Today I have made a new publication foray and submitted a manuscript to bioRxiv. This is the main paper to have come out of work on our BBSRC Lux grant. We are yet to find a peer-review home – but one of our co-authors has already had a conversation with someone who wants to use the method – so it was time to put the manuscript out there while we continue with the peer-review process. R code and Biomodels submission will follow. The manuscript details are:
We are looking for an excellent candidate for a PhD in Geospatial modelling the spread of antimicrobial resistance in the environment, funded by the NERC Envision doctoral training programme, supervised jointly by myself, Stuart Marsh (Nottingham Geospatial Institute), Malcolm Bennett (School of Veterinary Medicine and Science) and Andrew Singer (Centre for Ecology and Hydrology). Details of the project are below. Please apply by 6th January on http://www.envision-dtp.org/portal/apply.php.
Our vision is to develop mathematical models that can predict AMR spread in the environment. Such modelling will require numerous factors, including: prevalence of ARGs and the relative role of different AMR sources, pathways, drivers and receptors. These models would be used to inform policy on the priorities for controlling AMR in agriculture and the wider natural environment and on the most appropriate specific actions following an outbreak of an AMR pathogen. They will also help prioritise AMR surveillance. Most mathematical modelling for the environmental spread of AMR operates locally, e.g. in a slurry tank, field soil or a WWTP, or a smaller still, e.g. a biofilm. A challenge is to develop predictive models at much larger environmental scales.
This PhD project will begin to address this challenge, by following four novel modelling approaches: incorporation of the heterogeneity of AMR agents; using a combination of deterministic and stochastic models to account for both microscopic and population level scales; up-scaling the current approaches to an environmental scale by using methods developed for geospatial modelling of pollutants; and calibrating the models with geospatially explicit environmental AMR surveillance data from our projects and those of our collaborators.
Applicants should hold a minimum of a UK Honours Degree at 2:1 level or equivalent in any relevant scientific discipline with considerable quantitative component (mathematics, physics, computer science, engineering). They must be able to evidence excellent mathematical and computer programming skills, a willingness to work across multi-disciplinary boundaries, including physical geography and microbiology.
Full studentships are available to UK/EU candidates who’ve been ordinarily resident in the UK throughout the 3-year period immediately preceding the date of an award. EU candidates who’ve not been resident in the UK for the last 3-years are eligible for “tuition fees-only” awards (no maintenance grant).
We are delighted that our second paper – and first modelling paper – on antimicrobial resistance in slurry has been pubished, also in FEMS Microbial Ecology.
Baker M, Hobman JL, Dodd CER, Ramsden SJ and Stekel DJ (2016). Mathematical modelling of antimicrobial resistance in agricultural waste highlights importance of gene transfer rate. FEMS Microbial Ecology DOI:10.1093/femsec/fiw040.
The work came from the very short post that Michelle spent with us – funded by pump prime money from the school. Both the experimental paper (led by Jon Hobman) and the modelling paper have been accepted for the Virtual Issue of FEMS Microbial Ecology: Environmental Dimension of Antibiotic Resistance associated with the EDAR 2015 conference we attended last year. These papers can show the value and importance of timely institutional pump prime support.
Antimicrobial resistance is of global concern. Most antimicrobial use is in agriculture; manures and slurry are especially important because they contain a mix of bacteria, including potential pathogens, antimicrobial resistance genes and antimicrobials. In many countries, manures and slurry are stored, especially over winter, before spreading onto fields as organic fertilizer. Thus these are a potential location for gene exchange and selection for resistance. We develop and analyze a mathematical model to quantify the spread of antimicrobial resistance in stored agricultural waste. We use parameters from a slurry tank on a UK dairy farm as an exemplar. We show that the spread of resistance depends in a subtle way on the rates of gene transfer and antibiotic inflow. If the gene transfer rate is high, then its reduction controls resistance, while cutting antibiotic inflow has little impact. If the gene transfer rate is low, then reducing antibiotic inflow controls resistance. Reducing length of storage can also control spread of resistance. Bacterial growth rate, fitness costs of carrying antimicrobial resistance and proportion of resistant bacteria in animal faeces have little impact on spread of resistance. Therefore effective treatment strategies depend critically on knowledge of gene transfer rates.
Delighted to say that our first paper of 2016 is published. Matthias’s Biolog paper is now online-ready with the Journal of Bioinformatics and Computational Biology. This is also the first output from our Biolog grant – with a second paper detailing our newer software and analysis being planned.
Gerstgrasser M, Nicholls S, Stout M, Smart K, Powell C, Kypraios T and Stekel DJ. 2016. A Bayesian approach to analyzing phenotype microarray data enables estimation of microbial growth parameters. J Bioinform Comput Biol. DOI: 10.1142/S0219720016500074
Biolog phenotype microarrays (PMs) enable simultaneous, high throughput analysis of cell cultures in different environments. The output is high-density time-course data showing redox curves (approximating growth) for each experimental condition. The software provided with the Omnilog incubator/reader summarizes each time-course as a single datum, so most of the information is not used. However, the time courses can be extremely varied and often contain detailed qualitative (shape of curve) and quantitative (values of parameters) information. We present a novel, Bayesian approach to estimating parameters from Phenotype Microarray data, fitting growth models using Markov Chain Monte Carlo (MCMC) methods to enable high throughput estimation of important information, including length of lag phase, maximal “growth” rate and maximum output. We find that the Baranyi model for microbial growth is useful for fitting Biolog data. Moreover, we introduce a new growth model that allows for diauxic growth with a lag phase, which is particularly useful where Phenotype Microarrays have been applied to cells grown in complex mixtures of substrates, for example in industrial or biotechnological applications, such as worts in brewing. Our approach provides more useful information from Biolog data than existing, competing methods, and allows for valuable comparisons between data series and across different models.
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!
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.
We are delighted that our work with Josh Rappoport‘s laboratory, supported by the Birmingham-Nottingham Strategic Collaboration Fund, has led to successful publication. Well done to all, especially Sarah Fletcher who carried out all the experimental work, Mudassar Iqbal who did the model data fitting, and Sara Jabbari who has helped out both Mudassar with the modelling and Sarah with sorting out journal requirements.
Fletcher, S.J., Iqbal, M., Jabbari, S., Stekel, D.J. and Rappoport, J.Z. 2014. Analysis of Occludin Trafficking, Demonstrating Continuous Endocytosis, Degradation, Recycling and Biosynthetic Secretory Trafficking. PLoS ONE DOI: 10.1371/journal.pone.0111176.
Tight junctions (TJs) link adjacent cells and are critical for maintenance of apical-basolateral polarity in epithelial monolayers. The TJ protein occludin functions in disparate processes, including wound healing and Hepatitis C Virus infection. Little is known about steady-state occludin trafficking into and out of the plasma membrane. Therefore, we determined the mechanisms responsible for occludin turnover in confluent Madin-Darby canine kidney (MDCK) epithelial monolayers. Using various biotin-based trafficking assays we observed continuous and rapid endocytosis of plasma membrane localised occludin (the majority internalised within 30 minutes). By 120 minutes a significant reduction in internalised occludin was observed. Inhibition of lysosomal function attenuated the reduction in occludin signal post-endocytosis and promoted co-localisation with the late endocytic system. Using a similar method we demonstrated that ~20% of internalised occludin was transported back to the cell surface. Consistent with these findings, significant co-localisation between internalised occludin and recycling endosomal compartments was observed. We then quantified the extent to which occludin synthesis and transport to the plasma membrane contributes to plasma membrane occludin homeostasis, identifying inhibition of protein synthesis led to decreased plasma membrane localised occludin. Significant co-localisation between occludin and the biosynthetic secretory pathway was demonstrated. Thus, under steady-state conditions occludin undergoes turnover via a continuous cycle of endocytosis, recycling and degradation, with degradation compensated for by biosynthetic exocytic trafficking. We developed a mathematical model to describe the endocytosis, recycling and degradation of occludin, utilising experimental data to provide quantitative estimates for the rates of these processes.
The International Conference Modelling Biological Evolution 2013: Recent Progress, Current Challenges and Future Directions will be held at the University of Leicester on May 1-3, 2013. The topics sound very interesting, and are:
- Evolutionary Epidemiology of Infectious Disease
- Models of Somatic Evolution of Cancer
- Evolutionary Population Ecology
- Models in Behavioural Ecology and Sociobiology
- Solving Social Dilemmas
- Models of Evolution of Language
- Population and Quantitative Genetics
I have been invited to contribute a talk and will give a talk with the following title and abstract:
Adaptation for protein synthesis efficiency in natural and artificial gene regulatory networks
Dorota Herman, Dafyd Jenkins, Chris Thomas and Dov Stekel
In this talk, we will summarize work on the use of mathematical and computer models to explore the evolution and adaptation of gene regulatory network architectures.
First, we will look at a natural system, the korAB operon in RK2 plasmids, which is a beautiful natural example of a negatively and cooperatively self-regulating operon. We use a biologically grounded mechanistic multi-scale stochastic 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.
Next, we summarize results from two different artificial gene regulatory network models that are free to evolve: a fine-grained model that allows detailed molecular interactions, and a coarse-grained model that allows rapid evolution of many generations. A similar theme emerges in these models too: the control of cell energy and resources is a major driver of gene network topology and function. This is demonstrated in the fine-grained model with the emergence of biologically realistic mRNA and protein turnover rates that optimize energy usage and cell division time, and the evolution of basic repressor activities, and in the coarse-grained model by emergence of global regulators keeping all cellular systems under negative control.o
So far as I am aware, the conference organizers have extended their registration deadline, so places are still available for the next few days.