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Projects: Projects for Investigator
Reference Number	BB/I004513/1
Title	Systems biology of the butanol-producing Clostridium acetobutylicum: new source of biofuel and chemicals/COSMIC2
Status	Completed
Energy Categories	Renewable Energy Sources(Bio-Energy, Production of other biomass-derived fuels (incl. Production from wastes)) 50%; Renewable Energy Sources(Bio-Energy, Production of transport biofuels (incl. Production from wastes)) 50%;
Research Types	Basic and strategic applied research 100%
Science and Technology Fields	PHYSICAL SCIENCES AND MATHEMATICS (Applied Mathematics) 100%
UKERC Cross Cutting Characterisation	Not Cross-cutting 100%
Principal Investigator	Prof J (John ) King No email address given Mathematical Sciences University of Nottingham
Award Type	Research Grant
Funding Source	BBSRC
Start Date	01 April 2010
End Date	10 May 2013
Duration	37 months
Total Grant Value	£230,848
Industrial Sectors	Manufacturing; Transport Systems and Vehicles
Region	East Midlands
Programme	Systems Biology of Microorganisms 2 (SysMo2)
Investigators	Principal Investigator	Prof J (John ) King , Mathematical Sciences, University of Nottingham (100.000%)
Web Site
Objectives	Beneficaries: The ultimate goal of this project is to generate strains of Clostridium acetobutylicum that produce biobutanol with greater productivity and which can form the basis of a commercial process for biobutanol production. The major direct beneficary is, therefore, the industrial private sector concerned with chemical commodity production. A boost for the agricultural sector is also expected, as farmers will be able to profit from the demand for cellulosic waste products which form the substrates for biobutanol production. The results will also benefit national and international government green policies in helping to replace fossil fuels with biofuel. Achieving government targets in terms of green issues will also indirectly benefit the wider general public. How they benefit from this research: At the basic level, industrial producers will benefit through the availability of strains which may be employed as the basis of an economic process for biobutanol production. These beneficaries may be expected to gain a commercial advantage over competitors. The ultimate development of a biobutanol process will reduce national, and international, reliance on fossil fuels in the transportation sector, providing a cleaner environment and therefore indirectly impacting on human health. The technological developments will also provide an opportunity for export to third countries providing revenue for UK Plc. The project will also provide the opportunity for staff working directly on the project, together with postgraduate students indirectly affiliated to the project, to become trained in the arena of the strategically important areas of 'Systems Biology' and 'Bioenergy'. These skills should prove applicable to many different projects outside of butanol metabolism. To ensure that they benefit: The PI of the experimental partner programme at the University of Nottingham (Professor Nigel Minton) already has strong links with a major UK Biofuel company, who part fund other BBSRC projects at the University and with whom a commercial agreement is already in place. A high degree of collaboration will be maintained with this company, while at the same time other collaborative ventures will be explored. Through liaison with the University of Nottingham's Research and Innovation Services, Professor Minton will continue to monitor the IP and commercial potential of the research to be undertaken here, and will additionally chair a consortium wide committee concerned with IP and commercialisation of the consortia outputs. Professor King will draw upon his wide-ranging experience in industrial mathematics and multidisciplinary research in assisting in such developments and in ensuring that the mathematical modelling work encompasses the key biological mechanisms that control the productivity of biobutanol production, ensuring its impact in the above contexts. Over and above these activities, the project members will endeavour tocommunicatetheir work widely, b oth through the scientific and non-scientific press and through various media outlets, including websites.
Abstract	The enzymes catalyzing and controlling conversion of glucose to solvents and acids are encoded by up to 40 genes. Starting with model-driven hypotheses, specific mutants will be generated by knock-in and knock-out strategies and analyzed. Selected mutants will be grown in continuous culture, allowing the imposition of reproducible, controlled perturbations. Fermentation analysis will include substrate/product concentrations, determination of key intracellular metabolites, and transcriptome time series. These data will lead to further iterative experimentation and ultimately to a fine-tuned quantitative description of the process of solventogenesis. Workpackages:- 1) Construction of artificially controlled genes required for solvent production/regulation: ACE technology will place specific chromosomal genes under inducible control. These changes, in combination with gene knock-outs, will allow rational perturbation of the system. 2) Analysis of mutants in continuous culture under standardized conditions: Strains will be grown and analyzed in continuous culture. Quantitative analysis will include substrate and product determination, identification and quantification of key intracellular metabolites, and transcriptome time series. 3) Modelling, in silico generation of hypotheses and experimental design: An iterative process of model-based hypothesis generation and experimental testing by variations of the transcriptome and the environome will be adopted for refining the model. Controlled stimuli from the environome and rapid sampling experiments will be included. 4) Data management: DaMaSys will serve as an access-controlled repository for multi-'omics' datasets as well as platform for communication and joint model development. Data pre-treatment, data consistency checks, data curation for modelling purposes, and, in part, the cyclic interaction between model-based hypothesis generation and experimental testing will be organized into automatable workflows.
Publications	(none)
Final Report	(none)
Added to Database	10/12/13