, Equations 199 (3) to (6) set constraints corresponding to gene expression data. y i + , y i-and y i are boolean variables 200 representing the adequacy between the predicted flux ? i through reaction i and its expression level
, & (4) enforce that, if y i + =1 or y i-=1 (i.e., the reaction is active), the value 203 of the flux through reaction i is larger than a threshold ?, whereas if y i + =0 or y i-=0 (i.e., the reaction 204 is inactive), the flux through reaction i must be 0. For NEr, y i represents whether the reaction is 205 inactive: equations (5) & (6) enforce that, For HEr, y i + (y i-) represents whether the reaction is active in the forward (or backward, respectively) 202 direction: equations (3)
respectively) are Boolean variable representing whether the reaction i is active or not 211 in the forward (or backward, respectively) direction. When x i + = 1, the flux through reaction i must 212 be larger (or smaller, respectively) than ? (equation ,
, If x i + =0 and x i-=0, the flux through 215 reaction i is forced to be 0, forcing the reaction to be inactive. The constraints set in Eq. 9 ensure 216 that at least one reaction is able to produce the metabolite j is active. 217 The optimization problem consists in finding the flux distribution ?, which maximizes the 218 number of HEr which are active (? HEr ? ?) and the number of NEr which are inactive (? NEr = 0). the reactions were classified as "required" (R) if they were found to be active in all solutions 686 and conversely "Inactive" (I) if they display a zero-flux in all the optimal solutions. All other 687 reactions, found to be either active or inactive in the optimal solutions, were classified as 688 "Potentially active" (PA). This analysis was made independently for the d3 and d30 differentiation 689 stages. (B) Reactions identified as I at d3 but R or PA at d30 were considered as activated, For reversible reactions, when x i-= 1, the flux through reaction i must be negative and lower than 214 ? (equation (8)), forcing the reaction to be backward active
Principal component analysis of 3-day and 30-day subnetworks. PCA analysis was the MetExplore webserver, 2018. ,
, Results from liver-specific metabolic functions simulated for each of the cell-specific models 727 identified at each stage: for each type of function
, Assessment of the liver-specificity of generated 740 subnetworks: comparison with UNIPROT data (Figure S7); Assessment of the liver-specificity of 741 generated subnetworks: comparison with Human Proteome Atlas data (Figure S8); made from conjoint or individual analysis of cell-specific models for the keratin sulfate 747 degradation pathway (Figure S12); the Ubiquinone, N-glycan synthesis 753 and keratin sulfate degradation pathways (Supplementary Notes) (PDF) 754 Metabolic functions used for simulations (Table S1, XLSX) 755 NMR identified metabolites for 3-day and 30, Principle of the iMat algorithm illustrated on a Toy model
, Computed subnetworks for 3-day and 30-day HepaRG cells from our method and the 757 FASTCORE algorithm (Table S4, XLSX)
, This material is available free of charge via the
, ?INSB Institut des sciences biologiques, 764 UMR8204 Centre d'infection et d'immunité de
FV participated in the computational analysis; NP wrote the manuscript, NP contributed in data interpretation, discussion on results and writing and revising the manuscript ,
, This work was supported by the French Ministry of Research and National Research Agency as 772 part of the French MetaboHUB, the national metabolomics and fluxomics infrastructure
, This work was supported in part by the French Ministry of Research and the National Research
, Agency, as part of the French MetaboHUB infrastructure (the national metabolomics and 778 fluxomics infrastructure, Grant ANR-INBS-0010) and the NISTEC project
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