Data-driven and Physics Informed Modelling of Chinese Hamster Ovary Cell Bioreactors
This work addresses modeling challenges in bioprocess engineering for biologics production, offering an incremental improvement over traditional methods.
The authors tackled the problem of modeling Chinese Hamster Ovary cell bioreactors by proposing a hybrid data-driven and physics-informed model to address inconsistencies in existing quantitative methods, resulting in a framework that integrates physical laws with machine learning for improved accuracy.
Fed-batch culture is an established operation mode for the production of biologics using mammalian cell cultures. Quantitative modeling integrates both kinetics for some key reaction steps and optimization-driven metabolic flux allocation, using flux balance analysis; this is known to lead to certain mathematical inconsistencies. Here, we propose a physically-informed data-driven hybrid model (a "gray box") to learn models of the dynamical evolution of Chinese Hamster Ovary (CHO) cell bioreactors from process data. The approach incorporates physical laws (e.g. mass balances) as well as kinetic expressions for metabolic fluxes. Machine learning (ML) is then used to (a) directly learn evolution equations (black-box modelling); (b) recover unknown physical parameters ("white-box" parameter fitting) or -- importantly -- (c) learn partially unknown kinetic expressions (gray-box modelling). We encode the convex optimization step of the overdetermined metabolic biophysical system as a differentiable, feed-forward layer into our architectures, connecting partial physical knowledge with data-driven machine learning.