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.

Model Parameter estimation for Predictive Medicine

I am delighted to have been invited as a tutor to the Wellcome-Trust funded course on Model Parameter Estimation for Predictive Medicine organized by Sara Jabbari and Joanne Dunster. The course is being held from 4th-7th July at the University of Birmingham and is jointly run with the University of Nottingham.

The course promises to be extremely interesting, covering classical approaches to fitting dynamical models to data along with its main focus on Bayesian approaches, especially MCMC. Particularly pleased to be teaching alongside my colleagues Simon Preston and Theodore Kypraios.

Fitting models to data is a crucially important skill that has been somewhat neglected in mainstream mathematical biology and medicine so it is really great that Sara and Joanne have organized this and that Wellcome Trust has funded it. Do sign up!

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.