Towards a modeling, optimization and predictive control framework for fed-batch metabolic cybergenetics

TL;DR

This paper introduces a framework combining cybergenetics, dynamic modeling, and predictive control to optimize fed-batch bioprocesses, enabling real-time gene expression modulation for improved product yield and process robustness.

Contribution

It develops a novel integrated approach using dynamic constraint-based models and model predictive control for optimizing bioprocesses with online feedback and gene regulation.

Findings

Successful simulation of optogenetic control of ATPase complex

Demonstrated improved yield and productivity in dynamic conditions

Showed robustness of control strategy against process disturbances

Abstract

Biotechnology offers many opportunities for the sustainable manufacturing of valuable products. The toolbox to optimize bioprocesses includes \textit{extracellular} process elements such as the bioreactor design and mode of operation, medium formulation, culture conditions, feeding rates, etc. However, these elements are frequently insufficient for achieving optimal process performance or precise product composition. One can use metabolic and genetic engineering methods for optimization at the intracellular level. Nevertheless, those are often of static nature, failing when applied to dynamic processes or if disturbances occur. Furthermore, many bioprocesses are optimized empirically and implemented with little-to-no feedback control to counteract disturbances. The concept of cybergenetics has opened new possibilities to optimize bioprocesses by enabling online modulation of the gene expression of metabolism-relevant proteins via external inputs (e.g., light intensity in optogenetics). Here, we fuse cybergenetics with model-based optimization and predictive control for optimizing dynamic bioprocesses. To do so, we propose to use dynamic constraint-based models that integrate the dynamics of metabolic reactions, resource allocation, and inducible gene expression. We formulate a model-based optimal control problem to find the optimal process inputs. Furthermore, we propose using model predictive control to address uncertainties via online feedback. We focus on fed-batch processes, where the substrate feeding rate is an additional optimization variable. As a simulation example, we show the optogenetic control of the ATPase enzyme complex for dynamic modulation of enforced ATP wasting to adjust product yield and productivity.