal of Nonlinear Science, 2006
We discuss two methods that can be used to estimate the impact of internal and external variability on nonlinear systems, and demonstrate their utility by comparing two experimentally implemented oscillatory genetic networks with different designs. The methods allow for rapid estimations of intrinsic and extrinsic noise and should prove useful in the analysis of natural genetic networks and when constructing synthetic gene regulatory systems.