COSMIC-dFBA: A novel multi-scale hybrid framework for bioprocess modeling

Abstract

Metabolism governs cell performance in biomanufacturing, as it fuels growth and productivity. However, even in well-controlled culture systems, metabolism is dynamic, with shifting objectives and resources, thus limiting the predictive capability of mechanistic models for process design and optimization. Here, we present Cellular Objectives and State Modulation In bioreaCtors (COSMIC)-dFBA, a hybrid multi-scale modeling paradigm that accurately predicts cell density, antibody titer, and bioreactor metabolite concentration profiles. Using machine-learning, COSMIC-dFBA decomposes the instantaneous metabolite uptake and secretion rates in a bioreactor into weighted contributions from each cell state (growth or antibody-producing state) and integrates these with a genome-scale metabolic model. A major strength of COSMIC-dFBA is that it can be parameterized with only metabolite concentrations from spent media, although constraining the metabolic model with other omics data can further improve its capabilities. Using COSMIC-dFBA, we can predict the final cell density and antibody titer to within 10% of the measured data, and compared to a standard dFBA model, we found the framework showed a 90% and 72% improvement in cell density and antibody titer prediction, respectively. Thus, we demonstrate our hybrid modeling framework effectively captures cellular metabolism and expands the applicability of dFBA to model the dynamic conditions in a bioreactor.

Modelling dynamic host-pathway interactions at the genome scale

“…Our work contributes to the emerging area of hybrid metabolic modelling that attempts to expand the predictive power of traditional approaches and incorporate a broader range of cellular processes relevant for pathway engineering. Recent examples include the use of machine learning to augment the predictive power of GEMs 59,60 , the integration of FBA and bioprocess models 61 , and expanding the boundaries of GEMs to include signalling pathways 62 . We anticipate this area will continue to grow and draw inspiration from machine learning techniques as practitioners strive to balance mechanistic and data-driven approaches to model biological systems 63 .…”