Reporter algorithm integrates omics data with metabolic network and thereby identifies metabolic regulatory hotspots. M1 - metabolite; G1-5 - upregulated genes; purple/ green/blue circles & squares - transcription factors and corresponding binding motifs. 

Mathematical and statistical tools provide essential theoretical basis to our quest to uncover basic principles underlying operation of cellular metabolic networks. To this end, we are developing new model formulations and algorithms that are motivated by the underlying biological questions (for more information, see our EMBLgroup page.

Holistic understanding of the functioning and regulation of complex metabolic networks requires identifying biologically meaningful operating points in a high-dimensional space. We use linear and mixed-integer linear programming (MILP) tools to tackle the resulting modeling challenges. We have developed several in silico models for quantitatively predicting metabolic phenotypes from a defined genotype. To enhance the predictive power of metabolic models, we are also developing methods to integrate genomic, transcriptomic, proteomic and metabolomic information.

Patil Group

Selected publications

Uncovering transcriptional regulation of metabolism by using metabolic network topology. Patil, K.R. & Nielsen, J. Proc Natl Acad Sci U S A. 2005 Feb 22;102(8):2685-9. Epub 2005 Feb 14. PubMed

Metabolic network topology reveals transcriptional regulatory signatures of type 2 diabetes. Zelezniak, A., Pers, T.H., Soares, S., Patti, M.E. & Patil, K.R. PLoS Comput Biol. 2010 Apr 1;6(4):e1000729. PubMed

Improved vanillin production in baker's yeast through in silico design. Brochado, A.R., Matos, C., Moller, B.L., Hansen, J., Mortensen, U.H. & Patil, K.R. Microb Cell Fact. 2010 Nov 8;9(1):84. PubMed