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Cellular metabohc modeling

The derivation of regular patterns of cellular behavior that can be assigned to certain defined subpopulations (or single cells) from measured data necessarily involves modeling approaches that allow assignment of mechanistic (e.g., metabohc) models to subpopulations with individual parameters or other variations. Further, such models must allow the tracking of individual cell s dynamic behavior even when they fluctuate between different populations. Two principal approaches can be distinguished systems of partial different equations (population balance systems, PBE) and stochastic cell ensemble models (CEMs)... [Pg.136]

Due to lack of kinetic parameters, structural metabolic network modeling has been widely applied for analyzing cellular metabolism under steady-state. Depending on what assumptions are made and whether experimental data are required, different techniques have been developed to analyze the invariant of metabohc networks such as metabolic flux analysis (MFA), flux balance analysis (FBA), and metabolic pathway analysis (MPA) including elementary mode and extreme pathway analyses (Lewis et al. 2012 Stephanopoulos et al. 1998 Trinh et al. 2(X)9). [Pg.22]


See other pages where Cellular metabohc modeling is mentioned: [Pg.165]    [Pg.32]    [Pg.273]    [Pg.150]    [Pg.109]    [Pg.155]    [Pg.166]    [Pg.205]    [Pg.304]   
See also in sourсe #XX -- [ Pg.109 , Pg.119 ]




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