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iSCHRUNK--In Silico Approach to Characterization and Reduction of Uncertainty in the Kinetic Models of Genome-scale Metabolic Networks.

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第一作者： Stefano,Andreozzi

第一单位： Laboratory of Computational Systems Biotechnology (LCSB), Swiss Federal Institute of Technology (EPFL), CH-1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics, CH-1015, Switzerland.

作者： Stefano,Andreozzi ^[1] ; Ljubisa,Miskovic ^[2] ; Vassily,Hatzimanikatis ^[3]

作者单位： Laboratory of Computational Systems Biotechnology (LCSB), Swiss Federal Institute of Technology (EPFL), CH-1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics, CH-1015, Switzerland. ^[1] Laboratory of Computational Systems Biotechnology (LCSB), Swiss Federal Institute of Technology (EPFL), CH-1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics, CH-1015, Switzerland. Electronic address: ljubisa.miskovic@epfl.ch. ^[2] Laboratory of Computational Systems Biotechnology (LCSB), Swiss Federal Institute of Technology (EPFL), CH-1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics, CH-1015, Switzerland. Electronic address: vassily.hatzimanikatis@epfl.ch. ^[3]

关键词 Enzyme saturations Kinetic parameters Large-scale kinetic models Machine learning Monte Carlo sampling Uncertainty reduction