Kittler, S. | Search bioRxiv

bioRxiv2026

Coupling of ATP and PMF in Escherichia coli is determined by growth conditions

The ATP molecule is the universal energy currency across all living organisms. There are two fundamental pathways of ATP synthesis: substrate-level and oxidative phosphorylation. While substrate-level phosphorylation generates ATP directly, in oxidative phosphorylation, proton motive force (PMF) is required to power ATP synthesis via the F1Fo ATP synthase. Using Escherichia coli, we show that due to simultaneous use of both pathways, the strength of coupling between ATP and PMF strongly depends on growth conditions: coupling is weak when requirements for independent generation of ATP and PMF are met, and becomes essential when not. We determine the conditions, under which PMF-ATP coupling becomes essential and show that PMF is required for bacterial growth irrespective of its ATP synthesis function. We propose that the main role of F1Fo in Escherichia coli, contrary to the canonical view, is not to generate ATP but to provide an auxiliary pathway that allows both, ATP and PMF, to be produced.

microbiology↗

bioRxiv2024

Optimizing Bioprocessing Efficiency with OptFed: Dynamic Nonlinear Modeling Improves Product-to-Biomass Yield by 19%

Biotechnological production of recombinant molecules relies heavily on fed-batch processes. However, as the cells growth, substrate uptake, and production kinetics are often unclear, the fed-batches are frequently operated under sub-optimal conditions. Process design is based on simple feed profiles (e.g., constant or exponential), operator experience, and basic statistical tools (e.g., response surface methodology), which are unable to harvest the full potential of production. To address this challenge, we propose a general modeling framework, OptFed, which utilizes experimental data from non-optimal fed-batch processes to predict an optimal one. In detail, we assume that cell-specific rates depend on several state variables and their derivatives. Using measurements of bioreactor volume, biomass, and product, we fit the kinetic constants of ordinary differential equations. A regression model avoids overfitting by reducing the number of parameters. Thereafter, OptFed predicts optimal process conditions by solving an optimal control problem using orthogonal collocation and nonlinear programming. In a case study, we apply OptFed to a recombinant protein L fed-batch production process. We determine optimal controls for feed rate and reactor temperature to maximize the product-to-biomass yield and successfully validate our predictions experimentally. Notably, our framework outperforms RSM in both simulation and experiments, capturing an optimum previously missed. We improve the experimental product-to-biomass ratio by 19 % and showcase OptFeds potential for enhancing process optimization in biotechnology.

bioengineering↗