000131084 001__ 131084
000131084 005__ 20190316234449.0
000131084 037__ $$aPOST_TALK
000131084 245__ $$aEnzyme States Allow Identification of Rate-Limiting Steps
000131084 269__ $$a2008
000131084 260__ $$c2008
000131084 336__ $$aPosters
000131084 520__ $$aA precise quantification of the effect of perturbations in a metabolic network depends on explicit knowledge of the kinetic properties of the enzymes of the individual reactions. However, a comprehensive knowledge of enzymes kinetics in a metabolic network is very difficult, if not impossible, to obtain. Furthermore, experimental data obtained under different conditions introduce uncertainty in enzyme kinetic parameters. In this work, we model the uncertainty in the kinetic data and we predict quantitatively the responses of metabolic networks in the presence of genetic, biochemical, and environmental variations. The proposed methodology accounts explicitly for mechanistic properties of enzymes and physicochemical and thermodynamic constraints and is based on formalism from process control and metabolic control. The method employs a novel, efficient Monte Carlo sampling procedure that allows us to simulate all possible meaningful states of a metabolic network and to compute the corresponding values of the kinetic constants of the individual reaction steps. We demonstrate the properties of the proposed framework through a number of case studies.
000131084 6531_ $$aenzyme kinetics
000131084 6531_ $$aMonte Carlo sampling
000131084 6531_ $$ametabolic networks
000131084 700__ $$0240448$$g133818$$aMiskovic, Ljubisa
000131084 700__ $$aHatzimanikatis, Vassily$$g174688$$0240657
000131084 7112_ $$dSeptember 14-19, 2008$$cPuerto Vallarta, Mexico$$aMetabolic Engineering VII: Health and Sustainability
000131084 8564_ $$uhttp://www.engconfintl.org$$zURL
000131084 909C0 $$xU11422$$0252131$$pLCSB
000131084 909CO $$ooai:infoscience.tind.io:131084$$qGLOBAL_SET$$pSB$$pposter
000131084 937__ $$aLCSB-POSTER-2009-001
000131084 973__ $$sPUBLISHED$$aEPFL
000131084 980__ $$aPOSTER