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Metabolic network reconstruction and genome-scale model of butanol-producing strain clostridium beijerinckii ncimb 8052

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Title: Metabolic network reconstruction and genome-scale model of butanol-producing strain clostridium beijerinckii ncimb 8052
Author(s): Milne, Caroline
Advisor(s): Price, Nathan D.
Department / Program: Chemical & Biomolecular Engr
Discipline: Chemical Engineering
Degree Granting Institution: University of Illinois at Urbana-Champaign
Degree: M.S.
Genre: Thesis
Subject(s): Clostridium beijerinckii systems biology butanol genome-scale model
Abstract: 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 the metabolism of this microorganism from a systems viewpoint using constraint-based modeling allows for simulation of the global effect of various genetic modifications. 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 and extensive manual curation to improve model completeness. Interestingly, we found only a 34% overlap in annotation information between the three databases—highlighting the importance of evaluating the predictive accuracy of the resulting genome-scale model. To validate the iCM925 model we conducted fermentation experiments using the NCIMB 8052 strain, and evaluated the ability of the model to simulate 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, the model was unable to reproduce the observed profiles without additional constraints. 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 = 3.52x10-9, Fisher’s exact test). Inhibition of the hydrogenase reaction was found to have the largest effect on butanol formation—a relationship that has been experimentally observed. Our findings show that the 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 sustainable butanol production.
Issue Date: 2011-05-25
URI: http://hdl.handle.net/2142/24360
Rights Information: Copyright 2011 Caroline Milne
Date Available in IDEALS: 2011-05-25
2013-05-26
Date Deposited: 2011-05
 

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