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Details of Grant 

EPSRC Reference: EP/S001301/1
Title: Quantitative Systems Biology Tools to Design Microbiome-Derived Products
Principal Investigator: Shoaie, Dr S
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department: Dental Institute
Organisation: Kings College London
Scheme: EPSRC Fellowship - NHFP
Starts: 01 June 2018 Ends: 30 September 2021 Value (£): 463,712
EPSRC Research Topic Classifications:
EPSRC Industrial Sector Classifications:
Pharmaceuticals and Biotechnology R&D
Related Grants:
Panel History:
Panel DatePanel NameOutcome
08 May 2018 EPSRC UKRI CL Innovation Fellowship Interview Panel 3 - 8 and 9 May 2018 Announced
Summary on Grant Application Form
Quantitative Systems Biology with its predictive strength has enabled biotech industries to reduce the time and cost of clinical studies through predicting the right targets and simulating the toxicity and side effects of proposed treatments. One of the common denominators of systems biology are Genome-scale Metabolic Models (GEMs). GEMs provide a structured connection between metabolites, reactions and genes, thus serving as a framework for the simulation, integration and interpretation of genome wide data. Using GEMs for gut bacteria, we recently developed a platform, for in silico analysis of microbial communities to predict the healthy phenotype and related dietary intake within the gut microbiota of cardiometabolic patients. This approach has already provoked great interest from a variety of different industries, including Danone to invest in integrating this platform in their future product design.

Here, we propose creating a comprehensive computational platform that can be used in the development of efficient treatment strategies that target the microbiome, and in evaluation of the efficacy of microbial-derived products. In this project, we will i) establish a method for rapid generation of functional GEMs for bacteria ii) generate a multi-task toolbox that can be used in analysing microbial diversity by assigning correct interactions between GEMs, and finding the optimum solutions in different clinical or environmental conditions iii) run thousands of simulations in health and disease conditions, identifying beneficial microbes to improve the patient's health status. This toolbox will allow prediction of the metabolic profile of a microbial community and will reveal the interplay between GEMs for different microbes and host cell types/tissues. The products developed via our toolbox will be used to inform in vivo manipulation of microbiome dysbiosis, or identifying/designing synthetic supplements naturally secreted by microbes that can mediate this manipulation. Finally, this approach will be expanded to generate of personalized microbiome GEMs, which can contribute to the development of personalized microbiome medicine.
Key Findings
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