Kinetic model of clostridium ABE fermentation responsive to exogenous butyrate addition, amino acids effect and in combination with Saccharomyces cerevisiae coculture

Abstract

In the continuous effort to find efficient alternatives to the dominant fossil fuels, whose burning has already caused large impacts on global warming in terms of GHGs emissions most notably CO2, lignocellulosic ABE fermentation by Clostridial microorganisms is one of the most notable methods employed. The yield of biofuels produced by this process especially biobutanol is highly desired to be increased due to its valuable features, through the development of several techniques. Various methods are suggested in literature claiming to increase the butanol yield and/or productivity, such as adding butyrate as an exogenous co-substrate to the culture, as well as adding Saccharomyces cerevisiae as a coculture. In parallel, mathematical modeling is a crucial part in assisting experimental findings and has become an essential tool to validate proposed hypotheses, since it has the capability of showing how the fermentation system behaves mechanistically. This study suggests a kinetic model that is developed in detail to answer the proposed strategies mainly: adding butyrate, coculturing with another bacterium, and a combination of both, and analyze the final outputs. For the exogenous butyrate addition, the model is developed to described four scenarios being adding 0, 2, 4, 6 g/L of butyrate at the starting time of fermentation. It showed good accordance with experimental data and accurately described the positive responding of the system upon adding butyrate until passing the 4 g/L concentration where the effect of butyrate becomes more inhibitory. As for the coculture, the effect of amino acids naturally accumulated or exogenously added was incorporated on metabolic enzymes for regulation. In addition, the dual effect (coculture + butyrate addition) was also simulated, achieving the best concentration of 15.5 g/L butanol. The model was executed on MATLAB 2020, and parameter sensitivity analysis and scans were developed and performed for further investigation. The model played its role successfully in describing the dynamic progression of metabolites and had the capability to show how the fermentation system behaves mechanistically when subjected to alterations. It can strongly serve as an asset for the development of further studies whether computationally or experimentally.