Sunday, May 4, 2008
2-23

Development of a Genome-scale Metabolic Model for Improved Butanol Production

Yan Zhu, Yanping Zhang, and Yin Li. Center for Bioenergy and Industrial Biotechnology, Institute of Microbiology, Chinese Academy of Sciences, Datun Road, Chaoyang District, Beijing, 100101, China

A genome-scale metabolic model for the butanol producing bacterium Clostridium acetobutylicum ATCC 824 was constructed based on genome annotation and experimental data. The complete model constitutes 564 genes, 621 reactions, and 598 metabolites. Traditional biochemical system theory and modern statistical learning approaches were used to interpret complex fermentation profiles when strain ATCC 824 was grown in defined medium. Under the regulatory steady-state hypothesis, we constructed a set of equations to depict dynamic cellular-scape tending to metabolic equilibrium, which was supposed to be in the interval between two different gene expression states. The results showed that: (i) Based on constant experimental input and output fluxes, maximal ATP production was estimated and related to growth rate. (ii) Flux variability analysis supplemented the elementary mode analysis in identifying parallel pathways, e.g. pathways with identical end products but different co-factor usage and different inhibition or activation mechanism. (iii) With the different regulatory constraints added to simulation, such as the shift from acidification to production of butanol, central metabolic fluxes were compared quantitatively. Significantly improved production of butanol was observed when related genes were modified. Compared with stoichiometric modeling, these results demonstrate that the scaling-up dynamic model can be used to analyze the physiology of cell growth and butanol production on a defined medium with higher prediction accuracy than a linear system.

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