Dynamic Modeling of CHO Cell Metabolism Using the Hybrid Cybernetic Approach With a Novel Elementary Mode Analysis Strategy

摘要

Mammalian cell culture has a major importance on the production of biopharmaceuticals including recombinant therapeutic proteins such as monoclonal antibodies (MAb). Chinese hamster ovary (CHO) cells have been a standard industrial host due to their well-known gene transfection, amplification and clone selection technologies. High market demands have led to continuous genetic and bioprocess engineering efforts with this host. Nonetheless, productivity optimization is challenging and time consuming due to the complex cellular machinery, compartmentalized metabolism and high interconnection between multiple biological and media components. Mathematical modeling of biological systems can successfully assess metabolism complexity, while providing logical and systematic methods for relevant genetic target and culture parameter identification towards cell growth and production improvements. However, most modeling approaches on CHO cells have been performed under stationary constraints and only few dynamic models have been presented on simplified reaction sets. This is mainly due to their complex regulation and metabolite transport between cell compartments, leading to huge overparameterization problems. Therefore, the development of new dynamic metabolic modeling approaches that account for compartmentalization can improve engineering efforts towards the enhancement production capabilities. In this report, a novel elementary mode selection strategy, based on a polar representation of the convex solution space is presented and coupled to a cybernetic approach to model the dynamic physiologic and metabolic behavior of CHO-S cell cultures. The proposed Polar Space Yield Analysis was compared to other reported elementary mode selection approaches derived from Flux Balance Analysis common metabolic objectives, Yield Space Analysis and Lumped Yield Space Analysis. For this purpose, exponential growth phase dynamic metabolic models were calculated using kinetic rate equations based on previously modeled growth parameters. Finally, complete culture dynamic metabolic flux models were constructed using the hybrid cybernetic modeling approach with selected elementary mode sets. The yield space elementary mode- and the polar space elementary mode- hybrid cybernetic models presented the best fits and performances. Also, a novel reaction flux perturbation prediction approach based on the polar yield solution space resulted useful for metabolic network flux distribution capability analysis and identification of potential genetic modifications targets.