COMPModelpy: Understanding Microbial Metabolism in Bioreactors by Integrating Metabolic Models in a Compartment Modeling Framework Samira L. van den Bogaard and Titania C. Sugiarto, Tobias B. Alter, and Lars M. Blank Institute of Applied Biotechnology (iAMB), Aachen Biology and Biotechnology (ABBt), RWTH Aachen University Sustainable biomanufacturing has the potential to mitigate global challenges such as waste management and the reduction of greenhouse gas emissions. While numerous production processes have been demonstrated in research settings, scaling these novel bioprocesses to industrial application remains difficult. The complex interplay between cellular metabolism and the heterogeneous physical properties in large-scale reactors leads to diverse cellular states, highlighting the need for predictive, systems-level modeling approaches. Several computational demanding methods exists to understand the physical effects of large-scale fermentation vessels, such as Computational Fluid Dynamics (CFD). However, integration of intricate metabolic models into these models is challenging due to high computational loads. To address these challenges, we present COMPModelpy, a flexible systems-level modeling framework designed for integrating metabolic models into bioreactor conditions. By building a compartment model based on high-level reactor physics and empirical relations of reactor mixing, the computational load is reduced while still accurately capturing the reactor dynamics. Integration of genome-scale and Protein Allocation Models in the compartmentalized reactor yield valuable insights in potential metabolic bottlenecks. We showcase COMPmodelpy with two different applications: (1) using empirical equations to describe physical properties within compartment models, and (2) CFD simulation-guided parametrization of compartment models. Both cases yield insight into the spatial diversity of metabolic states in a bioreactor, which can be used to guide experimental designs and engineering strategies.
Bogaard et al. (Mon,) studied this question.