PhD studentships

There are currently 2 Systems Biology PhD studentships available (see below). For both projects, we are seeking ambitious individuals with mathematical knowledge to degree level and good programming skills. Experience in wet lab work would be a bonus, as would an MSc in Computational/Mathematical/Systems Biology. The training received in a cross-disiplinary, research-intensive environment will ensure that the successful applicants are uniquely placed to take advantage of the huge opportunities that exist, and will continue to do so, in quantitative biology.

Design and construction of a tuneable synthetic amplifier

A key challenge of synthetic biology is the design of biological circuitry that can reliably reproduce the response characteristics of equivalent electronic networks. Feedback amplifiers are an important building block for a vast array of electronic devices. In this project we will combine mathematical modelling and experimental work to construct a tuneable, synthetic feedback amplifier based upon a two-component signalling pathway. The circuit will constitute a novel synthetic biological part capable of providing graded signal amplification, with a broad range of potential application areas including controlled drug delivery. Key objectives of the project include:

  1. In silico circuit design – Iterative construction of viable amplifier architectures using quantitative mathematical models and experimental measurement of circuit performance. To ensure robustness of design, models will incorporate noise effects associated with factors such as cell-to-cell variability in protein copy number and external environmental fluctuations.
  2. Identifying critical points of regulation – The in silico models will be analysed to quantify the range of achievable amplifiers and the biochemical parameters predicted to have the greatest effect on amplifier gain. This computational analysis will be complemented by the use of directed in vivo evolution to select for circuits possessing tuneable gain. This integrated approach will identify the kinetic parameters (e.g in vivo half-life and dephosphorylation rates) that are most amenable to experimental manipulation.

This interdisciplinary project will combine the expertise of the Porter group (signal transduction) and Akman group (theoretical biology) at the University of Exeter. Techniques used will include deterministic and stochastic mathematical modelling, fluorescence microscopy, protein purification, and biochemical assay.

Applications

Applications can be submitted online.
The deadline for applications is the 24th February 2012. For informal discussion contact either Dr. Steve Porter or Dr. Ozgur Akman (O.E.Akman@ex.ac.uk) for further information.

 Dynamic inference of large biochemical networks - uncovering transcriptional circuits in the yeast Saccharomyces cerevisae

Mathematical modelling is becoming an increasingly important tool in the biosciences, providing a quantitative framework within which to interpret experimental results and uncover the design principles of biological circuits. This project will develop novel computational methods for inferring the dynamic relationships underlying transcriptional responses using a modelling method known as Hidden Variable Dynamic Modelling (HVDM). This will be applied to analyse the transcriptional circuitry that controls environmental responses in Saccharomyces cerevisiae, for which there exists a wealth of information on environmental responses, transcriptional control and the regulation of gene expression.

Previous studies have demonstrated that HVDM can successfully identify transcription factor targets in large microarray data sets, matching the performance of traditional methods based on clustering gene expression profiles. Moreover, by explicitly assuming a simple generic model for transcriptional activation, HVDM also generates confidence intervals for kinetic parameters and the predicted gene expression profiles. In its current form, however, the method only functions optimally for groups of genes regulated by the same transcription factor, which is a serious limitation. In this project the student will:

  1. Broaden the scope of HVDM modelling by incorporating multiple transcription factors.
  2. Improve the computational efficiency of the HVDM algorithm by using nested sampling as the parameter inference method.
  3. Modify the HVDM model(s) to cover a broader range of regulatory mechanisms, and to include other data sources such as transcriptional motifs.
  4. Use HVDM to generate predictive models of the response of S. cerevisiae to combinatorially applied environmental stressors.
  5. Test the predictions of the HVDM models experimentally.

The student will work jointly in the Haynes and Akman groups at the University of Exeter and will gain expertise in algorithm development, implementation and optimisation; mathematical modelling of biological systems and experimental biology.

Applications

Applications can be submitted online.
The deadline for applications is the 29th February 2012. Informal enquiries should be made to Prof. Ken Haynes or Dr. Ozgur Akman.

Related PhDs

You can also see ful lists of related PhDs: