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PhD in "Network inference using Pathogen Sequence Data and Phylodynamics for Livestock Diseases"

Many important human and animal pathogenic viruses and bacteria have genomes which mutate rapidly contributing to their clinical importance but also providing opportunities for understanding transmission [1]. By tracking where, when and in what species particular mutations or genomic re-arrangements arise, there is potential to infer transmission patterns between groups of individuals, e.g. patterns of farm to farm transmissions; global transmission patterns between countries (mediated by trade or travel patterns); or transmission between species or wild and domestic animals.

Pathogen sequencing studies are generating large amounts of genomic data as part of national and international disease surveillance efforts, and the challenge is to develop robust analysis and prediction methods to answer questions such as what are the routes of infection?; what are the rates of cross species transmissions?; and under what conditions might new strains arise?

In this project sequence data will be combined with other data types to infer the spread of pathogens over networks or multiplexes, and conversely the extent that the underlying properties of the network can be revealed from an observed transmission pattern will be investigated. Systems of interest include transmission patterns of avian influenza through migration of wild birds vs trading patterns [2], and transmission of cattle pathogens via known movements vs spatial diffusion or unknown (or under-sampled) wildlife vectors.

The student will develop skills in computational biology and data science, particularly stochastic simulation modelling, Bayesian phylogenetics/phylodynamics [1,3], network analysis and inference, and fast approximate estimation techniques, under the supervision of Dr Lycett (phylodynamics) and Dr Bronsvoort (livestock epidemiology).

This 3 year PhD project would suit someone with a masters degree (or research experience) in a quantitative/computational subject (e.g. physics, maths, computer science, bioinformatics, systems biology, epidemiology, economics).

Eligibility: All candidates should have or expect to have a minimum of an appropriate upper 2nd class degree and meet all PhD entry requirements.

This studentship will cover Home/EU tuition fees and provides an annual stipend.

References:

  1. RR Kao, DT Haydon, SJ Lycett, PR Murcia (2014) “Supersize me: how whole-genome sequencing and big data are transforming epidemiology” Trends in Microbiology Vol: 22 Pages: 282-91
  2. L Lu, SJ Lycett, AJ Leigh Brown (2014) “Determining the Phylogenetic and Phylogeographic Origin of Highly Pathogenic Avian Influenza (H7N3) in Mexico” PLoS One. 9, 9, e107330
  3. Frost, S.D.W., et al., Eight challenges in phylodynamic inference. Epidemics (2014), http://dx.doi.org/10.1016/j.epidem.2014.09.001

Application Procedure Applications including a full CV with names and addresses (including email addresses) of two academic referees, should be emailed to: RDSVS.PGR.Admin@ed.ac.uk.

When applying for the studentship please state clearly the title of the studentship and the supervisors in your covering letter.

The closing date for applications to this studentship is 06 February 2015.

CountryUK
LabThe Roslin Institute, University of Edinburgh
Starts on21/09/2015
Duration/Period3 years
Deadline06/02/2015 0:00
Linkhttp://www.roslin.ed.ac.uk/postgraduate/studentships/network-inference-using-pathogen-sequence-data-and-phylodynamics-for-livestock-diseases/
Keywordsepidemiology; inference; phylodynamics; phylogenetics;

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