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.

Tracking ecological and evolutionary dynamics in diverse biological communities
All animals, plants and microbes live in diverse assemblages of many hundreds of species. Understanding how these diverse communities respond to environmental change is a key challenge for ecology and evolution. An important question is whether species interactions play a major role in shaping responses. Under the ‘Court Jester’ paradigm, species respond independently to environmental changes, either ecologically by changes in abundance or dispersal or evolutionarily by adapting genetically to new conditions. Under the alternative ‘Red Queen’ paradigm, ecological interactions lead to ecological and co-evolutionary feedbacks that spread through the network of interacting species. The biotic environment of interacting species therefore exerts stronger pressures than the physical environment. Despite considerable interest through mathematical modelling and some empirical studies of the fossil record, we still lack general insight into how real communities respond to change, and where they lie along the ‘Court Jester’ to ‘Red Queen’ continuum. As a result, it is hard to understand or predict the responses of real communities to a given change. New data, theory and analytical methods are required for mechanistic understanding. This project will tackle these questions in microbial communities. Microbes underpin many processes that human populations depend upon, including nutrient cycling, decomposition and healthy gut functioning. In each case, a collective of many hundreds of species performs these key functions, rather than single focal taxa. Thanks to an explosion of data from genome sequencing technology and metabolomics, it is now possible to track species composition and resource use across communities containing hundreds of species – at an unprecedented level of detail. Furthermore, the rapid lifestyle and small scales of microbes permits observation of long time-series (1000s of generations) and experimental perturbation of whole communities. We still lack tools, however, to infer species interactions, to measure evolution and to predict dynamics and functioning. The student will explore new methods for estimating species interactions and evolution in whole communities with hundreds of species. By developing new theory and computer simulations, methods will be validated first under ‘controlled’ conditions, before applying them to real datasets. Data will be assimilated from online databases, with the option to collect new data and to seek out stakeholder engagement and real-world applications depending on the student’s interests. The results will be synthesised to identify conditions under which external environment versus biotic environment drive whole-community dynamics – answering the long-standing evolutionary questions outlined above. The project is perfect for a student with existing computing and/or mathematical skills (either from a biological or non-biological background), who is keen to bridge theory, methods and data, and to learn multi-disciplinary approaches in the QMEE area.
Tim Barraclough
Mauricio Barahona
Development of mathematical theory, Computing, Ecological observations / data collection, Evolutionary observations / data collection
Mauricio Barahona
Mechanistic models using ordinary differential equations, and efficient simulation for 100s of species. New methods for interaction networks based on graph-theoretical methods for geometric dimensionality reduction, sparsification techniques and Hierarchical Causal Structure Identification. Bioinformatic analyses of genomic data to infer resource use of species.
New methods for inferring interactions and evolution from whole-community: current work rely on a single species or laborious pairwise experiments. Maths methods for other data (e.g. gene regulation) provide a start for new methods linked to time-series of perturbed microbial communities. Applies to other biological data, e.g. resolved fossil records or long-term ecological field experiments.
How do communities respond to environmental change and biotic selection pressures exerted by other species? New theory to predict evolution in whole communities by building metabolic models of bacteria growing on chemical resources connected by a metabolic pathway. Links genome evidence for resource use to ecological interactions and selection pressures acting on use of different resources.
After developing general theory and methods, the project will seek stakeholder input to develop a specific application. This could entail waste-water treatment, where predicting and maintaining functioning of sewage treatment (performed by an unruly set of hundreds of microbial species) is a key challenge. This part will be tailored to fit the interests of the student.
It will provide a suite of methods for analysing metagenomic data that will transform our ability to understand and predict how microbial communities adapt to changing conditions. This will have broad applicability to environmental microbiology, and in other disciplines such as medical and human health.
The project combines expertise in ecological and evolutionary modelling, microbial ecology and genomic analysis in Life Sciences (Barraclough) with advanced mathematical methods for analysing interaction networks from Dept Mathematics (Barahona). The student will learn advanced mathematical techniques and computation as well as theory and data handling for microbial ecology and evolution.
Population genetics and evolution, Environmental microbiology, Ecological/Evolutionary tools, technology & methods
Maths: a mini-project with Barahona, group meetings, maths seminars, and MSc Applied Mathematics courses e.g. Mathematical Biology. Microbial ecology and evolution: a mini-project with Barraclough analysing a prior dataset, courses on the MSc course in Ecology, Evolution and Conservation. These plans will be adapted depending on the skill-set of the successful candidate.
Department of Mathematics, Department of Life Sciences, Imperial College London
No
2017-10-01 16:58:18