Resolution:
## Figure 1.
Schematic procedure of the framework for network modularization and Bayesian network
analysis (FMB). (A and B) Metabolic network model is repeatedly simulated for control and perturbation
conditions using constraints-based flux analysis with constraints from ^{13}C-based metabolic flux analysis and cell culture to calculate more reliable metabolic
flux distributions while amplifying the data size. (C) The result is a flux matrix
(N-by-M) that contains a total of 2,000 samples for each reaction from both control and perturbed
condition. (D) Core reactions (see main text for definition) are selected in this
step. (E) Flux matrix is converted to flux-pattern matrices that contain information
on flux variation pattern from sample to sample, having one of ‘1’, ‘-1’ and ‘0’ (see
Methods for details). All the generated flux-pattern matrices are adjoined into a
single large flux-pattern matrix for clustering. (F) Hierarchical clustering is applied
to this matrix, and reactions are clustered in terms of the uniform functionality,
creating metabolic clusters. (G) Bayesian network (BN) of each metabolic module is
first inferred, producing local scale BNs, and representative reactions, the most
influential ones in their corresponding metabolic module, are determined by measuring
the degree of influence of each reaction on others in a module using total mutual
information (TMI). Mutual information (MI) is calculated between a target node (blue
color) and the other remaining nodes pair by pair, indicated as MI_{1}, MI_{2}, MI_{3} and MI_{4}, in a local scale BN of metabolic module. TMI of the reaction is a summation of these
MI values. This procedure is repeated until TMIs of all the other reactions are calculated.
At the end, reaction with the highest value of TMI is selected as the representative
reaction of the metabolic module. (H) Representative reactions are finally subjected
to BN analysis to infer a global scale BN for detailed analysis of specific perturbation
given to the biological system.
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