New Publication: Reconstructing promoter activity from Lux bioluminescent reporters

Absolutely delighted to report that our paper has been published:

Iqbal M, Doherty N, Page AML, Qazi SNA, Ajmera I,  Lund PA, Kyraios T, Scott DJ, Hill PJ and Stekel DJ (2017) Reconstructing promoter activity from Lux bioluminescent reporters. PLOS Computational Biology 13(9): e1005731. https://doi.org/10.1371/journal.pcbi.1005731.

Abstract

The bacterial Lux system is used as a gene expression reporter. It is fast, sensitive and non-destructive, enabling high frequency measurements. Originally developed for bacterial cells, it has also been adapted for eukaryotic cells, and can be used for whole cell biosensors, or in real time with live animals without the need for euthanasia. However, correct interpretation of bioluminescent data is limited: the bioluminescence is different from gene expression because of nonlinear molecular and enzyme dynamics of the Lux system. We have developed a computational approach that, for the first time, allows users of Lux assays to infer gene transcription levels from the light output. This approach is based upon a new mathematical model for Lux activity, that includes the actions of LuxAB, LuxEC and Fre, with improved mechanisms for all reactions, as well as synthesis and turn-over of Lux proteins. The model is calibrated with new experimental data for the LuxAB and Fre reactions from Photorhabdus luminescens—the source of modern Lux reporters—while literature data has been used for LuxEC. Importantly, the data show clear evidence for previously unreported product inhibition for the LuxAB reaction. Model simulations show that predicted bioluminescent profiles can be very different from changes in gene expression, with transient peaks of light output, very similar to light output seen in some experimental data sets. By incorporating the calibrated model into a Bayesian inference scheme, we can reverse engineer promoter activity from the bioluminescence. We show examples where a decrease in bioluminescence would be better interpreted as a switching off of the promoter, or where an increase in bioluminescence would be better interpreted as a longer period of gene expression. This approach could benefit all users of Lux technology.

Author summary

Bioluminescent reporters are used in many areas of biology as fast, sensitive and non-destructive measures of gene expression. They have been developed for bacteria, adapted now for other kinds of organisms, and recently been used for whole cell biosensors, and for real-time live animal models for infection without the need for euthanasia. However, users of Lux technologies rely on the light output being similar to the gene expression they wish to measure. We show that this is not the case. Rather, there is a nonlinear relationship between the two: light output can be misleading and so limits the way that such data can be interpreted. We have developed a new computational method that, for the first time, allows users of Lux reporters to infer accurate gene transcription levels from bioluminescent data. We show examples where a small decrease in light would be better interpreted as promoter being switched off, or where an increase in light would be better interpreted as promoter activity for a longer time.

 

Thanks to all my brilliant collaborators and coauthors. Thanks also to the lovely referees (one of whom signed their review) who said of the article: “an extremely important contribution to the field” (Reviewer 1) and “a significant advance” (Reviewer 2) and  provided helpful and constructive feedback.

 

 

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Now recruiting: Research Associate/Fellow in Antimicrobial Resistance Modelling

We are now recruiting the mathematical modelling post-doc for the EVAL-FARMS project. This post will work with me, Theo Kypraios in Maths, and the EVAL-FARMS team more generally, developing mathematical models for risk of emergence of AMR pathogens in agricultural waste, using all the exciting data that are being generated by the empirical researchers on the grant. Details of the advert, as well as links to it, are:

Research Associate/Fellow in Antimicrobial Resistance Modelling

Agricultural & Environmental Sciences

Location:  Sutton Bonington
Salary:  £26,052 to £38,183 per annum, depending on skills and experience (minimum £29301 with relevant PhD). Salary progression beyond this scale is subject to performance
Closing Date:  Wednesday 28 June 2017
Reference:  SCI158617

We are seeking an excellent researcher in modelling of antimicrobial resistance. The successful applicant will use mathematical and statistical models to make predictions on risk of emergence of antimicrobial resistant pathogens in a farm slurry system and slurry amended soil. The post is funded by NERC-led EVAL-FARMS project (Evaluating the Threat of Antimicrobial Resistance in Agricultural Manures and Slurries). Thus the role holder will work closely with an interdisciplinary team, including experimental researchers in microbiology and analytical chemistry, and social researchers in science and technology studies, in order to develop meaningful, data driven risk models that could inform policy and practise. The work will involve deterministic and stochastic models, Bayesian statistics, data analysis and presentation.

Applicants must have, or be very close to completing, a PhD in mathematical, computer or statistical models applied to a relevant area in the biological or environmental sciences. Research experience in applying such models in antimicrobial resistance, metagenomics, analytical chemistry and/or water quality would be desirable. Applicants must be able to demonstrate skills in Bayesian approaches, including relevant computational techniques such as MCMC, development and analysis of deterministic and stochastic models, programming in a relevant language (e.g. R, Python or Matlab) and a broader appreciation of science. Applicants must also be able to demonstrate research ambition through timely publication of research, coupled with commitment to the research project as part of their on-going career development. Excellent oral and written English language skills are essential.

The post is a joint appointment between the Schools of Biosciences and Mathematical Sciences. The post holder will normally work on the Sutton Bonington Campus, and will also have meetings on the University Park Campus with staff in the School of Mathematics and other collaborating schools.

Fixed term for 2 years from 1st September 2017

Applications can be made through the University of Nottingham web site. I am happy to receive informal enquiries.

New publication: The dynamic balance of import and export of zinc in Escherichia coli suggests a heterogeneous population response to stress

I am absolutely delighted to see the (online) publication today of our paper:

Takahashi H, Oshima T, Hobman JL, Doherty N, Clayton SR, Iqbal M, Hill PJ, Tobe T, Ogasawara N, Kanaya S and Stekel DJ 2015. The dynamic balance of import and export of zinc in Escherichia coli suggests a heterogeneous population response to stress. Journal of the Royal Society Interface DOI: .

The abstract is at the bottom of the post. First I want to say why I am so happy about this particular paper.

1. This is the first piece of work I have published in which I have made a successful funding application (I say “I” loosely, as Taku Oshima wrote the Japanese part of the bid along with Naotake Ogasawara, Shigehiko Kanaya and Toru Tobe, and Jon Hobman wrote much of the UK part of the bid; however, I was PI as this was a Systems Biology call); led the research (most of the hard work was done by Hiroki Takahashi and Taku Oshima); wrote the paper (different parts were written by different people – almost all authors made an important contribution); and have seen the paper published. A complete cycle of more than 5 years from grant application to publication.

2. This is the first paper on which I am corresponding author that contains new experimental results. More than that, I played an important role in devising many of the experiments (time courses and viable cell count assays) – not in terms of technical details (in which I have little expertise) but in terms of what experiments we need and why we need to do them.

3. This is the first time I have come a complete “Systems Biology” cycle: from experiments, to models, to predictions, to experimental confirmation, to a new model and then new predictions.

4. Some of the most important ideas in this paper came as a result of one of the most enjoyable weeks in my scientific career. Hiroki, Jon, Selina and I were all attending the Biometals Conference in Brussels in July 2012. While Hiroki and I attended a few sessions of the conference, most of the time we spent in our hotel lobby trying to get the model to fit the data. The first data we had were the LB data, which the model could fit without too much trouble. When Taku asked me what other data we needed, I said: “time series, with different concentrations of zinc”, and by the conference we had those data too. Only one problem: the model no longer fitted the data.

Hiroki and I spent that week trying to work out how to get the model to work. Each morning, we sat in the hotel lobby, and over endless cups of tea, we devised new versions of the model; each afternoon Hiroki would code them up, and then run them overnight on the supercomputer in Japan. And the next morning, the model would still not fit the data. By the last day we were tearing our hair out: nothing we could think of would work. We had one last (desperate) idea: ditch the 100uM zinc data. Boom! The model fitted the 12.5uM zinc data just fine! And this led to the heterogeneity hypothesis, the viable cell count assay, the stochastic model, and all the results that make this paper exciting. It is that kind of week that we go into careers in science for: the frustration and delight of grappling with and overcoming a difficult problem.

5. As alluded to in the previous points, it has been an absolute delight working with my coauthors on this paper, and I have many happy memories in the UK, Japan and Brussels.

6. Finally, on a more personal note: we received this funding on 17th March 2010. In between receiving the funding and having the paper published (5 years later) I have got married (July 2010), had a baby (July 2011), had another baby (November 2013) and am now more tired yet more happy than at any other point in my life 🙂

Now for the abstract!

Abstract

Zinc is essential for life, but toxic in excess. Thus all cells must control their internal zinc concentration. We used a systems approach, alternating rounds of experiments and models, to further elucidate the zinc control systems in Escherichia coli. We measured the response to zinc of the main specific zinc import and export systems in the wild-type, and a series of deletion mutant strains. We interpreted these data with a detailed mathematical model and Bayesian model fitting routines. There are three key findings: first, that alternate, non-inducible importers and exporters are important. Second, that an internal zinc reservoir is essential for maintaining the internal zinc concentration. Third, our data fitting led us to propose that the cells mount a heterogeneous response to zinc: some respond effectively, while others die or stop growing. In a further round of experiments, we demonstrated lower viable cell counts in the mutant strain tested exposed to excess zinc, consistent with this hypothesis. A stochastic model simulation demonstrated considerable fluctuations in the cellular levels of the ZntA exporter protein, reinforcing this proposal. We hypothesize that maintaining population heterogeneity could be a bet-hedging response allowing a population of cells to survive in varied and fluctuating environments.

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

New Publication: Adaptation for Protein Synthesis Efficiency in a Naturally Occurring Self-Regulating Operon

Dorota’s second paper has just been published in PLoS ONE as:

Herman, D., Thomas, C.M. and Stekel, D.J. 2012. Adaptation for Protein Synthesis Efficiency in a Naturally Occurring Self-Regulating Operon. PLoS ONE 7(11): e49678.

We are particularly pleased with this work, and had some very nice comments from the reviewers. The quotes (from two different reviewers) here are reproduced with permission from PLoS ONE:

“the kinds of questions the authors address appear to be the most rewarding uses of computational/mathematical uses in biology”

“I found this paper important because it investigates a system with biological plausible parameters, thus, revealing whether the results of previous purely theoretical studies are biologically plausible.”

We are hoping for that sort of reception more generally in the community!

The abstract of the paper is:

The korAB operon in RK2 plasmids is a beautiful natural example of a negatively and cooperatively self-regulating operon. It has been particularly well characterized both experimentally and with mathematical models. We have carried out a detailed investigation of the role of the regulatory mechanism using a biologically grounded mechanistic multi-scale stochastic model that includes plasmid gene regulation and replication in the context of host growth and cell division. We use the model to compare four hypotheses for the action of the regulatory mechanism: increased robustness to extrinsic factors, decreased protein fluctuations, faster response-time of the operon and reduced host burden through improved efficiency of protein production. We find that the strongest impact of all elements of the regulatory architecture is on improving the efficiency of protein synthesis by reduction in the number of mRNA molecules needed to be produced, leading to a greater than ten-fold reduction in host energy required to express these plasmid proteins. A smaller but still significant role is seen for speeding response times, but this is not materially improved by the cooperativity. The self-regulating mechanisms have the least impact on protein fluctuations and robustness. While reduction of host burden is evident in a plasmid context, negative self-regulation is a widely seen motif for chromosomal genes. We propose that an important evolutionary driver for negatively self-regulated genes is to improve the efficiency of protein synthesis.

Research grant award from the BBSRC

On Friday we heard good news from the BBSRC that our research grant application for the analysis of Biolog data has been successful. This is a joint bid with Katherine Smart, Jon Hobman, Helen West and Theodore Kypraios. The relevant quote from the BBSRC is:

Dear Dr Stekel,

I am please to inform you that application BB/J01558X/1 – ‘High throughput analysis of cell growth data from phenotype arrays’ submitted to the BBSRC 2011 Responsive Mode Grant Round 3 (RM3) has been successful.  We are currently in the process of preparing the grants for announcement. 

There will be a postdoctoral position associated with this grant which will be advertised in due course according to usual University of Nottingham procedures.

Lay Summary for the Research Grant

Fifty people died as a result of the recent E. coli outbreak in Germany. Four thousand people were infected. With a growing global human population, how do we ensure that we all have access to safe food? Fossil fuels will run out, and the recent Fukushima disaster highlighted the risks of nuclear energy. How do we provide sustainable sources of fuel to meet our energy and transport needs in the context of a population that is not just growing, but also developing?

These are major challenges, and a key strategy for overcoming them is the study of microbes. In the case of E. coli the disease is caused by harmful bacteria, and we need to understand how harmful bacteria survive in farms, soil, food production, storage and preparation facilities, as well as in animal and human hosts. In the case of fuels, microbes provide an opportunity for a new generation of biofuels. Biofuels are carbon neutral technologies, but conventional biofuels need similar materials or land that could otherwise be used for food. We are now seeking to develop biofuels from plant matter that cannot be used for food and is currently wasted. To do this, we need to find new strains of yeast that can convert this plant matter into fuel.

In recent years, new technologies have been developed that enable us to read the full genome sequence of a microbe in just a day. This is indeed remarkable, but the genome sequence is a set of instructions in a language that we can only begin to understand. What really matters is how a microbe behaves in different environments: on what foods does it thrive, on what foods does it starve? What potential toxins can it survive and what toxins kill it? These questions are essential for understanding how we can combat harmful food-borne bacteria, or develop new bioenergy producing agents. And if we can link these answers to the genome sequence, we have a powerful way of decoding the language of the genes.

This proposal is focussed on a technology, called Biolog Phenotype Microarrays, that precisely measure how well microbes thrive in thousands of conditions, including different food sources and potential toxins. The arrays generate time courses that plot each condition at a regular point in time, with several hundred measurements of cell activity during the course of an experiment. Each time course encodes a wealth of information: how long does it take before the microbes start to become active? How quickly do they grow? Are they able to use more than one food source, and if so, is one better than the other? How much do they grow? Remarkably, there are no analysis methods available that allow users of Biolog arrays to obtain this information from the Biolog output: instead, users typically use a single datum, such as the end-point, or total growth, and discard most of the valuable information.

The aim of this proposal is to bridge this gap. To do so, we intend to build mathematical models that describe cell activity in Biolog arrays; these need to reflect the details of the technology, as well as the complexity of the conditions in which the cells are grown. We propose to develop automated ways of working out which model best fits any given set of data, and identify the key parameters describing microbial behaviour. Automation is essential, because a single experiment can generate 2000 microbial time courses. The methods have to be accessible to the wider scientific community, not just mathematicians, so we need to develop user-friendly interfaces to the methods we develop, and provide training for Biolog users in these methods.

Finally, in our established research programmes, we have generated vast quantities of Biolog data on survival of harmful E. coli strains, microbial soil contamination and the development of new yeast strains for producing biofuel from non-food plant material. We will directly address the food safety and bioenergy challenges by applying our methods to these data.