Mathematical modeling is key to understanding cellular metabolism. Two common approaches are kinetic modeling and constraint-based reconstruction and analysis (COBRA). COBRA models analyze steady-state fluxes using linear constraints but lack kinetic detail. Kinetic models offer mechanistic descriptions via differential equations but require (often unknown) kinetic parameters and enzyme concentrations. To bridge this gap, we introduce COBRA-k, a framework integrating nonlinear kinetic rate laws into COBRA models to consistently constrain metabolic fluxes, enzyme abundances, and metabolite concentrations. COBRA-k enables flexible exploration of metabolic steady states with optimization techniques, even with incomplete parametrization. COBRA-k models require solving computationally demanding mixed-integer nonlinear programs. We therefore developed a dedicated iterative algorithm, implemented in an open-source Python package. We applied COBRA-k to a large-scale Escherichia coli model, demonstrating its effectiveness and revealing holistic metabolic insights. For example, it accurately predicts and explains the phenomenon of high intracellular glutamate concentration. COBRA-k combines the flexibility of COBRA with kinetic precision, offering a powerful tool for predictive metabolic modeling and engineering.

Acetogenic bacteria such as Acetobacterium woodii use the Wood-Ljungdahl pathway to convert H2/CO2 and other C1 substrates into acetate and couple it via chemiosmotic energy conservation with ATP synthesis. To coordinate the associated electron flows under tight thermodynamic constraints, acetogens use not only NAD(H) and NAD (P)H but also ferredoxin as a third major redox cofactor. In this work, we systematically explore how cofactor specificity in redox reactions, including potential cofactor swaps, may influence growth and bioproduction. We initially reconstructed and validated a large-scale constraint-based metabolic model of A. woodii equipped with standard Gibbs-free-energy values and metabolite concentration bounds. We then analyzed the effects of swapping redox cofactors in the model. This analysis revealed that, in theory, suitable cofactor swaps could increase the growth rate by a factor of 5 compared to the wild type, but only under very high H2 and CO2 concentrations and with tight ranges for the redox states of the cofactors. More realistic solutions with higher driving forces and broader concentration ranges would still enable an up to 2.5-fold increase in growth rate and are based on two key swaps: (a) replacement of the hydrogen-dependent CO2 reductase (HDCR) by a NADPH-dependent formate dehydrogenase and (b) substitution of NAD+ by NADP+ in the bifurcating hydrogenase. These variants, which were frequently favored by the algorithm in different scenarios and are used by other acetogens, increase the amount of reduced ferredoxin available for subsequent ATP generation. Unexpectedly, our analysis further revealed that the use of ferredoxin and NADH alone (in combination with suitable cofactor swaps) could lead to similar growth rates and driving forces as for the wild type. We discuss possible reasons why these solutions may not have been selected by evolution. In particular, we show that the native redox cofactor specificities of A. woodii facilitate near-maximal driving forces under a wide range of H2 and CO2 concentrations. Finally, we evaluated production of 15 native and heterologous chemicals from four C1-substrate regimes. This analysis reveals that targeted cofactor engineering can in many (but not all) cases (i) enable growth-coupled synthesis of a target chemical if it is infeasible in the native A. woodii strain or (ii) enhance the thermodynamic driving force of product synthesis. Overall, this work provides a valuable resource and a generalizable thermodynamics-based framework for evaluating redox engineering strategies in acetogens and other energy-limited microorganisms.

Minimal cut sets (MCSs) have emerged as an important branch of constraint-based metabolic modeling, offering a versatile framework for analyzing and engineering metabolic networks. Over the past two decades, MCSs have evolved from a theoretical concept into a powerful tool for identifying tailored metabolic intervention strategies and studying robustness and failure modes of metabolic networks. Successful (experimental) applications range from designing highly efficient microbial cell factories to targeting cancer cell metabolism. This review highlights key conceptual and algorithmic advancements that have transformed MCSs into a flexible methodology applicable to metabolic models of any size. It also provides a comprehensive overview of their applications and concludes with a perspective on future research directions. The review aims to equip both newcomers and experts with the knowledge needed to effectively leverage MCSs for metabolic network analysis and design, therapeutic targeting, and beyond.

Abstract Background Due to increasing ecological concerns, microbial production of biochemicals from sustainable carbon sources like acetate is rapidly gaining importance. However, to successfully establish large-scale production scenarios, a solid understanding of metabolic driving forces is required to inform bioprocess design. To generate such knowledge, we constructed isopropanol-producing Escherichia coli W strains. Results Based on strain screening and metabolic considerations, a 2-stage process was designed, incorporating a growth phase followed by a nitrogen-starvation phase. This process design yielded the highest isopropanol titers on acetate to date (13.3 g L−1). Additionally, we performed shotgun and acetylated proteomics, and identified several stress conditions in the bioreactor scenarios, such as acid stress and impaired sulfur uptake. Metabolic modeling allowed for an in-depth characterization of intracellular flux distributions, uncovering cellular demand for ATP and acetyl-CoA as limiting factors for routing carbon toward the isopropanol pathway. Moreover, we asserted the importance of a balance between fluxes of the NADPH-providing isocitrate dehydrogenase (ICDH) and the product pathway. Conclusions Using the newly gained system-level understanding for isopropanol production from acetate, we assessed possible engineering approaches and propose process designs to maximize production. Collectively, our work contributes to the establishment and optimization of acetate-based bioproduction systems. Graphical Abstract

Microbial catalysts must partition incoming substrate between the synthesis of biomass and the synthesis of a desired product. Although biomass synthesis generates more catalyst and therefore potentially higher volumetric productivities, the synthesis of product increases specific production rates and product yields. Two-stage bioprocesses can accommodate this tradeoff through temporal separation of the growth and production phases. The biocatalyst first grows to optimal density; it is then switched to a growth-arrested state during which the product is synthesized. However, a substantial reduction in metabolic activity is often observed during cellular growth arrest, even in the presence of sufficient substrate. An ultimate bioengineering goal, therefore, is to create growth-arrested states that retain high metabolic activity. Achieving this goal brings the metabolic engineer to the intersection of microbial physiology, synthetic biology and biochemistry. In this Review, we describe various aspects of the design of microbial catalysts for two-stage bioprocesses for metabolite production, including synthetic biology tools to arrest cell growth using external or internal cues, and metabolic engineering tools to minimize interference from the native metabolic network and enhance substrate uptake and conversion. We highlight recent systems biology studies of nutrient-limited heterotrophs and phototrophs and conclude that the reduction in substrate uptake by cells in growth arrest is the consequence of reduced energy demand as well as imbalances in regulatory metabolites that typically arise during nutrient limitation. On the basis of these studies, we propose strategies for increasing metabolic activity in growth-arrested cells. Microbial catalysts must partition incoming substrate between synthesis of biomass and synthesis of a desired product. Two-stage bioprocesses can accommodate this tradeoff to maximize process productivity by temporal separation of growth and production phases. This Review discusses the challenges of maintaining a high metabolic activity during the production phase.

One central goal of bioprocess engineering is to maximize the production of specific chemicals using microbial cell factories. Many bioprocesses are one-stage (batch) processes (OSPs), in which growth and product synthesis are coupled. However, OSPs often exhibit low volumetric productivities due to the competition for substrate for biomass and product synthesis implying trade-offs between biomass and product yields. Two-stage or, more generally, multi-stage processes (MSPs) offer the potential to tackle this trade-off for improved efficiency of bioprocesses, for example, by separating growth and production. MSPs have recently gained much attention, also because of a rapidly growing toolbox for the dynamic control of metabolic fluxes. Despite these promising advancements, computational tools specifically tailored for the optimal design of MSPs in the field of biotechnology are still lacking. Here, we present OptMSP, a new Python-based toolbox for identifying optimal MSPs maximizing a user-defined process metrics (such as volumetric productivity, yield, and titer or combinations thereof) under given constraints. In contrast to other methods, our framework starts with a set of well-defined modules representing relevant stages or sub-processes. Experimentally determined parameters (such as growth rates, substrate uptake and product formation rates) are used to build suitable ODE models describing the dynamic behavior of each module. OptMSP finds then the optimal combination of those modules, which, together with the optimal switching time points, maximize a given objective function. We demonstrate the applicability and relevance of the approach with three different case studies, including the example of lactate production by E. coli in a batch setup, where an aerobic growth phase can be combined with anaerobic production phases with or without growth and with or without enhanced ATP turnover.

An organism’s survival hinges on maintaining the right thermodynamic conditions. Osmotic constraints limit the concentration range of metabolites, affecting essential cellular pathways. Despite extensive research on osmotic stress and growth, understanding remains limited, especially in hypo-osmotic environments. To delve into this, we developed a novel modeling approach that considers metabolic fluxes and metabolite concentrations along with thermodynamics. Our analysis of E. coli adaptation reveals insights into growth rates, metabolic pathways, and thermodynamic bottlenecks during transitions between hypo- and hyper-osmotic conditions. Both experimental and computational findings show that cells prioritize pathways that have higher thermodynamic driving force, like the pentose phosphate or the Entner–Doudoroff pathway, under low osmolarity. This work offers a systematic and mechanistic explanation for reduced growth rates in hypo- and hyper-osmotic conditions. The developed framework is the first of its kind to incorporate genome wide constraints that consider both natural logarithm and actual metabolite concentrations. ![Figure][1]</img> ### Competing Interest Statement The authors have declared no competing interest. [1]: pending:yes

Cell-free production systems are increasingly used for the synthesis of industrially relevant chemicals and biopharmaceuticals. Cell-free systems often utilize cell lysates, but biocatalytic cascades based on recombinant enzymes have emerged as a promising alternative strategy. However, implementing efficient enzyme cascades is a non-trivial task and mathematical modeling and optimization has become a key tool to improve their performance. In this work, we introduce a generic framework for the model-based optimization of cell-free enzyme cascades based on a given kinetic model of the system. We first formulate and systematize seven optimization problems relevant in the context of cell-free production processes including, for example, the maximization of productivity or product yield and the minimization of overall costs. We then present an approach that accounts for parameter uncertainties, not only during model calibration and model analysis but also when performing the actual optimization. After constructing a kinetic model of the enzyme cascade, experimental data are used to generate an ensemble of kinetic parameter sets reflecting their variabilities. For every parameter set, systems optimization is then performed and the resulting solution subsequently cross-validated for all other parameterizations to identify the solution with the highest overall performance under parameter uncertainty. We exemplify our approach for the cell-free synthesis of GDP-fucose, an important sugar nucleotide with various applications. We selected and solved three optimization problems based on a constructed dynamic model and validated two of them experimentally leading to significant improvements of the process (e.g., 50% increase of titer under identical total enzyme load). Overall, our results demonstrate the potential of model-driven optimization for the rational design and improvement of cell-free production systems. The developed approach for systems optimization under parameter uncertainty could also be relevant for the metabolic design of cell factories.

Anaerobic microbial fermentations provide high product yields and are a cornerstone of industrial bio-based processes. However, the need for redox balancing limits the array of fermentable substrate-product combinations. To overcome this limitation, here we design an aerobic fermentative metabolism that allows the introduction of selected respiratory modules. These can use oxygen to re-balance otherwise unbalanced fermentations, hence achieving controlled respiro-fermentative growth. Following this design, we engineer and characterize an obligate fermentative Escherichia coli strain that aerobically ferments glucose to stoichiometric amounts of lactate. We then re-integrate the quinone-dependent glycerol 3-phosphate dehydrogenase and demonstrate glycerol fermentation to lactate while selectively transferring the surplus of electrons to the respiratory chain. To showcase the potential of this fermentation mode, we direct fermentative flux from glycerol towards isobutanol production. In summary, our design permits using oxygen to selectively re-balance fermentations. This concept is an advance freeing highly efficient microbial fermentation from the limitations imposed by traditional redox balancing.

Optogenetic modulation of adenosine triphosphatase (ATPase) expression represents a novel approach to maximize bioprocess efficiency by leveraging enforced adenosine triphosphate (ATP) turnover. In this study, we experimentally implement a model-based open-loop optimization scheme for optogenetic modulation of the expression of ATPase. Increasing the intracellular concentration of ATPase, and thus the level of ATP turnover, in bioprocesses with product synthesis coupled with ATP generation, can lead to increased product formation and substrate uptake. Previous simulation studies formulated optimal control problems using dynamic constraint-based models to find optimal light inputs in fermentations with optogenetically mediated ATPase expression. However, using these models poses challenges due to resulting bilevel optimizations and complex parameterization. Here, we outline a simplified unsegregated and quasi-unstructured kinetic modeling approach that reduces the number of dynamic states and leads to single-level optimizations. The models can be augmented with Gaussian processes to compensate for model uncertainties. We implement optimal control constrained by knowledge-based and hybrid models for optogenetic ATPase expression in Escherichia coli with lactate as the main product. To do so, we genetically engineer E. coli to obtain optogenetic expression of ATPase using the CcaS/CcaR system. This represents the first experimental implementation of model-based optimization of ATPase expression in bioprocesses.

AbstractThe conversion of CO2 into methanol depicts one of the most promising emerging renewable routes for the chemical and biotech industry. Under this regard, native methylotrophs have a large potential for converting methanol into value-added products but require targeted engineering approaches to enhance their performances and to widen their product spectrum. Here we use a systems-based approach to analyze and engineer M. extorquens TK 0001 for production of glycolic acid. Application of constraint-based metabolic modeling reveals the great potential of M. extorquens for that purpose, which is not yet described in literature. In particular, a superior theoretical product yield of 1.0 C-molGlycolic acid C-molMethanol−1 is predicted by our model, surpassing theoretical yields of sugar fermentation. Following this approach, we show here that strain engineering is viable and present 1st generation strains producing glycolic acid via a heterologous NADPH-dependent glyoxylate reductase. It was found that lactic acid is a surprising by-product of glycolic acid formation in M. extorquens, most likely due to a surplus of available NADH upon glycolic acid synthesis. Finally, the best performing strain was tested in a fed-batch fermentation producing a mixture of up to total 1.2 g L−1 glycolic acid and lactic acid. Several key performance indicators of our glycolic acid producer strain are superior to state-of-the-art synthetic methylotrophs. The presented results open the door for further strain engineering of the native methylotroph M. extorquens and pave the way to produce two promising biopolymer building blocks from green methanol, i.e., glycolic acid and lactic acid.

BACKGROUND:Zymomonas mobilis is well known for its outstanding ability to produce ethanol with both high specific productivity and with high yield close to the theoretical maximum. The key enzyme in the ethanol production pathway is the pyruvate decarboxylase (PDC) which is converting pyruvate to acetaldehyde. Since it is widely considered that its gene pdc is essential, metabolic engineering strategies aiming to produce other compounds derived from pyruvate need to find ways to reduce PDC activity.RESULTS:Here, we present a new platform strain (sGB027) of Z. mobilis in which the native promoter of pdc was replaced with the IPTG-inducible PT7A1, allowing for a controllable expression of pdc. Expression of lactate dehydrogenase from E. coli in sGB027 allowed the production of D-lactate with, to the best of our knowledge, the highest reported specific productivity of any microbial lactate producer as well as with the highest reported lactate yield for Z. mobilis so far. Additionally, by expressing the L-alanine dehydrogenase of Geobacillus stearothermophilus in sGB027 we produced L-alanine, further demonstrating the potential of sGB027 as a base for the production of compounds other than ethanol.CONCLUSION:We demonstrated that our new platform strain can be an excellent starting point for the efficient production of various compounds derived from pyruvate with Z. mobilis and can thus enhance the establishment of this organism as a workhorse for biotechnological production processes.

Motivation Flux balance analysis (FBA) is widely recognized as an important method for studying metabolic networks. When incorporating flux measurements of certain reactions into an FBA problem, it is possible that the underlying linear program may become infeasible, e.g. due to measurement or modeling inaccuracies. Furthermore, while the biomass reaction is of central importance in FBA models, its stoichiometry is often a rough estimate and a source of high uncertainty.Results In this work, we present a method that allows modifications to the biomass reaction stoichiometry as a means to (i) render the FBA problem feasible and (ii) improve the accuracy of the model by corrections in the biomass composition. Optionally, the adjustment of the biomass composition can be used in conjunction with a previously introduced approach for balancing inconsistent fluxes to obtain a feasible FBA system. We demonstrate the value of our approach by analyzing realistic flux measurements of E.coli. In particular, we find that the growth-associated maintenance (GAM) demand of ATP, which is typically integrated with the biomass reaction, is likely overestimated in recent genome-scale models, at least for certain growth conditions. In light of these findings, we discuss issues related to the determination and inclusion of GAM values in constraint-based models. Overall, our method can uncover potential errors and suggest adjustments in the assumed biomass composition in FBA models based on inconsistencies between the model and measured fluxes.Availability and implementation The developed method has been implemented in our software tool CNApy available from https://github.com/cnapy-org/CNApy.

The ubiquitous coexistence of the redox cofactors NADH and NADPH is widely considered to facilitate an efficient operation of cellular redox metabolism. However, it remains unclear what shapes the NAD(P)H specificity of specific redox reactions. Here, we present a computational framework to analyze the effect of redox cofactor swaps on the maximal thermodynamic potential of a metabolic network and use it to investigate key aspects of redox cofactor redundancy in Escherichia coli . As one major result, our analysis suggests that evolved NAD(P)H specificities are largely shaped by metabolic network structure and associated thermodynamic constraints enabling thermodynamic driving forces that are close or even identical to the theoretical optimum and significantly higher compared to random specificities. Furthermore, while redundancy of NAD(P)H is clearly beneficial for thermodynamic driving forces, a third redox cofactor would require a low standard redox potential to be advantageous. Our approach also predicts trends of redox-cofactor concentration ratios and could facilitate the design of optimal redox cofactor specificities.

Hal S. Alper, University of Texas at Austin, Austin, TX, USA Maciek R. Antoniewicz, University of Delaware, Newark, DE, USA Nicole Borth, University of Natural Resources and Life Sciences, Vienna, Austria Ron Bates, Bristol-Myers Squibb, Syracuse, NY, USA Marc Blondel, Université de Bretagne Occidentale, Brest, France Nediljko Budisa, Technical University of Berlin, Berlin, Germany Joaquim M. S. Cabral, Universidade de Lisboa, Lisbon, Portugal Manuel Canovas, University of Murcia, Murcia, Spain Giorgio Carta, University of Virginia, Charlottesville, VA, USA Hyung Joon Cha, Pohang University of Science and Technology, Pohang, South Korea Jo-Shu Chang, National Cheng Kung University, Tainan, Taiwan Matthew Wook Chang, National University of Singapore, Singapore, Singapore George Guo-Qiang Chen, Tsinghua University, Beijing, China Wilfred Chen, University of Delaware, Newark, DE, USA Wen-Yih Chen, National Central University, Taoyuan, Taiwan Andre Choo, National University of Singapore, Singapore, Singapore Don A. Cowan, University of Pretoria, Pretoria, South Africa Matthew P. DeLisa, Cornell University, Ithaca, NY, USA Ruth Freitag, Bayreuth University, Bayreuth, Germany Hikmet Geckil, Inonu University, Malatya, Turkey Reingard Grabherr, University of Natural Resources and Life Sciences, Vienna, Austria Klaus Graumann, Phoenestra GmbH, Kundl, Switzerland Mohd Ali Hassan, Universiti Putra Malaysia, Serdang, Malaysia Vassily Hatzimanikatis, Swiss Federal Institute of Technology, Lausanne, Switzerland Michael Jewett, Northwestern University, Evanston, USA Jay D. Keasling, University of California – Berkeley, Berkeley, CA, USA Ali Khademhosseini, University of California-Los Angeles, Los Angeles, CA, USA Byung-Gee Kim, Seoul National University, Seoul, South Korea Dong-Myung Kim, Chungnam National University, Daejeon, South Korea Kazuhide Kimbara, Shizuoka University, Hamamatsu, Japan Steffen Klamt,Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany Mattheos Koffas, Rensselaer Polytechnic Institute, Troy, NY, USA Ashok Kumar, Indian Institute of Technology, Kanpur, India Götz Laible, Ruakura Research Centre, Hamilton, New Zealand Kong Peng Lam, Bioprocessing Technology Institute, Singapore, Singapore Gyun Min Lee, Korea Advanced Institute of Science and Technology, Daejeon, South Korea Luke P. Lee, University of California – Berkeley, Berkeley, CA, USA Xiaokun Li,Wenzhou Medical University, Wenzhou, China James Liao, University of California, Los Angeles, CA, USA Tiangang Liu,Wuhan University, Wuhan, China Timothy Lu,Massachusetts Institute of Technology, Cambridge, MA, USA Bansi Malhotra, Delhi Technological University, Delhi, India Carl-Fredrik Mandenius, Linköping University, Linköping, Sweden Diethard Mattanovich, University of Natural Resources and Life Sciences, Vienna, Austria Teruyuki Nagamune, The University of Tokyo, Tokyo, Japan Peter Neubauer, Technische Universität Berlin, Berlin, Germany Jens B. Nielsen, Chalmers University of Technology, Gothenburg, Sweden Lars K. Nielsen, The University of Queensland, Brisbane, Australia Bernd Nidetzky, Graz University of Technology, Graz, Austria Tetsuro Ogawa, Olympus Terumo Biomaterials Corp., Tokyo, Japan Sean P. Palecek, University of Wisconsin −Madison, Madison, WI, USA Hyun Gyu Park, Korea Advanced Institute of Science and Technology, Daejeon, South Korea Je-Kyun Park, Korea Advanced Institute of Science and Technology, Daejeon, South Korea Tai Hyun Park, Seoul National University, Seoul, South Korea Brian F. Pfleger, University of Wisconsin −Madison, Madison, WI, USA Nathan D. Price, Institute for Systems Biology, Seattle, WA, USA Mikhail L. Rabinovich, Bach Institute of Biochemistry, Moscow, Russia Anurag S. Rathore, Indian Institute of Technology, New Delhi, India Frank Riske, BioProcess Technology Consultants, Inc., Woburn, MA, USA Anne Skaja Robinson, Tulane University, New Orleans, LA, USA Cecilia Roque, Universidade Nova de Lisboa, Caparica, Portugal Berthold Rutz, European Patent Office, Munich, Germany Andreas Schmid, Helmholtz-Centre for Environmental Research, Leipzig, Germany Herta Steinkellner, University of Natural Resources and Life Sciences, Vienna, Austria Eva Stöger, University of Natural Resources and Life Sciences, Vienna, Austria Editor-in-Chief Cate Livingstone

Promoters adjust cellular gene expression in response to internal or external signals and are key elements for implementing dynamic metabolic engineering concepts in fermentation processes. One useful signal is the dissolved oxygen content of the culture medium, since production phases often proceed in anaerobic conditions. Although several oxygen-dependent promoters have been described, a comprehensive and comparative study is missing. The goal of this work is to systematically test and characterize 15 promoter candidates that have been previously reported to be induced upon oxygen depletion in Escherichia coli. For this purpose, we developed a microtiter plate-level screening using an algal oxygen-independent flavin-based fluorescent protein and additionally employed flow cytometry analysis for verification. Various expression levels and dynamic ranges could be observed, and six promoters (nar-strong, nar-medium, nar-weak, nirB-m, yfiD-m, and fnrF8) appear particularly suited for dynamic metabolic engineering applications. We demonstrate applicability of these candidates for dynamic induction of enforced ATP wasting, a metabolic engineering approach to increase productivity of microbial strains that requires a narrow level of ATPase expression for optimal function. The selected candidates exhibited sufficient tightness under aerobic conditions while, under complete anaerobiosis, driving expression of the cytosolic F1-subunit of the ATPase from E. coli to levels that resulted in unprecedented specific glucose uptake rates. We finally utilized the nirB-m promoter to demonstrate the optimization of a two-stage lactate production process by dynamically enforcing ATP wasting, which is automatically turned on in the anaerobic (growth-arrested) production phase to boost the volumetric productivity. Our results are valuable for implementing metabolic control and bioprocess design concepts that use oxygen as signal for regulation and induction.

The biotechnology industry can significantly benefit from new paradigms such as smart manufacturing, digitalization and quality-by-design to render more competitive and robust processes. Real-time monitoring of key process parameters and performance indicators can facilitate the transition toward smart biomanufacturing. Since cells are typically used to catalyze biotechnological processes, online monitoring of the cell's health and intracellular metabolic status is of interest to the bioprocess industry. However, intracellular monitoring is challenging due to the intrinsic physical limitation imposed by the cell wall/membrane and the long time required for analytical measurements. This work outlines how soft sensors based on moving horizon estimation allow inferring the intracellular metabolite concentrations in bioprocesses, even for advanced or complex metabolic systems. For example, it can be applied for monitoring metabolic cybergenetic systems, whereby metabolic pathways are dynamically regulated via genetic circuits and external inputs. The moving horizon estimator uses a kinetic model of the central carbon and energy metabolism that describes the dynamics of the intracellular metabolites. We underline the use of moving horizon estimation considering the anaerobic fermentation of Escherichia coli with optogenetic regulation of the adenosine triphosphate turnover. With the information on the extracellular substrate, product and biomass concentrations, we could reconstruct the internal cell's metabolic state with a good performance.

Synthetic microbial communities are promising production strategies that can circumvent, via division of labor, many challenges associated with monocultures in biotechnology. Here, we consider microbial communities as lumped metabolic pathways where their members catalyze different metabolic submodules. We outline a machine learning-supported cybergenetic strategy for manipulating the reaction rates of microbial consortia via dynamic regulation of key biomass population levels. To do so, we show a quasi-unstructured modeling approach for synthetic microbial communities with external regulation of intracellular growth regulatory components. Then, we formulate an optimal control problem to find the optimal initial conditions and dynamic input trajectories. We use model predictive control to address system uncertainty, which can be coupled to an observer based on moving horizon estimation. Using a two-member community with optogenetic control as a simulation example, we found the optimal initial biomass concentrations and light intensity trajectories to maximize naringenin production.

Biotechnology offers many opportunities for the sustainable manufacturing of valuable products. The toolbox to optimize bioprocesses includes extracellular process elements such as the bioreactor design and mode of operation, medium formulation, culture conditions, feeding rates, and so on. 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.

Abstract Background The microbial production of isobutanol holds promise to become a sustainable alternative to fossil-based synthesis routes for this important chemical. Escherichia coli has been considered as one production host, however, due to redox imbalance, growth-coupled anaerobic production of isobutanol from glucose in E. coli is only possible if complex media additives or small amounts of oxygen are provided. These strategies have a negative impact on product yield, productivity, reproducibility, and production costs. Results In this study, we propose a strategy based on acetate as co-substrate for resolving the redox imbalance. We constructed the E. coli background strain SB001 (ΔldhA ΔfrdA ΔpflB) with blocked pathways from glucose to alternative fermentation products but with an enabled pathway for acetate uptake and subsequent conversion to ethanol via acetyl-CoA. This strain, if equipped with the isobutanol production plasmid pIBA4, showed robust exponential growth (µ = 0.05 h−1) under anaerobic conditions in minimal glucose medium supplemented with small amounts of acetate. In small-scale batch cultivations, the strain reached a glucose uptake rate of 4.8 mmol gDW−1 h−1, a titer of 74 mM and 89% of the theoretical maximal isobutanol/glucose yield, while secreting only small amounts of ethanol synthesized from acetate. Furthermore, we show that the strain keeps a high metabolic activity also in a pulsed fed-batch bioreactor cultivation, even if cell growth is impaired by the accumulation of isobutanol in the medium. Conclusions This study showcases the beneficial utilization of acetate as a co-substrate and redox sink to facilitate growth-coupled production of isobutanol under anaerobic conditions. This approach holds potential for other applications with different production hosts and/or substrate–product combinations.