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Details of Grant 

EPSRC Reference: EP/K026003/1
Title: Sequence data and the ecology of pathogens: phylogeny and beyond
Principal Investigator: Colijn, Dr C
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department: Mathematics
Organisation: Imperial College London
Scheme: EPSRC Fellowship
Starts: 01 October 2013 Ends: 31 July 2018 Value (£): 1,002,244
EPSRC Research Topic Classifications:
Statistics & Appl. Probability
EPSRC Industrial Sector Classifications:
Related Grants:
Panel History:
Panel DatePanel NameOutcome
24 Jul 2013 EPSRC Mathematics Fellowships Interviews - July 2013 Announced
12 Jun 2013 Mathematics Prioritisation Panel Meeting June 2013 Announced
Summary on Grant Application Form
This proposal aims to improve our ability to infer the ecological processes shaping a pathogen's evolution by understanding pathogen phylogenies in a novel way. It is important to understand the ecology of pathogen spread. Indeed, ecological ideas have much to offer in understanding pathogens in particular: ecologists are accustomed to complex datasets without the opportunity for truly controlled experiments, and ecological concepts such as competition and competitive exclusion, niche adaptation, and habitat filtering are increasingly the paradigm of choice for understanding pathogen evolution. An example of a question for which ecological ideas are particularly relevant is that of how and why some pathogens evolve widespread drug resistance rapidly while others maintain long-term coexistence of resistant and sensitive strains.

Pathogen phylogenies contain a lot of information, in principle, both about the specifics of where certain strains or sequences originate and about the general underlying processes shaping when, where, and which pathogen strains are able to spread. Mathematicians have developed tools to create maximum-likelihood phylogenetic trees representing the estimated ancestral patterns of a dataset, as well as tools to simultaneously infer a phylogeny and the population's demographic history. However, existing tools offer no systematic approaches to infer the ecological context shaping a pathogen's spread or evolution. In addition, current methodologies use very limited metrics to assess phylogenies, so to the extent that approaches do exist to use genetic data to understand epidemiological and ecological processes, these are based on very little of the rich information in genetic data.

The work proposed here aims to fill the gap between the rich datasets of pathogen genomes being gathered and our ability to analyse them. I will first develop a suite of ways to summarise phylogenetic trees, taking the topology of the tree into account. Existing methods identify the probability of a tree with the probabilities of its branching times, neglecting the tree's topology. Developing informative measures is likely to be challenging because of the other factors that affect trees (stochastic transmission and mutation, selection, and others). For each new summary I aim to find its distribution on random trees drawn uniformly from tree space, to determine how rare a given tree is. In the second stage of the work I aim to improve inference of the underlying ecological processes shaping pathogen evolution, by better understanding what features of phylogenetic trees (including the novel summary measures developed in the first stage) make them able to account for observed data. Inference of ecological processes from phylogenies will carry some of the same challenges that occur in the inference of population demographics, one of these being that the number of possible trees on n leaves is too high for summing over all such trees to be feasible. Yet such sums are at the heart of likelihood-based inference. I propose to use the features identified in the first stage of the proposal, together with an improved understanding of how the likelihood of the data D given a tree G, L(D|G), is distributed over tree space, to simplify the sum and improve inference.
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Organisation Website: http://www.imperial.ac.uk