A systematic approach for finding the objective function and active constraints for dynamic flux balance analysis.

Dynamic flux balance analysis (DFBA) has become an instrumental modeling tool for describing the dynamic behavior of bioprocesses. DFBA involves the maximization of a biologically meaningful objective subject to kinetic constraints on the rate of consumption/production of metabolites. In this paper, we propose a systematic data-based approach for finding both the biological objective function and a minimum set of active constraints necessary for matching the model predictions to the experimental data. The proposed algorithm accounts for the errors in the experiments and eliminates the need for ad hoc choices of objective function and constraints as done in previous studies. The method is illustrated for two cases: (1) for in silico (simulated) data generated by a mathematical model for Escherichia coli and (2) for actual experimental data collected from the batch fermentation of Bordetella Pertussis (whooping cough).

Identification of active constraints in dynamic flux balance analysis.

This study deals with the calibration of dynamic metabolic flux models that are formulated as the maximization of an objective subject to constraints. Two approaches were applied for identifying the constraints from data. In the first approach a minimal active number of limiting constraints is found based on data that are assumed to be bounded within sets whereas, in the second approach, the limiting constraints are found based on parametric sensitivity analysis. The ability of these approaches to finding the active limiting constraints was verified through their application to two case studies: an in-silico (simulated) data-based study describing the growth of E. coli and an experimental data-based study for Bordetella pertussis (B. pertussis). © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 33:26-36, 2017.

Modeling of cell culture damage and recovery leads to increased antibody and biomass productivity in CHO cell cultures.

The development of an efficient and productive cell-culture process requires a deep understanding of intracellular mechanisms and extracellular conditions for optimal product synthesis. Mathematical modeling provides an effective strategy to predict, control, and optimize cell performance under a range of culture conditions. In this study, a mathematical model is proposed for the investigation of cell damage of a Chinese hamster ovary cell culture secreting recombinant anti-RhD monoclonal antibody (mAb). Irreversible cell damage was found to be correlated with a reduction in pH. This irreversible damage to cellular function is described mathematically by a Tessier-based model, in which the actively growing fraction of cells is dependent on an intracellular metabolic product acting as a growth inhibitor. To further verify the model, an offline model-based optimization of mAb production in the cell culture was carried out, with the goal of minimizing cell damage and thereby enhancing productivity through intermittent refreshment of the culture medium. An experimental implementation of this model-based strategy resulted in a doubling of the yield as compared to the batch operation and the resulting biomass and productivity profiles agreed with the model predictions.