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

Closing date for applications: 31st July 2016

Informal enquiries to Dr Jon Hobman ( )

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


Some thoughts on Thomas et al. 2012. Directional Migration of Recirculating Lymphocytes through Lymph Nodes via Random Walks.

This article [1] was published just over 4 months ago by Benny Chain’s laboratory in UCL, based on work carried out for Niclas Thomas’s PhD. It is a rare privilege to read an article that so clearly relates itself to work that I carried out – indeed work carried out during my own PhD. Therefore I have decided to post some thoughts on this (and related) articles, which I will also post on the PLoS ONE web site.

Thomas et al.’s work contains new data on the distribution of lymphocyte transit times, together with rigorous fitting of mathematical models to their data. Importantly, they show that their data can be fitted by a random walk model that allows for motion orthogonal to the main direction of motion (i.e. through the lymph node tissue). This random walk model, although implemented in one dimension, is intended to reflect a three dimensional motion in which the cells move either along the main axis of motion, or in dimensions perpendicular to it.

There were two models for lymphocyte recirculation that I proposed for my PhD, both of which were implemented in a one dimensional domain (along the lymph node). The first was a convection-diffusion model [2], which could be thought of as a biased random walk, although could be sufficiently general to include other mechanisms. The second was a model that proposed that lymphocyte migration was halted due to encounters with dendritic or other cell types [3, 4]. Both models could explain the available data on lymphocyte recirculation transit times.

Following my own PhD, two photon microscopy technology developed to the point where the motion of individual lymphocytes could be tracked [5, 6, 7]. This has led to the discovery that lymphocyte migration is essentially random, and that the hypothesis I set forward in [3, 4] is false. Thomas et al.’s work, following work by Textor et al. [7], has shown that indeed the distribution of lymphocyte recirculation times can be explained well by a three-dimensional diffusion model.

So, how do I feel to have had some of my research empirically falsified? Well, actually, it makes me rather happy! Now don’t get me wrong: I would much rather that the two photon microscopy had shown T cells moving along the lymph nodes, stopping at dendritic cells for a while, and then moving along again, as my work of [3, 4] proposed. That is not the case. I take solace in two things. First, the convection-diffusion model of [2] is essentially correct in its most naive form. But more significantly, I can hear Karl Popper cheering me on from the side-lines: my hypotheses were good science, even if incorrect. The important point to learn from [2, 3, 4] is that the observed distribution of lymphocyte transit times is non-trivial: it demands a mechanistic explanation, and, at that time, no such explanations had been proposed. The dendritic cell hypothesis was perfectly plausible (in fact, I came up with it through discussions with Benny Chain and David Katz, with whom Benny Chain shared a lab) – and worthy of experimental testing. (Parenthetically, this was in the bad old days of “mathematical biology”, when theoreticians generally worked independently of experimentalists. I remember once, at that time, a theoretician proudly stating at a conference that no experimental data could falsify their model. Thankfully things are much better today under the “systems biology” paradigm, and Thomas et al’s article is an excellent example of experiment and theory working so well together).

To be honest, the thing that annoys me a little is that I didn’t think to check myself, during my PhD, whether three dimensional diffusion could explain the distribution of recirculation times (and full credit to Niclas Thomas and others for investigating this!). At the time, I was unhappy with the value of the one-dimensional diffusion coefficient that my first model needed to fit the data: based on a Brownian motion calculation for T-cell diffusivity, I thought that it was two orders too fast to be realistic as random motion, and so looked to other explanations. Indeed, this was discussed at my PhD viva, and my examiners (an eminent modelling-friendly immunologist and an eminent mathematical biologist) made me correct my thesis to include the Brownian motion calculation. But the assumptions behind the calculation were completely wrong, for reasons that my examiners and I should have known. First, on biological grounds, the calculations were based on Brownian motion of T-cells – when of course we knew that T-cells move actively – and even then there was some data available on speed of such movement (e.g. from Tim Springer’s lab). Second, the calculations were based on a 1-D diffusion – when of course we know that 3D diffusion is qualitatively and quantitatively different (e.g. the recurrence / transience of 1D / 3D random walks taught in undergraduate Markov chain courses). I had even written a 3D diffusion simulator (for another part of my PhD) which could have easily been used to test the hypothesis. Hind-sight is a wonderful thing!

All-in-all, I congratulate Niclas Thomas on this work. I think it is wonderful and enhances our knowledge of this extremely important field.

1. Thomas N, Matejovicova L, Srikusalanukul W, Shawe-Taylor J, Chain B. 2012. Directional Migration of Recirculating Lymphocytes through Lymph Nodes via Random Walks. PLoS ONE 7(9): e45262.

2. Stekel, D.J., Parker, C.E. and Nowak, M.A. 1997. A model of lymphocyte recirculation. Immunology Today, 18:216-21.

3. Stekel, D.J. 1997. The role of inter-cellular adhesion in the recirculation of T lymphocytes. Journal of Theoretical Biology, 186:491-501.

4. Stekel, D.J. 1998. The simulation of density-dependent effects in the recirculation of T lymphocytes. Scandinavian journal of immunology47:426-30.

5. Miller MJ, Hejazi AS, Wei SH, Cahalan MD, Parker I. 2004. T cell repertoire scanning is promoted by dynamic dendritic cell behavior and random t cell motility in the lymph node. Proceedings of the National Academy of Sciences of the United States of America 101: 998-1003.

6. Beltman JB, Mare AFM, Lynch JN, Miller MJ, de Boer RJ. 2007. Lymph node topology dictates t cell migration behavior. The Journal of Experimental Medicine 204: 771–780.

7. Textor J, Peixoto A, Henrickson SE, Sinn M, von Andrian UH, Westermann, J. 2011. Defining the quantitative limits of intravital two-photon lymphocyte tracking. Proceedings of the National Academy of Sciences of the United States of America 108: 12401–6.

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

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.