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Fig. 4 | BMC Systems Biology

Fig. 4

From: JuPOETs: a constrained multiobjective optimization approach to estimate biochemical model ensembles in the Julia programming language

Fig. 4

Proof of concept biochemical network study. Inset right: Prototypical biochemical network with six metabolites and seven reactions modeled using the hybrid cybernetic approach (HCM). Intracellular cellmass precursors A,B, and C are balanced (no accumulation) while the extracellular metabolites A e ,B e , and C e are dynamic. The oval denotes the cell boundary, q j is the jth flux across the boundary, and v k denotes the kth intracellular flux. Four data sets (each with A e ,B e ,C e and cellmass measurements) were generated by varying the kinetic constants for each biochemical mode. Each data set was a single objective in the JuPOETs procedure. a Ensemble simulation of extracellular substrate A e and cellmass versus time. b Ensemble simulation of extracellular substrate B e and C e versus time. The gray region denotes the 95% confidence estimate of the mean ensemble simulation. The data points denote mean synthetic measurements, while the error bars denote the 95% confidence estimate of the measurement computed over the four training data sets. c Trade-off plots between the four training objectives. The quantity O j denotes the jth training objective. Each point represents a member of the parameter ensemble, where gray denotes rank 0 sets, while black denotes rank 1 sets. Ensembles were generated using POETs without employing local refinement

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