Learning Gene Regulatory Networks with Predefined Attractors for Sequential Updating Schemes Using Simulated Annealing

A simulated annealing framework is presented for learning gene regulatory networks with predefined attractors, under the threshold Boolean network model updated sequentially. The proposed method is used to study the robustness of the networks, defined as the number of different updating sequences they can have without loosing the attractor. The results suggests a power law between the frequency of the networks and the number of the updating sequences, also, a decrease of the networks’ robustness as the cycle length grows. In general, the proposed simulated annealing framework is effective for reverse engineering problems.

[1]  Eric Goles Ch.,et al.  Sequential operator for filtering cycles in Boolean networks , 2010, Adv. Appl. Math..

[2]  Satoru Miyano,et al.  Inferring Gene Regulatory Networks from Time-Ordered Gene Expression Data of Bacillus Subtilis Using Differential Equations , 2002, Pacific Symposium on Biocomputing.

[3]  Wei-Po Lee,et al.  Applying Intelligent Computing Techniques to Modeling Biological Networks from Expression Data , 2008, Genom. Proteom. Bioinform..

[4]  Dirk Repsilber,et al.  Reverse engineering of regulatory networks: simulation studies on a genetic algorithm approach for ranking hypotheses. , 2002, Bio Systems.

[5]  S. Kauffman.  Metabolic stability and epigenesis in randomly constructed genetic nets. , 1969, Journal of theoretical biology.

[6]  Alina Sîrbu,et al.  Comparison of evolutionary algorithms in gene regulatory network model inference , 2010, BMC Bioinformatics.

[7]  Satoru Miyano,et al.  Identification of Genetic Networks from a Small Number of Gene Expression Patterns Under the Boolean Network Model , 1998, Pacific Symposium on Biocomputing.

[8]  Paul P. Wang,et al.  Advances to Bayesian network inference for generating causal networks from observational biological data , 2004, Bioinform..

[9]  S Fuhrman,et al.  Reveal, a general reverse engineering algorithm for inference of genetic network architectures. , 1998, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[10]  Hitoshi Iba,et al.  Construction of genetic network using evolutionary algorithm and combined fitness function. , 2003, Genome informatics. International Conference on Genome Informatics.

[11]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[12]  Dianjing Guo,et al.  A new multiple regression approach for the construction of genetic regulatory networks , 2010, Artif. Intell. Medicine.

[13]  E. Álvarez-Buylla,et al.  Dynamics of the genetic regulatory network for Arabidopsis thaliana flower morphogenesis. , 1998, Journal of theoretical biology.

[14]  Edward R. Dougherty,et al.  Probabilistic Boolean Networks - The Modeling and Control of Gene Regulatory Networks , 2010 .

[15]  Eric Goles Ch.,et al.  On the robustness of update schedules in Boolean networks , 2009, Biosyst..

[16]  Chunguang Zhou,et al.  Reconstruction of Gene Regulatory Networks Based on Two-Stage Bayesian Network Structure Learning Algorithm , 2009 .

[17]  Richard Banks,et al.  Modelling and Analysing Genetic Networks: From Boolean Networks to Petri Nets , 2006, CMSB.

[18]  Yiannis N Kaznessis,et al.  Optimization of a stochastically simulated gene network model via simulated annealing. , 2006, Biophysical journal.