Job opportunity: two-month postdoctoral position in mathematical modelling / inference

Research Associate/ Fellow

Closing Date
Friday, 8th February 2013
Job Type
Research & Teaching
School of Biosciences – Division of Agricultural & Environmental Science, Multidisciplinary Centre for Integrative Biology
£24766 to £29541 per annum depending on skills and experience, minimum £27,854 per annum with relevant PhD.

This full-time post is available on a fixed term contract for a period of two months.

Applications are invited to join a highly motivated multi-disciplinary team of research scientists working the Universities of Nottingham and Birmingham. The successful candidate will join a jointly funded project to carry out modeling of occludin trafficking during epithelial polarization and wound healing. The post could be located either in the School of Biosciences at the University of Nottingham’s Sutton Bonington Campus, or at the School of Biosciences at the University of Birmingham.

The work will include (i) developing a mathematical models (using ODEs) to describe the turnover of occludin protein in the cell as well as the kinetic trafficking of occludin between cellular compartments; (ii) to estimate model parameter values from experimentally derived data using Monte Carlo Markov Chain approaches; and (iii) to iteratively improve the model, with cycles of model and data comparison, in order to provide greater certainty about the important mechanisms involved that can explain the experimental data. Other duties will include contributing to publication of this research in peer-reviewed journals, contributing to writing of research grant applications, and generally collaborating between disciplines and institutions.

The successful candidate must have a PhD or equivalent in mathematical modelling or statistics or a related area. Research experience within a mathematical biology or systems biology research area would be desirable but not essential. Candidates must to be able to demonstrate excellent mathematical ability, especially in the areas of ordinary differential equations and statistical analysis of data; experience of application of these skills to biological research would be desirable. Candidates must also be able to evidence excellent computing skills in a suitable environment (e.g. R or Matlab). Excellent English language oral and written communication skills are also essential. This post will require the person appointed to be able to work independently and as part of a multi-disciplinary team, to be motivated, flexible and willing to learn.

Full details, including how to apply, can be found on the University of Nottingham’s vacancy system.

Informal enquiries may be addressed to Dr Dov Stekel, email: or Dr Josh Rappoport, email:


New Publication: Inferring the Brassica rapa interactome using protein–protein interaction data from Arabidopsis thaliana

Happy New Year to my blog readers and followers. We have had a good start to the year with a new publication! The lead author, Jianhua Yang, is employed on the EU MEIOSYS grant with which I am involved; our main input is through contributions from Mudassar.

Yang, J., Osman, K., Iqbal, M., Stekel, D.J., Luo, Z., Armstrong, S.J. and Franklin, F.C.H. 2013. Inferring the Brassica rapa interactome using protein–protein interaction data from Arabidopsis thaliana. Frontiers in Plant Science 3:297.


Following successful completion of the Brassica rapa sequencing project, the next step is to investigate functions of individual genes/proteins. For Arabidopsis thaliana, large amounts of protein–protein interaction (PPI) data are available from the major PPI databases (DBs). It is known that Brassica crop species are closely related to A. thaliana. This provides an opportunity to infer the B. rapa interactome using PPI data available from A. thaliana. In this paper, we present an inferred B. rapa interactome that is based on the A. thaliana PPI data from two resources: (i) A. thaliana PPI data from three major DBs, BioGRID, IntAct, and TAIR. (ii) ortholog-based A. thaliana PPI predictions. Linking between B. rapa and A. thaliana was accomplished in three complementary ways: (i) ortholog predictions, (ii) identification of gene duplication based on synteny and collinearity, and (iii) BLAST sequence similarity search. A complementary approach was also applied, which used known/predicted domain–domain interaction data. Specifically, since the two species are closely related, we used PPI data from A. thaliana to predict interacting domains that might be conserved between the two species. The predicted interactome was investigated for the component that contains known A. thaliana meiotic proteins to demonstrate its usability.