000231783 001__ 231783
000231783 005__ 20180317095320.0
000231783 037__ $$aPOST_TALK
000231783 245__ $$aFacing The Unknown: Suggesting Alternative Novel Biochemistry in Escherichia coli and Saccharomyces cerevisiae
000231783 269__ $$a2017
000231783 260__ $$c2017
000231783 336__ $$aPosters
000231783 520__ $$aWhile advances in genome sequencing have greatly facilitated the inference of metabolic networks, the presence of unknown biochemistry in the organisms still challenges the analysis and understanding of metabolism. Even for one of the most known organism like Escherichia coli, there are around 10% of its open reading frames (ORFs) that remain to be annotated. The experimental identification of metabolic capabilities in an organism will benefit from the guidance from computational analysis. The study of genome-scale models represents an attractive approach to identify the metabolic capabilities required for growth and for the connectivity of all metabolites that are part of the metabolic network. For this purpose, we develop a gap-filling approach that identifies known and novel alternative reactions to the ones integrated in the latest genome-scale models of E. coli (iJO1366) and Saccharomyces cerevisiae (iTO977). The novel reactions were obtained from the recently developed repository of all possible biochemical reactions (ATLAS of biochemistry). Our method uses a mixed-integer linear programming (MILP) formulation to identify alternative metabolic reactions that satisfy mass balance constraints. We then evaluate the thermodynamic feasibility of the novel reactions at the intracellular conditions. We further used a cheminformatics tool to compare the sequence similarity of the alternative gap-filled enzymes with the ORF of closely related organisms. Here, we present a comparative study between the unknown biochemistry from E. coli and S. cerevisiae, and we highlight the currently unknown metabolic functions that represent the most attractive candidates for experimental characterization.
000231783 6531_ $$aATLAS
000231783 6531_ $$agap-filling
000231783 6531_ $$agenome-scale metabolic models
000231783 6531_ $$aconstraint-based approach
000231783 6531_ $$amixed-integer linear programming (MILP)
000231783 700__ $$0247949$$aChiappino Pepe, Anush$$g239558
000231783 700__ $$0245962$$aAtaman, Meriç$$g215631
000231783 700__ $$0244260$$aHadadi, Noushin$$g185577
000231783 700__ $$0240657$$aHatzimanikatis, Vassily$$g174688
000231783 7112_ $$a253rd ACS National Meeting & Exposition$$cSan Francisco, CA, USA$$dApril 2-6, 2017
000231783 720_2 $$0240657$$aHatzimanikatis, Vassily$$edir.$$g174688
000231783 909CO $$ooai:infoscience.tind.io:231783$$pposter$$pSB
000231783 909C0 $$0252131$$pLCSB$$xU11422
000231783 917Z8 $$x239558
000231783 917Z8 $$x239558
000231783 937__ $$aEPFL-POSTER-231783
000231783 973__ $$aEPFL
000231783 980__ $$aPOSTER