Evidence of global warming, a desire for energy security and a push for sustainability have made research in renewable liquid fuels increasingly important. Butanol is a higher carbon alcohol with lower water affinity and vapor pressure than ethanol, and hence holds great promise as a liquid fuel alternative. Although producible using the current ethanol production process, low concentrations generated during fermentation is a major challenge. Clostridium beijerinckii has the potential to enhance industrial production of butanol in an economically advantageous manner, as it is a natural hyper-butanol producing strain and has the potential to co-ferment both pentoses and hexoses present in the cellulosic biomass feedstock. The challenge with this bacterium is that its butanol production capacity still remains below the threshold needed to maximize profitability. A systems biology approach to the rational engineering of a lesser-characterized organism such as C. beijerinckii allows for exploration of phenotype modifications using a genome-scale computational model.

The successful reconstruction and modification of an organism's metabolic network for a specific industrial goal has been demonstrated in model organisms (e.g. E. coli and S. cerevisiae). In this work, the application of metabolic reconstruction and modeling for the enhancement of a particular characteristic trait is demonstrated on C. beijerinckii NCIMB 8052. Genome annotation databases (e.g. Kegg, NCBI) and published in silico models were used to compile an initial list of metabolic genes and reactions for the reconstruction, and these data were supplemented through extensive literature searches and model-inferred gap filling. The reconstruction in itself is beneficial as it not only highlights discrepancies and missing information in the existing C. beijerinckii knowledge base, but also allows for genome-scale modeling of metabolic function using various linear algebra and computational methods.

Constraint-based analysis on the metabolic model provides information needed to rationally align the natural objectives of the organism (selection pressure for growth) with the industrial objectives of the engineer (butanol production). Specifically, by using flux balance analysis and other network optimization tools, we are able to predict network behavior for different cellular objectives and environmental states. This allows for identification of key metabolic pathways whose modification may enhance butanol production. Furthermore, in silico gene knockouts and other pathway adjustment methods available for the model all serve as guides to experimentally engineer C. beijerinckii to the network state needed to make it an economically competitive fermentation organism. Demonstration of systems biology guided cellular engineering of a lesser-characterized organism will lead to more rapid modeling and engineering of other organisms, and has the potential to significantly impact the future of biotechnology.