000177955 001__ 177955
000177955 005__ 20190316235411.0
000177955 037__ $$aPOST_TALK
000177955 245__ $$aMetabolic control analysis of the central carbon pathway in optimally grown E. coli
000177955 269__ $$a2012
000177955 260__ $$c2012
000177955 336__ $$aPosters
000177955 520__ $$aThe engineering of the metabolism requires the building of reliable kinetic models of the metabolic pathways through the integration of the information from heterogeneous data sources. Unfortunately, the quantitative knowledge about fluxes, mechanisms or kinetic parameters of the constituent reactions of the metabolic network is most of the times uncertain or missing, due to limitations in the measurement techniques of the relevant biological properties of the cell and variability in experimental conditions. In this contribution, we employ ORACLE (Optimization and Risk Analysis of Complex Living Entities), the computational framework for the analysis and optimization of metabolic networks that addresses these issues. ORACLE allows us to generate populations of kinetic models that account explicitly for the physico-chemical and thermodynamic features and constraints of metabolic networks. We show how ORACLE incorporates fluxomics and metabolomics data and constraints into the central carbon core model of E. coli to uncover its possible steady state behaviors, when the cells are cultivated under the physiological condition of optimal growth on different carbon sources, both anaerobically and aerobically. Alternative cases of possible intracellular flux and thermodynamic states, corresponding to the given experimental data, are identified and subsequently the kinetic analysis is performed to evaluate how the control over fluxes and concentrations is distributed over the metabolic network. We demonstrate that ORACLE methodology provides guidance for metabolic engineering in spite of uncertain/missing information in the local/global properties of the metabolic network, integrating a statistical account on the semi-quantitative available data with consistent thermodynamic information and plausible hypotheses.
000177955 6531_ $$aMetabolic engineering
000177955 6531_ $$aMetabolic control analysis
000177955 6531_ $$aoptimal growth
000177955 6531_ $$asampling
000177955 6531_ $$auncertainty
000177955 700__ $$0244258$$g193203$$aAndreozzi, Stefano
000177955 700__ $$0240448$$g133818$$aMiskovic, Ljubisa
000177955 700__ $$0243874$$g177860$$aSoh, Keng Cher
000177955 700__ $$aHatzimanikatis, Vassily$$g174688$$0240657
000177955 7112_ $$dJune 03-07, 2012$$cBiarritz, France$$aMetabolic Engineering IX
000177955 8564_ $$uhttps://infoscience.epfl.ch/record/177955/files/MetEngIX_ANDREOZZI.pdf$$zn/a$$s977736$$yn/a
000177955 909C0 $$xU11422$$0252131$$pLCSB
000177955 909CO $$ooai:infoscience.tind.io:177955$$qGLOBAL_SET$$pSB$$pposter
000177955 917Z8 $$x133818
000177955 917Z8 $$x193203
000177955 937__ $$aEPFL-POSTER-177955
000177955 973__ $$sPUBLISHED$$aEPFL
000177955 980__ $$aPOSTER