Maximizing batch fermentation efficiency by constrained model‐based optimization and predictive control of adenosine triphosphate turnover

Abstract

We present a constrained model‐based optimization and predictive control framework to maximize the production efficiency of batch fermentations based on the core idea of manipulating adenosine triphosphate (ATP) wasting. In many bioprocesses, enforced ATP wasting —rerouting ATP use towards an energetically possibly suboptimal path— allows increasing the metabolic flux towards the product, thereby enhancing product yields and specific productivities. However, this often comes at the expense of lower biomass yields and reduced volumetric productivities. To maximize the overall efficiency, we formulate ATP wasting as a model‐based optimal control problem. This allows for balancing trade‐offs between different objectives such as product yield and volumetric productivity for batch fermentations. Unlike static metabolic control, one obtains a higher degree of flexibility, adaptability, and competitiveness. This can be advantageous towards achieving a sustainable and economically efficient biotechnology industry. To compensate for model‐plant mismatch, disturbances, and uncertainties, we propose not only solving the optimal control problem once. Instead, we exploit the concept of moving horizon model predictive control combined with constraint‐based dynamic modeling to capture the fermentation dynamics. The approach is underlined considering the industrially relevant bioproduction of lactate by Escherichia coli. We discuss practical challenges for the described control strategy and provide an outlook towards future developments.

Toward a modeling, optimization, and predictive control framework for fed‐batch metabolic cybergenetics

“…We are also working on extending the scope of the presented metabolic cybergenetic framework to include synthetic microbial communities (cf. e.g., Espinel‐Ríos et al, 2023, n.d).…”

“…As foreseeable, the higher the induction capacity strength of the CcaS/CcaR system, the higher the net ATPase enzyme expression, and therefore the higher the net increase in product yield. In previous works dealing with dynamic ATP turnover in one‐stage batch fermentations, the increase in product yield was correlated with a loss in volumetric productivity (Espinel‐Ríos, Bettenbrock, et al, 2022; Espinel‐Ríos, Morabito, Bettenbrock, et al, 2022b). Here, we show that with a fed‐batch system, it is possible to increase both the product yield via the ATP turnover mechanism and the volumetric productivity via the introduction of a feeding rate.…”

“…e.g., Boecker et al, 2021; Hädicke et al, 2015; Zahoor et al, 2020). Dynamic manipulation of the ATPase expression, and thereby the ATPase flux, can thus be exploited to modulate the product yield and volumetric productivity in bioprocesses (Espinel‐Ríos, Bettenbrock, et al, 2022; Espinel‐Ríos, Morabito, Bettenbrock, et al 2022).…”

“…The dynamics of the lactate fermentation are based on an existing deFBA model (Espinel‐Ríos, Bettenbrock, et al, 2022). See Figure 3 for a summary of the resource allocation model.…”

“… Note : (1) Assumed biologically sound values inferred from feasible deFBA simulations (Espinel‐Ríos, Bettenbrock, et al, 2022) for different induction strength scenarios ($$\,{{\rm{S}}}_{i}$$). (2) Estimated as $${d}_{\text{ATPase}}=\frac{\mathrm{ln}(2)}{{t}_{0.5}}$$, where $${t}_{0.5}$$ is the ATPase protein half‐life time (Benito et al, 1991).…”