Design of genetic networks with specified functions by evolution
            <i>in silico</i>

François, Paul; Hakim, Vincent

doi:10.1073/pnas.0304532101

Cited by 263 publications

(244 citation statements)

References 33 publications

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“…A related circuit, the mixed feedback loop, in which A is a repressor to B and the A and B proteins bind to form a complex was recently studied using rate equations [29,30]. It was found to exhibit bistability within a range of parameters.…”

Section: Discussionmentioning

confidence: 99%

Stochastic simulations of genetic switch systems

et al. 2007

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Genetic switch systems with mutual repression of two transcription factors are studied using deterministic methods (rate equations) and stochastic methods (the master equation and Monte Carlo simulations). These systems exhibit bistability, namely two stable states such that spontaneous transitions between them are rare. Induced transitions may take place as a result of an external stimulus. We study several variants of the genetic switch and examine the effects of cooperative binding, exclusive binding, protein-protein interactions and degradation of bound repressors. We identify the range of parameters in which bistability takes place, enabling the system to function as a switch. Numerous studies have concluded that cooperative binding is a necessary condition for the emergence of bistability in these systems. We show that a suitable combination of network structure and stochastic effects gives rise to bistability even without cooperative binding. The average time between spontaneous transitions is evaluated as a function of the biological parameters.

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Section: Discussionmentioning

confidence: 99%

Stochastic simulations of genetic switch systems

et al. 2007

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“…We assumed that cell fitness could be estimated in terms of the S exp objective function. This allowed the study of GTRN adaptation under changing environments in one (Δv k¼i ≠ 0 and Δv k≠i ¼ 0) or multiple (Δv k ≠ 0∀k) directions (14). To do this, we defined the optimality degree, ξ Δvk , in a target environment characterized by Δv Ã k and different from the optimal environment as the difference between S exp evaluated in an environment containing Δv k ¼ 0 (i.e., fitness in the optimal condition) and that evaluated in the target environment containing Δv Ã k .…”

Section: Methodsmentioning

confidence: 99%

“…The computational design of small TRNs was first proposed by using computational evolution with a system of ODEs describing the TRN (14), although no nucleotide sequence was generated for the evolved TRN. Recently, the use of a modular approach based on the assembly of biological part models has allowed the assignation of nucleotide sequences to the evolved TRN (15), which opened the door to the automatic design of genomic-sized sequences.…”

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confidence: 99%

Computational design of genomic transcriptional networks with adaptation to varying environments

Carrera

Elena

Jaramillo

2012

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

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Transcriptional profiling has been widely used as a tool for unveiling the coregulations of genes in response to genetic and environmental perturbations. These coregulations have been used, in a few instances, to infer global transcriptional regulatory models. Here, using the large amount of transcriptomic information available for the bacterium Escherichia coli, we seek to understand the design principles determining the regulation of its transcriptome. Combining transcriptomic and signaling data, we develop an evolutionary computational procedure that allows obtaining alternative genomic transcriptional regulatory network (GTRN) that still maintains its adaptability to dynamic environments. We apply our methodology to an E. coli GTRN and show that it could be rewired to simpler transcriptional regulatory structures. These rewired GTRNs still maintain the global physiological response to fluctuating environments. Rewired GTRNs contain 73% fewer regulated operons. Genes with similar functions and coordinated patterns of expression across environments are clustered into longer regulated operons. These synthetic GTRNs are more sensitive and show a more robust response to challenging environments. This result illustrates that the natural configuration of E. coli GTRN does not necessarily result from selection for robustness to environmental perturbations, but that evolutionary contingencies may have been important as well. We also discuss the limitations of our methodology in the context of the demand theory. Our procedure will be useful as a novel way to analyze global transcription regulation networks and in synthetic biology for the de novo design of genomes.automated design | synthetic genomics | genome refactoring | evolutionary computation O rganisms have evolved mechanisms for regulating transcription to better adapt to changing environments. Could such regulation be engineered in a different way (1, 2)? Recent experiments investigating the evolvability of bacterial transcriptional regulatory networks (TRNs) have shown that the massive addition of new links to the network does not significantly alter cell growth. Isalan et al. (3) added transcriptional fusions of promoters with different master transcriptional regulators and showed that Escherichia coli (E. coli) tolerated almost all rewired networks; however, growth was perturbed by as much as 5% (3). This inherent predisposition of E. coli networks to dampen extreme changes in their circuitry enables the possibility of conducting genome-wide rewiring (4). Global transcription regulation could also be analyzed by comparing the regulatory models from distant organisms, provided they show a similar response to the set of studied environments. In this way, they could provide alternative regulatory models, although the lack of knowledge of species-specific selective pressures may blur the conclusions. We will propose here an alternative evolution experiment, which will be conducted computationally thanks to the availability of a quantitative model for the genom...

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“…Firstly, on a technical side, I will introduce a system similar to the one by Francois and Hakim to evolve oscillating genetic regulatory networks. This system will be used in order to (i) extend on the results reported by Francois and Hakim in [16] where only very little information is given about the performance of their method and the range of results they obtained. (ii) Another (minor) objective is to compare these results to the regulatory networks evolved by Knabe and coworkers and Drennan and Beer.…”

Section: A Objective Of This Contributionmentioning

confidence: 99%

“…A more recent attempt to use evolutionary computation to find genetic regulatory networks that show a user-defined behaviour is by Francois and Hakim [16]; they describe an algorithm to evolve genetic regulatory networks with oscillatory dynamics. Francois and Hakim use a simple genetic algorithm to evolve both the structure (i.e.…”

Section: Previous Workmentioning

confidence: 99%

Evolving genetic regulatory networks for systems biology

Chu

2007

2007 IEEE Congress on Evolutionary Computation

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Abstract-Recently there has been significant interest in evolving genetic regulatory networks with a user-determined behaviour. It is unclear whether or not artificial evolution of biochemical networks can be of direct benefit for or biological relevance to Systems Biology. This article highlights some pitfalls when concluding from artificially evolved genetic regulatory networks to real networks. This article also gives a (brief) review of some previous attempts to evolve genetic regulatory networks with oscillatory behaviour; it also describes a new system to evolve networks and describes the networks that have been evolved. These networks seem to be very diverse sharing no apparent common motifs either with one another or with their real-life counterparts.

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Design of genetic networks with specified functions by evolution in silico

Cited by 263 publications

References 33 publications

Stochastic simulations of genetic switch systems

Stochastic simulations of genetic switch systems

Computational design of genomic transcriptional networks with adaptation to varying environments

Evolving genetic regulatory networks for systems biology

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