원문정보
초록
영어
Metabolic flux distributions are an ultimate cellular phenotype as an outcome of interplays among various cell components. They are condition-specific, so that they have been obtained separately under specified conditions. Their integral analysis obtained from various conditions could provide further insight into the science of metabolic fluxes. For this, we developed a framework that sequentially employs constraint-based flux analysis and Bayesian network analysis, which ultimately yields causal network of metabolic fluxes based on specified
conditions. Constraint-based flux analysis is a simulation method of a stoichiometric model using various optimization techniques. Bayesian network is a probabilistic graphical model that reveals causal relationships among parameters of interest and is useful for data integration. This framework enables integration of metabolic fluxes, which could supplement metabolic flux analysis. [This work was supported by the Korea Science and Engineering Foundation (KOSEF) grant funded by the Korea government (MOST) (No. M10309020000-03B5002-00000).
Further supports by LG Chem Chair Professorship, Microsoft and IBM SUR program are appreciated.]