Metabolic modelling of biofilms in a mixed population
Changes in microbiomes have been recently linked with major impacts on human health. Furthermore, sequencing studies can elucidate the abundance of the species with high accuracy. The large number of species and biological components involved in these microbiomes, coupled with their highly dynamic behaviour, make it difficult to extract information from available biological samples. Another major challenge is that such populations are often mixed (bacteria and fungi).
We therefore propose to leverage state-of-the-art tools in metabolic modelling (Magnúsdóttir et al., Nature Biotechnology, 2017) with the goal of augmenting the available data collected by Complement Genomics and explaining biofilm behaviour from a mechanistic point of view, towards a mixed- and multi-species genome-scale metabolic model of the microbiome. Both steady-state and dynamic approaches will be explored (Zhang et al., bioRxiv, 2018), with the idea of combining them towards a dynamic multi-scale model (Henson et al, Biochemical Society Transactions, 2015). The biofilm architecture (considering spatial arrangement, thickness, viability) will be also studied and taken into account. Data analytics and machine/deep learning techniques will be also used to simulate the model and interpret the data, enabling comparison of the biofilm behaviour in various conditions.
For this project, machine/deep learning techniques and multi-objective optimisation algorithms will be used in combination with biomedical modelling. Complement Genomics Ltd will provide microbiomic data sets for use in this project and which are of relevance to the human condition.
Applicants should use the links provided in each topic or project area to the Research Centres and Research Groups identified, or to the named supervisors for each project, to ensure that their application and proposal fits with the research interests and topics defined in the studentships offered.
This PhD studentship opportunity is offered through the Intensive Industrial Innovation Programme (IIIP). The IIIP Programme is a collaboration between Northumbria, Durham, Newcastle and Teesside Universities, receiving up to £2,202,411 of funding from the England European Regional Development Fund (ERDF) as part of the European Structural and Investment Funds Growth Programme 2014-2020. The Ministry of Housing, Communities and Local Government is the Managing Authority for ERDF. Established by the European Union, ERDF funds help local areas stimulate their economic development by investing in projects which will support innovation, businesses, create jobs and local community regenerations.
Funding eligibility
Applicants should hold or expect to obtain a relevant degree at 2.1 minimum, or an equivalent overseas degree in Bioinformatics, Mathematics, Engineering, Physics, Computer Science or a closely related subject. Good programming skills are desired. Previous experience in general mathematical or biomedical modelling is also desired, but not essential. International students would be subject to the standard entry criteria relating to Tier 4 visa procedures English language ability and ATAS certification.
Entry requirements
Applications are welcome from strong UK, EU and International students. The studentship covers tuition fees at the Home/EU rate for three years and provides an annual tax-free stipend of £15,000 p.a. for three years, subject to satisfactory progress. Non-EU International students will be required to pay the difference between the Home/EU and International fee rate.
For more information click "LINK TO ORIGINAL" below.
This opportunity has expired. It was originally published here:
https://www.tees.ac.uk/sections/research/funding_details.cfm?fundingID=40