PhD opportunity: Tunable zinc responsive bacterial promoters for controlled gene expression

 

Tunable zinc responsive bacterial promoters for controlled gene expression

Supervisory Team: Dr Jon Hobman (School of Biosciences), Dr Phil Hill (School of Biosciences), Dr Dov Stekel (School of Biosciences).

Applications are invited for this 4-year PhD project which is part of a University-funded Doctoral Training Programme (DTP) in Synthetic Biology and associated with Nottingham’s new BBSRC/EPSRC Synthetic Biology Research Centre. Students will benefit from a diverse range of training opportunities, including specialist workshops, lectures and seminars, as well as participation in Nottingham’s yearly BBSRC DTP Spring School event.

Zinc is an essential metal, required in ~30% of bacterial proteins, but is toxic at higher intracellular concentrations. Bacteria such as E. coli have evolved sophisticated zinc import and export systems controlled by transcription factors that repress the expression of genes encoding importer proteins (regulator Zur) or activate expression of zinc efflux (regulator ZntR). These regulators and the promoters they control represent a good example of fine tuning of cellular response to external zinc concentrations (1) and different Zur and ZntR regulated promoters have different affinities and transcription levels. The aim of this PhD will be to study the levels of expression from engineered Zur and ZntR regulated promoters in response to zinc, so that a suite of promoters can be used to finely control gene expression in response to zinc levels in growth media. These promoters will be used to control gene expression in engineered bacteria using cheap zinc inducers and zinc chelators, and will allow tuned expression of industrially useful synthetic pathways in E. coli and other Gram-negative bacteria. These tunable promoters could have potential impact in a range of biotechnology/biosynthesis contexts.

The project is available from 1st October 2016 and is open to UK and EU students with a 2(i) degree or above in microbiology, genetics, biochemistry, or a related discipline. The work will be based at the School of Biosciences in Nottingham.

The supervision team for this project is multi-disciplinary, enabling training in a wide-range of subjects and techniques in microbiology, molecular biology, cell engineering, reporter gene systems, mathematical modelling, data analysis, and cell metabolism.

Applicants should submit a covering letter, CV and the names of two academic referees addressed to: Rob Johnston School Administrator Robert.Johnston@nottingham.ac.uk

Closing date for applications: 31st July 2016

Informal enquiries to Dr Jon Hobman ( Jon.Hobman@nottingham.co.uk )

(1)       Takahashi et al (2015). Journal of the Royal Society Interface 12: 20150069

 

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Welcome to Anna Swan, Mengyuan Yu and Sophie Patel

Three new people have joined the laboratory recently.

Anna Swan is in the final year of her PhD. Anna writes of herself:

I am currently a PhD student, having started in 2010, working on the bioinformatics analysis of omics data from models of articular cartilage with the aim to identify biomarkers for Osteoarthritis. I have focussed largely on the analysis of proteomics data, generated by mass spectrometry, and the application of machine learning to such data. Machine learning, particularly rule-based methods, have been used for both classification of samples and identification of putative biomarkers. I am funded by a BBSRC Case studentship and Waltham Centre for Pet Nutrition (Mars).

Prior to starting my PhD, I completed a BSc in Biological Sciences at the University of Reading (2009) and an MSc in Applied Bioinformatics at Cranfield University (2010). I also spent some time working at Genetix (now Molecular Sciences) as a laboratory scientist, working in mammalian cell culture and on the development of a cell imaging platform.

Mengyuan Yu and Sophie Patel join the lab as undergraduate project students. Both Mengyuan and Sophie are final year students studying Environmental Science. Mengyuan will be modelling production and flow of nitrates in dairy slurry, making use of the dairy farm at Sutton Bonington. Sophie will be analyzing camera trap data from the Malaysian rain forest, based on data she has captured from a summer project with Ahimsa Campos-Arceiz at the University of Nottingham, Malaysia.

PhD opportunities at the University of Nottingham

The University of Nottingham and the Rothamsted Research Institute are now advertising for 42 fully funded four-year PhD places in their Doctoral Training Partnership. For applicants with a maths, physics or computing background interested in mathematical / computational biology, there are opportunities in all three themes to become involved in world-leading bioscience research. There are three projects on which I would be a second / third supervisor.

  1. Bayesian Inference for Dynamical Systems: From Parameter Estimation to Experimental Design with Theodore Kypraios (maths) as main supervisor. This project will be entirely mathematical / computational.
  2. The role of a novel zinc uptake system (C1265-7) in uropathogenic E. coli, with Jon Hobman as main supervisor. This project will be mostly experimental, but could involve a mathematical modelling component should the student be interested.
  3. Tunable zinc responsive bacterial promoters for controlled gene expression in E. coli, with Phil Hill as main supervisor. This project will be mostly experimental, but could involve a mathematical modelling component should the student be interested.

For more information, please visit the advert site on findaphd.com

Matthias speaking at Computational Biology and Innovation PhD Symposium, Dublin

Today sees the start of the Computational Biology and Innovation PhD Symposium at University College, Dublin. Matthias Gerstgrasser will be giving a presentation in tomorrow’s (Wednesday’s) session.

Title and abstract are:

Parallelising Sequential Metropolis-Hastings: Implementing MCMC in multi-core and GPGPU environments.

Markov Chain Monte Carlo (MCMC) techniques have become popular in recent years to efficiently calculate complex posterior distributions in Bayesian statistics. In computational biology, these methods have a wide range of applications, and in particular lend themselves to parameter estimation in models of complex biological systems. The Metropolis-Hastings algorithm is one widely used routine in this context. (1)

Our research focuses on employing the computational power provided by multi-core CPUs and general-purpose graphics processing units (GPGPUs) to provide a speedup to the operation of this algorithm. Both multi-core and GPGPU architectures offer vast computing power compared to traditional single-core environments, but tapping into these resources presents additional complexities. Yet current computer systems rely increasingly on increasing core count rather than performance per core to provide improvements in computing power, a trend that is almost certain to continue in the future. While (2) provides a GPGPU algorithm applicable to Independent Metropolis-Hastings (IMH), a parallel implementation of general  MH instances has proven difficult due to the inherently sequential nature of this algorithm. In our own research, we are investigating possible speedups in automated model fitting and parameter estimation in large phenotype arrays of brewer’s yeast and other microorganisms. Our findings, however, would be equally applicable to other problems in systems biology.

We show how for some types of target distributions we can leverage independence in the structure of these distributions in order to partially parallelise the running of the MH algorithm. We furthermore discuss how this approach can be implemented efficiently on both multi-core CPUs as well as in GPGPU environments. In both cases we divide the workload of computing the acceptance probability in the MH algorithm’s main loop among several threads. Furthermore, we replicate the remaining instructions of the loop among these threads as well in order to minimise overhead incurred by thread creation, synchronisation and deletion. More importantly, in GPGPU environments this modification greatly decreases data transfers between GPU and main memory. Both our implementations show a significant speedup over a single-threaded classical MH algorithm for computationally expensive target distributions. We discuss limitations of these implementations and necessary conditions for them to provide a measurable speedup over single-threaded implementations. 

In conclusion we compare the performance of parallelising a single instance of the MH algorithm compared to running several instances in parallel on either a multi-core CPU or in a GPGPU environment. The latter approach is particularly applicable to the common situation of estimating e.g. parameters from a number of distinct, but similar, experiments. We show how GPGPU computing can be used in these situations to provide an even greater speedup compared to single-threaded implementations. 

1. Wilkinson, D J. Stochastic Modelling for Systems Biology, 2006.
2. Jacob, P, Robert CP, Murray HS. 2011; arXiv:1010.1595v3.