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Open Access Highly Accessed Research article

Metabolic network reconstruction and genome-scale model of butanol-producing strain Clostridium beijerinckii NCIMB 8052

Caroline B Milne12, James A Eddy23, Ravali Raju7, Soroush Ardekani1, Pan-Jun Kim2, Ryan S Senger5, Yong-Su Jin26, Hans P Blaschek246 and Nathan D Price1238*

Author affiliations

1 Department of Chemical and Biomolecular Engineering, University of Illinois, Urbana, IL, USA

2 Institute for Genomic Biology, University of Illinois, Urbana, IL, USA

3 Department of Bioengineering, University of Illinois, Urbana, IL, USA

4 Center for Advanced BioEnergy Research, University of Illinois, Urbana, IL, USA

5 Department of Biological Systems Engineering, Virginia Tech, Blacksburg, VA, USA

6 Department of Food Science and Human Nutrition, University of Illinois, Urbana, IL, USA

7 Department of Chemical Engineering and Material Science, University of Minnesota, MN, USA

8 Institute for Systems Biology, 401 Terry Avenue N, Seattle, WA 98109, USA

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Citation and License

BMC Systems Biology 2011, 5:130  doi:10.1186/1752-0509-5-130

Published: 16 August 2011

Abstract

Background

Solventogenic clostridia offer a sustainable alternative to petroleum-based production of butanol--an important chemical feedstock and potential fuel additive or replacement. C. beijerinckii is an attractive microorganism for strain design to improve butanol production because it (i) naturally produces the highest recorded butanol concentrations as a byproduct of fermentation; and (ii) can co-ferment pentose and hexose sugars (the primary products from lignocellulosic hydrolysis). Interrogating C. beijerinckii metabolism from a systems viewpoint using constraint-based modeling allows for simulation of the global effect of genetic modifications.

Results

We present the first genome-scale metabolic model (iCM925) for C. beijerinckii, containing 925 genes, 938 reactions, and 881 metabolites. To build the model we employed a semi-automated procedure that integrated genome annotation information from KEGG, BioCyc, and The SEED, and utilized computational algorithms with manual curation to improve model completeness. Interestingly, we found only a 34% overlap in reactions collected from the three databases--highlighting the importance of evaluating the predictive accuracy of the resulting genome-scale model. To validate iCM925, we conducted fermentation experiments using the NCIMB 8052 strain, and evaluated the ability of the model to simulate measured substrate uptake and product production rates. Experimentally observed fermentation profiles were found to lie within the solution space of the model; however, under an optimal growth objective, additional constraints were needed to reproduce the observed profiles--suggesting the existence of selective pressures other than optimal growth. Notably, a significantly enriched fraction of actively utilized reactions in simulations--constrained to reflect experimental rates--originated from the set of reactions that overlapped between all three databases (P = 3.52 × 10-9, Fisher's exact test). Inhibition of the hydrogenase reaction was found to have a strong effect on butanol formation--as experimentally observed.

Conclusions

Microbial production of butanol by C. beijerinckii offers a promising, sustainable, method for generation of this important chemical and potential biofuel. iCM925 is a predictive model that can accurately reproduce physiological behavior and provide insight into the underlying mechanisms of microbial butanol production. As such, the model will be instrumental in efforts to better understand, and metabolically engineer, this microorganism for improved butanol production.