Conditional confined oscillatory dynamics of Escherichia coli strain K12-MG1655 in chemostat systems

Redox controls metabolic robustness in the gas-fermenting acetogen Clostridium autoethanogenum

Living biological systems display a fascinating ability to self-organize their metabolism. This ability ultimately determines the metabolic robustness that is fundamental to controlling cellular behavior. However, fluctuations in metabolism can affect cellular homeostasis through transient oscillations. For example, yeast cultures exhibit rhythmic oscillatory behavior in high cell-density continuous cultures. Oscillatory behavior provides a unique opportunity for quantitating the robustness of metabolism, as cells respond to changes by inherently compromising metabolic efficiency. Here, we quantify the limits of metabolic robustness in self-oscillating autotrophic continuous cultures of the gas-fermenting acetogen Clostridium autoethanogenum Online gas analysis and high-resolution temporal metabolomics showed oscillations in gas uptake rates and extracellular byproducts synchronized with biomass levels. The data show initial growth on CO, followed by growth on CO and H2 Growth on CO and H2 results in an accelerated growth phase, after which a downcycle is observed in synchrony with a loss in H2 uptake. Intriguingly, oscillations are not linked to translational control, as no differences were observed in protein expression during oscillations. Intracellular metabolomics analysis revealed decreasing levels of redox ratios in synchrony with the cycles. We then developed a thermodynamic metabolic flux analysis model to investigate whether regulation in acetogens is controlled at the thermodynamic level. We used endo- and exo-metabolomics data to show that the thermodynamic driving force of critical reactions collapsed as H2 uptake is lost. The oscillations are coordinated with redox. The data indicate that metabolic oscillations in acetogen gas fermentation are controlled at the thermodynamic level.

Heterogeneity in Pure Microbial Systems: Experimental Measurements and Modeling

Cellular heterogeneity influences bioprocess performance in ways that until date are not completely elucidated. In order to account for this phenomenon in the design and operation of bioprocesses, reliable analytical and mathematical descriptions are required. We present an overview of the single cell analysis, and the mathematical modeling frameworks that have potential to be used in bioprocess control and optimization, in particular for microbial processes. In order to be suitable for bioprocess monitoring, experimental methods need to be high throughput and to require relatively short processing time. One such method used successfully under dynamic conditions is flow cytometry. Population balance and individual based models are suitable modeling options, the latter one having in particular a good potential to integrate the various data collected through experimentation. This will be highly beneficial for appropriate process design and scale up as a more rigorous approach may prevent a priori unwanted performance losses. It will also help progressing synthetic biology applications to industrial scale.