Evolving genetic regulatory networks for systems biology

Chu, Dominique

doi:10.1109/cec.2007.4424562

Cited by 15 publications

(16 citation statements)

References 20 publications

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“…These results highlighted the requirements for the determination of better evolutionary constraints to evolve biologically plausible ACSNs. These findings would support the arguments outlined by Chu [2] where he identified a number of currently open problems regarding the methodologies employed to evolve artificial biochemical networks.…”

Section: A Top-down Approachsupporting

confidence: 77%

Modeling and evolving biochemical networks: insights into communication and computation from the biological domain

Decraene¹,

Mitchell²,

McMullin³

2008

China-Ireland International Conference on Information and Communications Technologies (CIICT 2008)

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This paper is concerned with the modeling and evolving of Cell Signaling Networks (CSNs) in silico. CSNs are complex biochemical networks responsible for the coordination of cellular activities. We examine the possibility to computationally evolve and simulate Artificial Cell Signaling Networks (ACSNs) by means of Evolutionary Computation techniques. From a practical point of view, realizing and evolving ACSNs may provide novel computational paradigms for a variety of application areas. For example, understanding some inherent properties of CSNs such as crosstalk may be of interest: A potential benefit of engineering crosstalking systems is that it allows the modification of a specific process according to the state of other processes in the system. This is clearly necessary in order to achieve complex control tasks. This work may also contribute to the biological understanding of the origins and evolution of real CSNs. An introduction to CSNs is first provided, in which we describe the potential applications of modeling and evolving these biochemical networks in silico. We then review the different classes of techniques to model CSNs, this is followed by a presentation of two alternative approaches employed to evolve CSNs within the ESIGNET project 1 . Results obtained with these methods are summarized and discussed.

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Section: A Top-down Approachsupporting

confidence: 77%

Modeling and evolving biochemical networks: insights into communication and computation from the biological domain

Decraene¹,

Mitchell²,

McMullin³

2008

China-Ireland International Conference on Information and Communications Technologies (CIICT 2008)

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show abstract

“…The use of features in fitness functions in this manner has been limited in GPs to date. Borrelli et al incorporated a small number of basic features in a multi-objective GP [9] for symbolic regression with added noise, while Chu [12] and Leier et al [41] used single features to specifically target oscillating behaviour of probabilistic models. The fitness function explored in this thesis makes use of a larger set of features and is tested on a variety of behaviours produced by both expressions with added noise and probabilistic networks.…”

Section: Justificationmentioning

confidence: 99%

“…• Chu [12] and Leier et al [41] used features to evolve probabilistic GRN models exhibiting oscillating behaviour.…”

Section: Feature-based Fitness Functions To Learn Dynamic Modelsmentioning

confidence: 99%

“…• To target oscillation, Chu [12] based the fitness function on autocorrelation, thus focussing on matching a specific periodicity and tolerating variation in phase and amplitude.…”

Section: Fitness Functions Used To Learn Grn Modelsmentioning

confidence: 99%

“…• Chu [12] used the Gibson-Bruck algorithm to simulate a set of reactions and rates obtained from an evolutionary algorithm, targeting oscillating behaviour.…”

Section: Learning Probabilistic Grn Models With Evolutionary Algorithmsmentioning

confidence: 99%

See 2 more Smart Citations

Evolutionary Synthesis of Stochastic Gene Network Models Using Feature-based Search Spaces

Imada

Ross

2011

New Gener. Comput.

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A feature-based fitness function is applied in a genetic programming system to synthesize stochastic gene regulatory network models whose behaviour is defined by a time course of protein expression levels. Typically, when targeting time series data, the fitness function is based on a sum-of-errors involving the values of the fluctuating signal. While this approach is successful in many instances, its performance can deteriorate in the presence of noise. This thesis explores a fitness measure determined from a set of statistical features characterizing the time series' sequence of values, rather than the actual values themselves.Through a series of experiments involving symbolic regression with added noise and gene regulatory network models based on the stochastic π-calculus, it is shown to successfully target oscillating and non-oscillating signals. This practical and versatile fitness function offers an alternate approach, worthy of consideration for use in algorithms that evaluate noisy or stochastic behaviour.

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Modeling dynamic gene expression in STREPTOMYCES COELICOLOR: Comparing single and multi‐objective setups

Thomas

Jin

Laing

et al. 2016

Evolutionary Computation in Gene Regulatory Network Research

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Evolving genetic regulatory networks for systems biology

Cited by 15 publications

References 20 publications

Modeling and evolving biochemical networks: insights into communication and computation from the biological domain

Modeling and evolving biochemical networks: insights into communication and computation from the biological domain

Evolutionary Synthesis of Stochastic Gene Network Models Using Feature-based Search Spaces

Modeling dynamic gene expression in STREPTOMYCES COELICOLOR: Comparing single and multi‐objective setups

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