Gene regulatory networks

Baltazar Aguda, Georghe Craciun and Rengul Cetin-Atalay, Data Sources and Computational Approaches for Generating Models of Gene Regulatory Networks. 

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Margolin A, et al. ARACNE An algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformat. 2006 7(Suppl 1) S7. 

Takayanagi S, Fukuda R, Takeuchi Y, Tsukada S, Yoshida K (2013) Gene regulatory network of unfolded protein response genes in endoplasmic reticulum stress. Cell Stress Chaperones 18(1) 11-23. doi l0.1007/sl2192-012-0351-5... 

Zhang Q, Andersen M (2007) Dose response relationship in anti-stress gene regulatory networks. PLoS ComputBiol 3 e24. doi 10.1371/ journal.pcbi.0030024.eor... 

Li Z, Li P, Krishnan A, Liu JD (2011) Large-scale dynamic gene regulatory network inference combining differential equation models with local dynamic Bayesian network analysis. Bioinformatics 27 2686-2691. doi 10.1093/ bioinformatics/btr454... 

Vlany other gene-regulatory networks function in ways analogous to those of the lac operon. For example, genes taking part in purine and, to a lesser degree, pyrimidine biosynthesis are repressed by the pur repressor. This dimeric protein is 31% identical in sequence with the lac repressor and has a... 

Sources and Computational Approaches for Generating Models of Gene Regulatory Networks. 

Some bioinformatics problems can be formulated as MOOPs, for instance the sequence alignment of DNA or protein sequences, protein structure prediction and design, and inference of gene regulatory networks, just to mention a few. The interested reader is referred to the review of Handl et al. (2007) that covers in detail more MOO applications in bioinformatics. The next few paragraphs describe some of the current applications in bioinformafics of interest to the chemical engineering community. 

Dasika M. S., Gupta A., Maranas C. D. and Varner J. D. (2004). Mixed integer linear programming (MILP) framework for inferring time delay in gene regulatory network. Pacific Symposium on Biocomputing. 9, pp 474-485. 

Zou M. and Conzen S. D. (2004). A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data. Bioinformatics. 21, pp. 71-79. 

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