Abstract
Developing predictive, high-resolution microbial models that retain mechanistic insight remains a central challenge in biochemical engineering. This paper addresses the challenge of preserving metabolic information while ensuring model accuracy and proper statistical definition. It employs the Hybrid Cybernetic Modelling (HCM) approach to integrate metabolic regulation with genome-scale information, and dynamically predict E.Coli phenotypes. The main aim of this work is to explore HCM's parameter identifiability to advance its accuracy and robustness for a limited set of data. To bypass the computational burden of computing the elementary flux modes, the opt-yield Flux Balance Analysis (opt-yield FBA) is employed to identify a physiologically relevant set of yield-maximising metabolic pathways. Metabolic Yield Analysis (MYA) then reduces this to four key pathways which capture 99% of the original metabolic yield space. The cybernetic model is formulated where "artificial enzymes" are allocated among these key competing pathways, based on a return-on-investment criterion. Initial parameter estimates are derived through non-linear least squares regression for an in silico data set of biomass, glucose and acetate concentration. Practical identifiability analysis is employed to explore parameter estimates. Specifically, profile likelihood & sensitivity analysis results lead to model reduction by eliminating the combined acetate-biomass producing pathway. To further improve parameter uncertainty, Optimal Experiment Design (OED) is used to obtain an additional informative experiment. Finally, the calibrated model is validated against a new artificial dataset.