Milne2011 - Genome-scale metabolic network of Clostridium beijerinckii (iCB925)

Milne2011 - Genome-scale metabolic network of
Clostridium beijerinckii (iCB925)

This model is described in the article:

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

Milne CB, Eddy JA, Raju R, Ardekani
S, Kim PJ, Senger RS, Jin YS, Blaschek HP, Price ND.
BMC Syst Biol 2011; 5: 130
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.

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