The QMEE CDT Project proposal database

Welcome to the QMEE CDT Project proposal database. This is a live list of projects proposals put forward by PIs across the CDT partner institutions

PIs/Supervisors will continue to add projects to this list over the next few months, so do keep checking back! You can search the projects using the box below: simply enter some text and press Search to do a text search across all the database fields. If you want to search more finely, the search tool also allows you to search on particular details of the project descriptions: you will see these finer search options appear if you click on the search box.

Click on the view button next to a project to get the full proposal description. If you want to download project details, either for all projects, or for a subset you have searched for, then click on the 'Download details' button.

Microbial networks, communities, niches and ecosystem function
Microbial communities are omnipresent, hyper-diverse, and are fundamental for the functioning of all ecosystems. However, methods for tracking microbial communities have only recently been developed. We have conducted experiments that manipulate microbial community structure to understand how changes to the communities affect ecosystem functioning. The project will make use of these data, consisting of thousands of taxa across hundreds of sites, to infer microbial food webs and interaction networks. The aims will be to identify keystone species and functional guilds, and to use the food webs to predict how ecosystem functioning will be affected by the removal or addition of species. The project will involve mathematical and computational work in machine learning and network analysis. Specically, we will use methods from Sparse Bayesian Learning, Geometric Graphs and Manifold Learning to extract graph representations from data. The networks will then be analysed using graph partitioning, role identi cation and community detection
Tom Bell
Mauricio Barahona
Development of mathematical theory, Computing, Quantitative data analysis, Ecological observations / data collection
Mauricio Barahona
The student will develop and implement algorithms for detecting functional groups using machine learning and network analysis.
Bacteria are vital components of ecosystems, but are too diverse to understand on a species-by-species basis. There is a great need to therefore develop quantitative methods for identifying functional groups within communities in order to simplify the complexity of these systems.
Identification of the fundamental linkages between the structure and function of microbial communities.
Industrial applications include e.g. identification of important functional groups for degrading sewage or for bioremediation of pollutants. For example, the methods could identify the group of bacteria responsible for degrading a particular pollutant.
Quantitative methods are transforming the eld of environmental microbiology. Integration and development of network analysis methods from analysis of networks are often the only way of unravelling the
The project will apply the network approaches developed by the Barahona group for looking at complex systems (e.g. protein interactions, gene networks) with the data being generated by the Bell group looking linking bacterial communities with ecosystem functioning.
Environmental microbiology, Environmental genomics, Ecological/Evolutionary tools, technology & methods
Training in quantitative methods (network analysis and machine learning) will be provided by postdocs in the Barahona group. Laboratory training in molecular methods (next gen sequencing, bioinformatics pipelines, handling of bacterial cultures) will be provided by postdocs in the Bell lab as required to complement the quantitative work.
South Kensington and Silwood Park
2017-10-02 12:45:59