Seeking Postdoctoral Fellows in Bioinformatics and Computational Systems Biology

The Center for Quantitative Medicine is recruiting several postdoctoral fellows to pursue research in bioinformatics and computational systems biology with a particular focus on algorithm development and computational modeling of cellular processes, as well as applications of quantitative methods to biomedicine. Positions are available in the Laubenbacher, Mendes, and Vera-Licona research groups. Learn more about these exciting postdoctoral fellow positions in our employment opportunities page.

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 www.biomedcentral.com/1752-0509/8/37.

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.

Center for Quantitative Medicine Website Launches

Dr. Reinhard Laubenbacher and the Center for Quantitative is pleased to announce the launch of its website cqm.uchc.edu. The site brings the Center to Quantitative Medicine into cyberspace and was developed in partnership with the UConn Health Office of Communications. The Center for Quantitative Medicine website is designed to share information about the center’s research and education initiatives and to introduce its faculty to the UConn Health, UConn-Storrs, Jackson Laboratory for Genomic Medicine, and broader research community.

The site includes an event calendar and news feed, provides links to the CQM social media accounts, and provides information about the center, its history, work, and faculty and staff.

Please check out the site at http://cqm.uchc.edu and let us know what you think!