Paper by CQM Authors Noted as BMC Systems Biology Editor’s Pick, Highly Accessed

Figure Comparison between the true IRMA network and the networks inferred by our algorithm
The experiment measured the expression level of 5 genes after a shift from galactose-raffinose- to glucose-containing medium. a) The true Yeast Synthetic Network; b) The inferred static network from the Switch ON data set; c) The inferred static network from the Switch OFF data set. Solid gray edges represent inferred interactions that are not present in the real network, or that have the wrong direction (false positives), and dotted gray lines represent false negatives.

A new paper by Dr. M. Paola Vera-Licona and Dr. Reinhard Laubenbacher entitled “An algebra-based method for inferring gene regulatory networks” published in BMC Systems Biology has been noted as an Editor’s pick. It’s also ranked as a highly accessed article. The paper, found in BMC Systems Biology 2014, 8:37, describes a new gene regulatory network inference method based on polynomial dynamical systems incorporates time-resolved gene expression data and existing causal information and into its model inference and uses and evolutionary algorithm for parameter estimation. Congratulations Dr. Vera-Licona and Laubenbacher!  Check out the paper at

Paola Vera-Licona, Abdul Jarrah, Luis David Garcia-Puente, John McGee, Reinhard Laubenbacher. An algebra-based method for inferring gene regulatory networks. BMC Syst Biol. 2014; 8: 37. Published online 2014 March 26. doi: 10.1186/1752-0509-8-37.

Download a full text PDF version of An algebra-based method for inferring gene regulatory networks.

Informatics and Data Analytics Short Course

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