pH-induced gene regulation of solvent production by Clostridium acetobutylicum in continuous culture: parameter estimation and sporulation modelling

Sporulation in solventogenic and acetogenic clostridia

The Clostridium genus harbors compelling organisms for biotechnological production processes; while acetogenic clostridia can fix C1-compounds to produce acetate and ethanol, solventogenic clostridia can utilize a wide range of carbon sources to produce commercially valuable carboxylic acids, alcohols, and ketones by fermentation. Despite their potential, the conversion by these bacteria of carbohydrates or C1 compounds to alcohols is not cost-effective enough to result in economically viable processes. Engineering solventogenic clostridia by impairing sporulation is one of the investigated approaches to improve solvent productivity. Sporulation is a cell differentiation process triggered in bacteria in response to exposure to environmental stressors. The generated spores are metabolically inactive but resistant to harsh conditions (UV, chemicals, heat, oxygen). In Firmicutes, sporulation has been mainly studied in bacilli and pathogenic clostridia, and our knowledge of sporulation in solvent-producing or acetogenic clostridia is limited. Still, sporulation is an integral part of the cellular physiology of clostridia; thus, understanding the regulation of sporulation and its connection to solvent production may give clues to improve the performance of solventogenic clostridia. This review aims to provide an overview of the triggers, characteristics, and regulatory mechanism of sporulation in solventogenic clostridia. Those are further compared to the current knowledge on sporulation in the industrially relevant acetogenic clostridia. Finally, the potential applications of spores for process improvement are discussed.Key Points• The regulatory network governing sporulation initiation varies in solventogenic clostridia.• Media composition and cell density are the main triggers of sporulation.• Spores can be used to improve the fermentation process.

Mathematical modelling of clostridial acetone-butanol-ethanol fermentation

Clostridial acetone-butanol-ethanol (ABE) fermentation features a remarkable shift in the cellular metabolic activity from acid formation, acidogenesis, to the production of industrial-relevant solvents, solventogensis. In recent decades, mathematical models have been employed to elucidate the complex interlinked regulation and conditions that determine these two distinct metabolic states and govern the transition between them. In this review, we discuss these models with a focus on the mechanisms controlling intra- and extracellular changes between acidogenesis and solventogenesis. In particular, we critically evaluate underlying model assumptions and predictions in the light of current experimental knowledge. Towards this end, we briefly introduce key ideas and assumptions applied in the discussed modelling approaches, but waive a comprehensive mathematical presentation. We distinguish between structural and dynamical models, which will be discussed in their chronological order to illustrate how new biological information facilitates the ‘evolution’ of mathematical models. Mathematical models and their analysis have significantly contributed to our knowledge of ABE fermentation and the underlying regulatory network which spans all levels of biological organization. However, the ties between the different levels of cellular regulation are not well understood. Furthermore, contradictory experimental and theoretical results challenge our current notion of ABE metabolic network structure. Thus, clostridial ABE fermentation still poses theoretical as well as experimental challenges which are best approached in close collaboration between modellers and experimentalists.

System-level modeling of acetone-butanol-ethanol fermentation

Acetone-butanol-ethanol (ABE) fermentation is a metabolic process of clostridia that produces bio-based solvents including butanol. It is enabled by an underlying metabolic reaction network and modulated by cellular gene regulation and environmental cues. Mathematical modeling has served as a valuable strategy to facilitate the understanding, characterization and optimization of this process. In this review, we highlight recent advances in system-level, quantitative modeling of ABE fermentation. We begin with an overview of integrative processes underlying the fermentation. Next we survey modeling efforts including early simple models, models with a systematic metabolic description, and those incorporating metabolism through simple gene regulation. Particular focus is given to a recent system-level model that integrates the metabolic reactions, gene regulation and environmental cues. We conclude by discussing the remaining challenges and future directions towards predictive understanding of ABE fermentation.

The metabolic network of Clostridium acetobutylicum: Comparison of the approximate Bayesian computation via sequential Monte Carlo (ABC-SMC) and profile likelihood estimation (PLE) methods for determinability analysis

The Gram-positive bacterium Clostridium acetobutylicum is an anaerobic endospore-forming species which produces acetone, butanol and ethanol via the acetone-butanol (AB) fermentation process, leading to biofuels including butanol. In previous work we looked to estimate the parameters in an ordinary differential equation model of the glucose metabolism network using data from pH-controlled continuous culture experiments. Here we combine two approaches, namely the approximate Bayesian computation via an existing sequential Monte Carlo (ABC-SMC) method (to compute credible intervals for the parameters), and the profile likelihood estimation (PLE) (to improve the calculation of confidence intervals for the same parameters), the parameters in both cases being derived from experimental data from forward shift experiments. We also apply the ABC-SMC method to investigate which of the models introduced previously (one non-sporulation and four sporulation models) have the greatest strength of evidence. We find that the joint approximate posterior distribution of the parameters determines the same parameters as previously, including all of the basal and increased enzyme production rates and enzyme reaction activity parameters, as well as the Michaelis-Menten kinetic parameters for glucose ingestion, while other parameters are not as well-determined, particularly those connected with the internal metabolites acetyl-CoA, acetoacetyl-CoA and butyryl-CoA. We also find that the approximate posterior is strongly non-Gaussian, indicating that our previous assumption of elliptical contours of the distribution is not valid, which has the effect of reducing the numbers of pairs of parameters that are (linearly) correlated with each other. Calculations of confidence intervals using the PLE method back this up. Finally, we find that all five of our models are equally likely, given the data available at present.