HIGH THROUGHPUT DATA FRAMEWORK BASED CHARACTERIZATION AND EVALUATIONS OF THERMOBIFIDA FUSCA FOR INDUSTRIAL APPLICATIONS
Cellulolytic organisms are being heavily studied for the production of biofuels, given that lignocellulosic biomass would be a cheap, abundant, and renewable starting material for chemical production. A challenge with cellulolytic microorganisms is that they are typically poorly characterized and o...
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| Format: | Others |
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VCU Scholars Compass
2013
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| Online Access: | http://scholarscompass.vcu.edu/etd/3274 http://scholarscompass.vcu.edu/cgi/viewcontent.cgi?article=4273&context=etd |
| Summary: | Cellulolytic organisms are being heavily studied for the production of biofuels, given that lignocellulosic biomass would be a cheap, abundant, and renewable starting material for chemical production. A challenge with cellulolytic microorganisms is that they are typically poorly characterized and often difficult to genetically manipulate. Our group focuses characterization and engineering of a thermophilic aerobic, cellulolytic actinobacterium, Thermobifida fusca. The wider range of optimal temperature and pH for the growth condition, besides the secretion of several group of cellulases, have made this microbe a potentially efficient host system for industrially application. After the development of first ever successful genetic manipulation protocol by for T. fusca in 2011 in our group the quest continues to better understand and further explore this microbe with such remarkable capabilities. Available genome annotation of the bacteria gives a preliminary clue towards the exploration of its biological system. Genome-scale metabolic reconstruction provides one such framework to populate all the available piece of information to mimic the biological systems to the closest functional state. Further, this skeletal base network can be made more realistic by applying the constraint that controls the flux through various reactions in the pathway network thereby providing the optimal solution space for operation. For the purpose of curation of this in silico model, we aim to integrate the experimental datasets (proteomic and metabolomics) and optimize the agreement between the in silico and in vivo conditions at a steady state condition. Once the model considerably imitates the original biological network, it will be used for the fundamental understanding of the microbial system for the application towards production biofuel and high yields of compound of pharmaceutical interest. The ultimate objective of this project is to design the candidate strain for the cellulolytic production of Natural products. Natural products play an important role in manufacturing of several active pharmaceutical ingredients (APIs). APIs or precursors of APIs can be produced in living organisms with the major challenge of designing and optimizing metabolic pathways to obtain the compounds of interest. In this capacity, living organisms can act as renewable catalysts with high product specificity to produce APIs with potential cost savings over purely synthetic chemistry synthesis routes. This is an effort to understand and design industrially usable microorganism T. fusca to act as a host system for the purpose of production of these compounds. The present project focuses on, in silico characterization and experimental validation of T. fusca, with particular focus on the terpenoids backbone biosynthesis (TBB) pathways using a genome-scale metabolic model, transcriptomics, proteomics and metabolite analysis. The DXP pathway leads to the production of terpenoids precursors that have applications in nutraceutics and pharmaceutics. This study generates the metabolic model, iTFU975 for T. fusca based on the proteomics dataset as the starting point. Further the model and the experimental dataset together helps to characterize the secondary metabolites pathways and compounds in the network associated with the production of terpenoids. In conclusion, this is an effort to characterize the natural products biosynthesis in T. fusca by establishing a bridge between the analytical methodologies and computational efficiencies on “-omics” knowledge to prove the diverse applicability of Systems Biology. |
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