DESCRIPTION (provided by applicant): Genome-scale in silico models capable of describing cellular functions represents the complex link between cellular genotype and environmental conditions to an expressed phenotype. The development of highly detailed in silico metabolic and regulatory models has ensued using genome-scale networks and flux balance analysis (FBA). However, not addressed in these recent developments of prokaryotes are regulatory events leading to cellular differentiation. A genome-scale in silico model of anaerobic Clostridium acetobutylicum is proposed to study the effects of cellular differentiation (sporulation) on the metabolic network, including pathways related to solventogenesis. Long-term goals include the use of developed genome-scale model for in silico design of metabolic engineering experiments to maximize solvent (butanol, ethanol and acetone) production while minimizing/eliminating cellular differentiation (sporulation). In addition, as C. acetobutylicum is closely related with known pathogenic clostridia, application of the proposed model will include identification of targets for future antibiotic development by locating clostridia- specific enzymes required for growth. Specific aims inlcude the development of independent metabolic networks to describe observable characteristics in defined "compartments" of culture growth and differentiation in the presence of dominant sigma factors: (1) vegetative growth (sigma-A), (2) early sporulation (sigma-H, sigma-F), (3) middle sporulation (sigma-E, sigma-G) and (4) late sporulation (sigma- K). Identification of sets of regulatory rules for cellular differentiation will be proposed through analysis of DMA microarray transcriptional data, and non-transcriptional regulation rules will be generated from a wealth of literature data and data available in the Papoutsakis (host) laboratory. Then, genetic algorithms will be applied to select sets of governing regulatory rules that optimize the transitions between the temporal "compartments" described above. Using genomic information to construct a mathematical model capable of describing all inner-workings of a specific clostridia cell will provide significant contributions to public health and biotechnology. This model will be used to identify targets for the medical community to develop new more effective and specific antibiotics to which clostridia cannot develop resistance. In the case of certain clostridia, this model will also be used to identify targets of metabolic engineering that result in increased production of biofuels (ethanol and butanol) and renewable energy (hydrogen).