PhD: Statistical learning to improve gene knockout predictions for metabolic networks Lille University, France (BioComputing team) http://www.lifl.fr/BioComputing/data/learningPhD2013.html Prediction algorithms from computer science will become increasingly relevant to guide the engineering of synthetic biological systems. An important instance is the prediction of gene knockouts from metabolic gene networks [2,6]. This problem is starting to gain industrial relevance for the production of bio-active products by bacteria. An important limitation of existing approaches is the absence of quantitative information on the strength of metabolic fluxes in such networks. We propose to remedy the situation by inferring such quantitative information from experimental data by applying statistical machine learning techniques [5,7], and developing prediction algorithms that benefit from the additional quantitative information, based on techniques from the area of formal program analysis. The supervisory team: J. Niehren (INRIA) and C. Kuttler (Lille 1 University), both from the LIFL's BioComputing team. Local collaborators will be, for statistical learning, M. Tommasi (Lille 3 University), and for the biological aspects P. Jacques and F. Coutte (ProBioGem, Polytech Lille). BioComputing and ProBioGem have already cooperated on related topics for two years [1,3,4]. ProBioGem will recruit a PhD student in biology, who will be cooperating with BioComputing's PhD student, on the wet lab side. * background: A Master's level with first-class academic credentials is required, preferably in Computer Science, with knowledge on formal methods. We might also consider candidates from biostatistics or maths. * duration: 3 years, starting from Sept/Oct 2013. * the project is fully funded, an open to students of any citizenship. * mail contact : bio-computing-apply@lists.gforge.inria.fr * Please contact us AS SOON AS POSSIBLE! Publications [1] F. Coutte, M. John, M. Bechet, M. Nebut, J. Niehren, V. Leclère, and P. Jacques. Synthetic Engi- neering of Bacillus subtilis to Overproduce Lipopeptide Biosurfactants. In 9th European Symposium on Biochemical Engineering Science, Istanbul, Turkey, 2012. [2] A. Goelzer, F. B. Brikci, I. M. Verstraete, P. Noirot, P. Bessieres, S. Aymerich, and V. Fromion. Re- construction and analysis of the genetic and metabolic regulatory networks of the central metabolism of Bacillus subtilis. BMC Systems Biology, 2(1):20+, 2008. [3] M. John, F. Coutte, M. Nebut, P. Jacques, and J. Niehren. Knockout Prediction for Reaction Networks with Partial Kinetic Information: Application to Surfactin Overproduction in Bacillus subtilis. In 3rd International Symposium on Antimicrobial Peptides, Lille, France, June 2012. [4] M. John, M. Nebut, and J. Niehren. Knockout Prediction for Reaction Networks with Partial Kinetic Information. In 14th International Conference on Verification, Model Checking, and Abstract Inter- pretation, volume 7737 of Lecture Notes in Computer Science, pages 355–374, Rome, Italy, Jan. 2013. Springer. [5] K. Murphy and S. Mian. Modelling gene expression data using dynamic Bayesian networks. Technical report, UC Berkeley, 1999. [6] N. D. Price, J. L. Reed, and B. Ø. Palsson. Genome-scale models of microbial cells: evaluating the consequences of constraints. Nature reviews. Microbiology, 2(11):886–897, Nov. 2004. [7] K. Y. Yeung, K. M. Dombek, K. Lo, J. E. Mittler, J. Zhu, E. E. Schadt, R. E. Bumgarner, and A. E. Raftery. Construction of regulatory networks using expression time-series data of a genotyped population. Proceedings of the National Academy of Sciences, 108(48):19436–19441, 2011. ----------------------------------------------------------------------------------------- Celine Kuttler, PhD Lille 1 University, LIFL http://www.lifl.fr/~kuttler phone: +33 3 20 33 59 55