MOTIVATION: In kinetic models of metabolism, the parameter values determine the dynamic behaviour predicted by these models. Estimating parameters from in vivo experimental data require the parameters to be structurally identifiable, and the data to be informative enough to estimate these parameters. Existing methods to determine the structural identifiability of parameters in kinetic models of metabolism can only be applied to models of small metabolic networks due to their computational complexity. Additionally, a priori experimental design, a necessity to obtain informative data for parameter estimation, also does not account for using steady-state data to estimate parameters in kinetic models. RESULTS: Here, we present a scalable methodology to structurally identify parameters for each flux in a kinetic model of metabolism based on the availability of steady-state data. In doing so, we also address the issue of determining the number and nature of experiments for generating steady-state data to estimate these parameters. By using a small metabolic network as an example, we show that most parameters in fluxes expressed by mechanistic enzyme kinetic rate laws can be identified using steady-state data, and the steady-state data required for their estimation can be obtained from selective experiments involving both substrate and enzyme level perturbations. The methodology can be used in combination with other identifiability and experimental design algorithms that use dynamic data to determine the most informative experiments requiring the least resources to perform. AVAILABILITY AND IMPLEMENTATION: https://github.com/LMSE/ident. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Motivation: Metabolism can exhibit dynamic phenomena like bistability due to the presence of regulatory motifs like the positive feedback loop. As cell factories, microorganisms with bistable metabolism can have a high and a low product flux at the two stable steady states, respectively. The exclusion of metabolic regulation and network dynamics limits the ability of pseudo-steady state stoichiometric models to detect the presence of bistability, and reliably assess the outcomes of design perturbations to metabolic networks. Results: Using kinetic models of metabolism, we assess the change in the bistable characteristics of the network, and suggest designs based on perturbations to the positive feedback loop to enable the network to produce at its theoretical maximum rate. We show that the most optimal production design in parameter space, for a small bistable metabolic network, may exist at the boundary of the bistable region separating it from the monostable region of low product fluxes. The results of our analysis can be broadly applied to other bistable metabolic networks with similar positive feedback network topologies. This can complement existing model-based design strategies by providing a smaller number of feasible designs that need to be tested in vivo. Availability and implementation: http://lmse.biozone.utoronto.ca/downloads/. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.