Short-term dynamic behavior of Escherichia coli in response to successive glucose pulses on glucose-limited chemostat cultures

A reinforcement learning-based hybrid modeling framework for bioprocess kinetics identification

Constructing predictive models to simulate complex bioprocess dynamics, particularly time-varying (i.e., parameters varying over time) and history-dependent (i.e., current kinetics dependent on historical culture conditions) behavior, has been a longstanding research challenge. Current advances in hybrid modeling offer a solution to this by integrating kinetic models with data-driven techniques. This article proposes a novel two-step framework: first (i) speculate and combine several possible kinetic model structures sourced from process and phenomenological knowledge, then (ii) identify the most likely kinetic model structure and its parameter values using model-free Reinforcement Learning (RL). Specifically, Step 1 collates feasible history-dependent model structures, then Step 2 uses RL to simultaneously identify the correct model structure and the time-varying parameter trajectories. To demonstrate the performance of this framework, a range of in-silico case studies were carried out. The results show that the proposed framework can efficiently construct high-fidelity models to quantify both time-varying and history-dependent kinetic behaviors while minimizing the risks of over-parametrization and over-fitting. Finally, the primary advantages of the proposed framework and its limitation were thoroughly discussed in comparison to other existing hybrid modeling and model structure identification techniques, highlighting the potential of this framework for general bioprocess modeling.

Is energy excess the initial trigger of carbon overflow metabolism? Transcriptional network response of carbon-limited Escherichia coli to transient carbon excess

Background

Escherichia coli adapted to carbon-limiting conditions is generally geared for energy-efficient carbon utilization. This includes also the efficient utilization of glucose, which serves as a source for cellular building blocks as well as energy. Thus, catabolic and anabolic functions are balanced under these conditions to minimize wasteful carbon utilization. Exposure to glucose excess interferes with the fine-tuned coupling of anabolism and catabolism leading to the so-called carbon overflow metabolism noticeable through acetate formation and eventually growth inhibition.

Results

Cellular adaptations towards sudden but timely limited carbon excess conditions were analyzed by exposing slow-growing cells in steady state glucose-limited continuous culture to a single glucose pulse. Concentrations of metabolites as well as time-dependent transcriptome alterations were analyzed and a transcriptional network analysis performed to determine the most relevant transcription and sigma factor combinations which govern these adaptations. Down-regulation of genes related to carbon catabolism is observed mainly at the level of substrate uptake and downstream of pyruvate and not in between in the glycolytic pathway. It is mainly accomplished through the reduced activity of CRP-cAMP and through an increased influence of phosphorylated ArcA. The initiated transcriptomic change is directed towards down-regulation of genes, which contribute to active movement, carbon uptake and catabolic carbon processing, in particular to down-regulation of genes which contribute to efficient energy generation. Long-term changes persisting after glucose depletion and consumption of acetete encompassed reduced expression of genes related to active cell movement and enhanced expression of genes related to acid resistance, in particular acid resistance system 2 (GABA shunt) which can be also considered as an inefficient bypass of the TCA cycle.

Conclusions

Our analysis revealed that the major part of the trancriptomic response towards the glucose pulse is not directed towards enhanced cell proliferation but towards protection against excessive intracellular accumulation of potentially harmful concentration of metabolites including among others energy rich compounds such as ATP. Thus, resources are mainly utilized to cope with “overfeeding” and not for growth including long-lasting changes which may compromise the cells future ability to perform optimally under carbon-limiting conditions (reduced motility and ineffective substrate utilization).

Supplementary Information

The online version contains supplementary material available at 10.1186/s12934-022-01787-4.

Exclusion of the fittest predicts microbial community diversity in fluctuating environments

Microorganisms live in environments that inevitably fluctuate between mild and harsh conditions. As harsh conditions may cause extinctions, the rate at which fluctuations occur can shape microbial communities and their diversity, but we still lack an intuition on how. Here, we build a mathematical model describing two microbial species living in an environment where substrate supplies randomly switch between abundant and scarce. We then vary the rate of switching as well as different properties of the interacting species, and measure the probability of the weaker species driving the stronger one extinct. We find that this probability increases with the strength of demographic noise under harsh conditions and peaks at either low, high, or intermediate switching rates depending on both species' ability to withstand the harsh environment. This complex relationship shows why finding patterns between environmental fluctuations and diversity has historically been difficult. In parameter ranges where the fittest species was most likely to be excluded, however, the beta diversity in larger communities also peaked. In sum, how environmental fluctuations affect interactions between a few species pairs predicts their effect on the beta diversity of the whole community.

Escherichia coli metabolism under short-term repetitive substrate dynamics: adaptation and trade-offs

Background

Microbial metabolism is highly dependent on the environmental conditions. Especially, the substrate concentration, as well as oxygen availability, determine the metabolic rates. In large-scale bioreactors, microorganisms encounter dynamic conditions in substrate and oxygen availability (mixing limitations), which influence their metabolism and subsequently their physiology. Earlier, single substrate pulse experiments were not able to explain the observed physiological changes generated under large-scale industrial fermentation conditions.

Results

In this study we applied a repetitive feast–famine regime in an aerobic Escherichia coli culture in a time-scale of seconds. The regime was applied for several generations, allowing cells to adapt to the (repetitive) dynamic environment. The observed response was highly reproducible over the cycles, indicating that cells were indeed fully adapted to the regime. We observed an increase of the specific substrate and oxygen consumption (average) rates during the feast–famine regime, compared to a steady-state (chemostat) reference environment. The increased rates at same (average) growth rate led to a reduced biomass yield (30% lower). Interestingly, this drop was not followed by increased by-product formation, pointing to the existence of energy-spilling reactions. During the feast–famine cycle, the cells rapidly increased their uptake rate. Within 10 s after the beginning of the feeding, the substrate uptake rate was higher (4.68 μmol/g_CDW/s) than reported during batch growth (3.3 μmol/g_CDW/s). The high uptake led to an accumulation of several intracellular metabolites, during the feast phase, accounting for up to 34% of the carbon supplied. Although the metabolite concentrations changed rapidly, the cellular energy charge remained unaffected, suggesting well-controlled balance between ATP producing and ATP consuming reactions.

Conclusions

The adaptation of the physiology and metabolism of E. coli under substrate dynamics, representative for large-scale fermenters, revealed the existence of several cellular mechanisms coping with stress. Changes in the substrate uptake system, storage potential and energy-spilling processes resulted to be of great importance. These metabolic strategies consist a meaningful step to further tackle reduced microbial performance, observed under large-scale cultivations.

Current state and challenges for dynamic metabolic modeling

While the stoichiometry of metabolism is probably the best studied cellular level, the dynamics in metabolism can still not be well described, predicted and, thus, engineered. Unknowns in the metabolic flux behavior arise from kinetic interactions, especially allosteric control mechanisms. While the stoichiometry of enzymes is preserved in vitro, their activity and kinetic behavior differs from the in vivo situation. Next to this challenge, it is infeasible to test the interaction of each enzyme with each intracellular metabolite in vitro exhaustively. As a consequence, the whole interacting metabolome has to be studied in vivo to identify the relevant enzymes properties. In this review we discuss current approaches for in vivo perturbation experiments, that is, stimulus response experiments using different setups and quantitative analytical approaches, including dynamic carbon tracing. Next to reliable and informative data, advanced modeling approaches and computational tools are required to identify kinetic mechanisms and their parameters.

Microchemostat array with small-volume fraction replenishment for steady-state microbial culture

A chemostat is a bioreactor in which microorganisms can be cultured at steady-state by controlling the rate of culture medium inflow and waste outflow, thus maintaining media composition over time. Even though many microbial studies could greatly benefit from studying microbes in steady-state conditions, high instrument cost, complexity, and large reagent consumption hamper the routine use of chemostats. Microfluidic-based chemostats (i.e. microchemostats) can operate with significantly smaller reagent consumption while providing accurate chemostatic conditions at orders of magnitude lower cost compared to conventional chemostats. Also, microchemostats have the potential to significantly increase the throughput by integrating arrays of microchemostats. We present a microchemostat array with a unique two-depth culture chamber design that enables small-volume fraction replenishment of culture medium as low as 1% per replenishment cycle in a 250 nl volume. A system having an array of 8 microchemostats on a 40 × 60 mm(2) footprint could be automatically operated in parallel by a single controller unit as a demonstration for potential high throughput microbial studies. The model organism, Saccharomyces cerevisiae, successfully reached a stable steady-state of different cell densities as a demonstration of the chemostatic functionality by programming the dilution rates. Chemostatic functionality of the system was further confirmed by quantifying the budding index as a function of dilution rate, a strong indicator of growth-dependent cell division. In addition, the small-volume fraction replenishment feature minimized the cell density fluctuation during the culture. The developed system provides a robust, low-cost, and higher throughput solution to furthering studies in microbial physiology.