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Projects: Projects for Investigator
Reference Number	BB/J01558X/1
Title	High throughput analysis of cell growth data from phenotype arrays
Status	Completed
Energy Categories	Renewable Energy Sources(Bio-Energy, Other bio-energy) 30%; Not Energy Related 70%;
Research Types	Basic and strategic applied research 100%
Science and Technology Fields	BIOLOGICAL AND AGRICULTURAL SCIENCES (Biological Sciences) 80%; PHYSICAL SCIENCES AND MATHEMATICS (Applied Mathematics) 20%;
UKERC Cross Cutting Characterisation	Not Cross-cutting 100%
Principal Investigator	Dr D (Dov ) Stekel No email address given Biosciences University of Nottingham
Award Type	Research Grant
Funding Source	BBSRC
Start Date	15 October 2012
End Date	14 October 2015
Duration	36 months
Total Grant Value	£273,083
Industrial Sectors	Transport Systems and Vehicles
Region	East Midlands
Programme
Investigators	Principal Investigator	Dr D (Dov ) Stekel , Biosciences, University of Nottingham (99.996%)
	Other Investigator	Dr T (Theodore ) Kypraios , Mathematical Sciences, University of Nottingham (0.001%) Dr H (Helen ) West , Biosciences, University of Nottingham (0.001%) Dr J (Jonathan ) Hobman , Biosciences, University of Nottingham (0.001%) Dr C (Chris ) Powell , Biosciences, University of Nottingham (0.001%)
Web Site
Objectives	This work will have direct and/or indirect impacts on academic beneficiaries, industry, the general public, the public sector and schools. Academic beneficiaries for this research will include microbiologists, systems biologists and cell biologists who are using phenotype microarrays as tools to understand how prokaryotic and eukaryotic cells respond to nutrition, environment and inhibitory compounds, who are characterizing novel pathogens, and characterizing strains that can be used to produce future fuels, chemicals and pharmaceuticals. This includes the major facilities at the AHVLA, the Sanger Institute and the BBSRC-funded TGAC. This research will allow them to extract more information from the data already generated, and further enhance multi-disciplinarity approaches to understanding the response and function of organisms. This impact will be direct. Direct impact to industry will come to companies in the pharmaceutical and biotechnology sectors that use Biolog devices. Industrial applications include analysis of new pathogens, evaluating new drugs, toxicology testing, functional genomics, optimizing growth and secondary metabolite production, cell and enzyme based assays. Improved methods for analysis of these data will have impacts on companies' return on investment in the technology. Indirect impact to industry will come through identification of yeast strains with potential commercial value for bioenergy production. When identified, these will be exploited by researchers in the UK (BBSRC Sustainable Bioenergy Centre) and our collaborators in the USA (Energy Biosciences Institute, Joint Bioenergy Institute) and Brazil (EMBRAPA). Furthermore it is anticipated that this data will also permit our LACE industrial partners (BP, British Sugar, DSM, SABMiller, Coors, Lallemand) to interrogate the data sets more effectively and select strains for deployment in commercial fermentation scenarios. Indirect impacts will be enjoyed by the general public in terms of food safety and bioenergy. In health, the rapid phenotypic characterization of new pathogens and understanding their survival, persistence and resistance could improve our understanding, prevention and treatment of foodborne disease outbreaks. In bioenergy, this research can contribute towards environmental sustainability, and reduction in reliance on imported fuels, reduced use of food crops for fuel, and a reduction in emissions. Indirect impact to the public sector will come as a result of our applications to food safety and bioenergy. With regards food safety, increased understanding of the persistence of pathogenic E. coli strains in food production and the persistence and remediation of pathogens from soil will, in the long term, aid government intervention and decision making. Similarly, a second generation of bioenergy agents will lead to potential for increased bioenergy production, that can impact on government decisions of overall energy production s trategy. Direct impacts on schools will come through a programme of direct engagement with Science Clubs and G&T schemes in local and regional schools. We will use food safety and bioenergy as ways of engaging young people with microbiology. We already have on-going contact with Wilesthorpe School (Long Eaton), St Matthews School (Duddeston), and Prince Albert J.I. School (Aston), the latter two being in areas of social and economic deprivation, as well as with the Association of Science Educators. This research will also foster training of skilled multidisciplinary individual and cross-disciplinary training.
Abstract	Biolog phenotype microarrays are unique tools for high throughput analysis of phenotypic responses of organisms to diverse conditions. They are highly sensitive, rapidly responsive and non-destructive to the samples, enabling repeated measurements over the timescale of an experiment. They are increasingly widely used for analysis of new pathogens, evaluating new drugs, toxicology testing, functional genomics, optimizing growth and secondary metabolite production, cell and enzyme based assays. The software provided with the system summarizes the time-course as a single datum. Thus, currently, most of the information generated during the experiment is not used. In fact, the time courses generated can be extremely varied: some are simple, and can be fitted by simple models (such as logistic growth); others are complex, with multi-stage lag phases, or exhibit diauxic switching. These time courses often contain important and detailed information, both qualitative (shape of curve) and quantitative (values of parameters) about the response of the organism of study to the environmental conditions. As a consequence, there is a big research gap: how best to effectively and robustly ascertain the key qualitative and quantitative phenotypic output from high throughput Biolog PM experiments. We aim to create improved mathematical models combined with sophisticated inference and model choice techniques that will allow users to derive maximum information from the Biolog output. Moreover, we will produce user-friendly open-source software to allow laboratory users to carry out these analyses in high throughput. These aims will be achieved by building on our existing Systems Biology and Biostatistics programmes. We will apply the methods developed to data already obtained in our food safety and bioenergy research programmes, so this work will have specific impacts in these areas, as well as general benefit for all users of this technology.
Publications	(none)
Final Report	(none)
Added to Database	14/04/14