Linking intra- and extra-cellular metabolic domains via neural-network surrogates for dynamic metabolic control

TL;DR

This paper presents a novel approach that uses neural-network surrogates derived from steady-state metabolic models to enable dynamic optimization of intracellular fluxes, aiding metabolic engineering design with limited data.

Contribution

It introduces a method to link intracellular fluxes to exchange rates via neural surrogates, simplifying complex models for dynamic metabolic control.

Findings

Successfully applied to E. coli metabolic network

Demonstrated effective prediction of optimal flux trajectories

Facilitated early-stage metabolic engineering decisions

Abstract

We outline a modeling and optimization strategy for investigating dynamic metabolic engineering interventions. Our framework is particularly useful at the early stages of research and development, often constrained by limited knowledge and experimental data. Elucidating a priori optimal trajectories of manipulatable intracellular fluxes can guide the design of suitable control schemes, e.g., cyber(ge)netic or in-cell approaches, and the selection of appropriate actuators, e.g., at the transcriptional or post-translational levels. Model-based dynamic optimization is proposed to predict optimal trajectories of target manipulatable intracellular fluxes. A challenge emerges as existing models are often oversimplified, lacking insights into metabolism, or excessively complex, making them difficult to build and implement. Here, we use surrogates derived from steady-state solutions of constraint-based metabolic models to link manipulatable intracellular fluxes to the process exchange rates of structurally simple hybrid dynamic models. The latter can be conveniently used in optimal control problems of metabolism. As a proof of concept, we apply our method to a reduced metabolic network of $\textit{Escherichia coli}$ considering two different scenarios of dynamic metabolic engineering.