초록
<P><B>Significance</B></P><P>This work elucidates the interdependence of gene regulation, metabolism, and environmental cues during clostridial acetone-butanol-ethanol (ABE) fermentation. It also demonstrates the necessity of the integrative view for quantitative understanding of that complex process. Therefore, this work advances our fundamental knowledge concerning ABE fermentation. In addition, the work provides a quantitative tool for generating new hypotheses and for guiding strain design and protocol optimization, which facilitates the development of next-generation biofuels. More broadly, by using ABE fermentation as an example, the work further sheds light on systems biology toward an integrated and quantitative understanding of complex microbial physiology.</P><P>Microbial metabolism involves complex, system-level processes implemented via the orchestration of metabolic reactions, gene regulation, and environmental cues. One canonical example of such processes is acetone-butanol-ethanol (ABE) fermentation by <I>Clostridium acetobutylicum</I>, during which cells convert carbon sources to organic acids that are later reassimilated to produce solvents as a strategy for cellular survival. The complexity and systems nature of the process have been largely underappreciated, rendering challenges in understanding and optimizing solvent production. Here, we present a system-level computational framework for ABE fermentation that combines metabolic reactions, gene regulation, and environmental cues. We developed the framework by decomposing the entire system into three modules, building each module separately, and then assembling them back into an integrated system. During the model construction, a bottom-up approach was used to link molecular events at the single-cell level into the events at the population level. The integrated model was able to successfully reproduce ABE fermentations of the WT <I>C. acetobutylicum</I> (ATCC 824), as well as its mutants, using data obtained from our own experiments and from literature. Furthermore, the model confers successful predictions of the fermentations with various network perturbations across metabolic, genetic, and environmental aspects. From foundation to applications, the framework advances our understanding of complex clostridial metabolism and physiology and also facilitates the development of systems engineering strategies for the production of advanced biofuels.</P>