New publication: Mathematical modelling of antimicrobial resistance in agricultural waste highlights importance of gene transfer rate

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.

Abstract

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.

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New Publication: Modelling Plasmid Regulatory Systems

Springer have brought out on on-line encyclopedia on Molecular Life Sciences and my contribution has just been published:

Stekel D.: Modelling Plasmid Regulatory Systems. In: Bell E., Bond J., Klinman J., Masters B., Wells R. (Ed.) Molecular Life Sciences: An Encyclopedic Reference: SpringerReference (www.springerreference.com). Springer-Verlag Berlin Heidelberg, 2013.

Synopsis

The success of plasmids as stably inherited, autonomously replicating units depends on control circuits that ensure that positive events such as replication occur efficiently at a set average frequency and that the genetic load carried by the plasmid is at minimal metabolic cost to the host.  While selective pressure has ensured that natural plasmids do achieve this, the wish to exploit plasmids or interfere with their survival mechanisms for biotechnological applications means that we need to understand the critical features that are needed for success.  Mathematical modelling of the intracellular control circuits can help to explore different systems and to distinguish between key parameters and those whose variation will have little effect on the system.  The relatively low complexity of plasmids makes them ideal systems to model and they also provide suitable systems to test prediction from the models.  In the past, plasmid modelling has particularly focussed on the ColE1 and R1 plasmids, using both deterministic and stochastic approaches; more recent work has started to address plasmids with more complex regulatory architectures, such as RK2. This has developed our understanding of the contrasting regulatory mechanisms found in high and low copy number plasmids. The combination of mathematical modelling with robust statistical methods for parameter estimation can integrate experimental data into the model, leading to more realistically parameterized mathematical models. These have greater predictive power and are likely to play a crucial future role in the rational design of plasmids for use in biotechnology and bioprocessing.

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.

New Publication: Evolution of resource and energy management in biologically realistic gene regulatory network models

Today sees the publication of a new article:

Stekel, D.J. and Jenkins, D.J. 2012. Evolution of resource and energy management in biologically realistic gene regulatory network models. Advances in Experimental Medicine and Biology 751: 301-328.

This is, in fact, a chapter in the book Evolutionary Systems Biology that has been edited by Orkun Soyer and contains many interesting articles.

Our own article is a review of research work carried out mostly by Dafyd during his PhD. The abstract is:

We describe the use of computational models of evolution of artificial gene regulatory networks to understand the topologies of biological gene regulatory networks. We summarize results from three complementary approaches that explicitly represent biological processes of transcription, translation, metabolism and gene regulation: a fine-grained model that allows detailed molecular interactions, a coarse-grained model that allows rapid evolution of many generations, and a fixed-architecture model that allows for comparison of different hypotheses. In the first two cases, we are able to evolve networks towards the biological fitness objectives of survival and reproduction. A theme that emerges is that 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; in the fixed architecture model with a negative self-regulating gene evolving major efficiencies in mRNA usage; and in the coarse-grained model by the need for the inclusion of basal gene expression to obtain biologically plausible networks and the emergence of global regulators keeping all cellular systems under negative control. In summary, we demonstrate the value of biologically realistic computer evolution techniques, and the importance of energy and resource management in driving the topology and function of gene regulatory networks.

We are excited by this article as it brings together an important and emergent theme from four of our previous research articles.

Modelling and Microbiology Summer School

Modelling and Microbiology Summer School
University of Nottingham, 20-23 August

The aims of our summer school are:
•       To bring together microbiologists and theoreticians in a practical-led training workshop at the interface of modelling and microbiology
•       To provide modellers with the opportunity to carry out laboratory experimental work
•       To provide experimentalists with the opportunity to carry out mathematical modelling
•       To show how working together with the two approaches on the same data can enhance our understanding of the natural world
•       To provide research seminars at the interface of modelling and microbiology to showcase state-of-the-art systems biology research

For the two practical days, delegates with experimental and theoretical backgrounds will be paired, working together first in the laboratory, and then in the computer room. This exciting format enables people with different disciplines to learn from each other.

Open to all – MSc and PhD students, post-doctoral researchers, PIs, pharma and biotech R&D.

We welcome applications from all sub-disciplines – you don’t have to be a microbiologist to attend!

Places are limited to 28. We will ask whether your background is mainly experimental or theoretical and a brief statement of what you hope to get out of the workshop. Thanks to support from BBSRC under the StoMP and Bioluminescence grants the course fee is only £80 and this covers tuition, single en-suite accommodation for 3 nights, and all meals and refreshments.

Confirmed seminar speakers

Paul Williams (Nottingham)
Gail Preston (Oxford)
John Ward (Loughborough)

Course Organisers: Dov Stekel (University of Nottingham) and Jamie Wood (University of York).

Applications are now being accepted – please complete the application form at:

http://www.cpib.ac.uk/events/microbiology-workshop-registration-form/

If you have any problems or questions, please contact Mirela Axinte (Mirela.Axinte@nottingham.ac.uk)

To download a flyer, please click the following link:

Modelling and Microbiology summer school flyer

BBSRC Lux Grant Launch

This weeks sees the launch of our BBSRC Lux grant.

We are delighted to have recruited two experienced and talented PostDocs to work on the project.

Neil Doherty will work with Phil Hill and Dave Scott on the experimental elements of the work. Neil has a BSc in Biochemistry from the University of Warwick and a PhD in molecular microbiology also from the University of Warwick. He has since been carrying out postdoctoral research at the University of Nottingham in a number of molecular microbiology research groups, where he has carried out a wide range of experimental work in Staphylococcus aureus, Helicobacter pylori, Streptococcus pneumoniae and Escherichia coli.

Mudassar Iqbal will work with me on the modelling and inference elements of the work. Mudassar has a MSc in physics from the University of the Punjab, an MRes in modelling and simulation of complex realities from the ICTP/SISSA, Trieste, and a PhD in bioinformatics at the University of Kent. He was since carrying out postdoctoral research at the Warwick Systems Biology Centre. Mudassar’s experience includes development of algorithms for analysis of codon usage bias, protein-protein interactions and inference in transcriptomics.

We welcome both Neil and Mudassar to Nottingham and look forward to several years of interdisciplinary research.

 

e – Science Institute Theme 14 Workshop: Microbiology Modelling and Evolution

In the last three days I have been running a focussed workshop at the e-Science Institute in Edinburgh on microbiology modelling and evolution. With 16 contributors roughly equally split between experimentalists and modelers, we presented and discussed scientific work at the interface between experimental and modelling approaches to evolution in microbiology.

At the start of the meeting, I set out my personal views on the challenges and opportunities at this interface – my hope was that at the end of the meeting my views would be in need of updating ! From a biological perspective, the challenges are around emergence of antimicrobial resistance and pathogenicity; the opportunities afforded by fast and cheap genome sequencing. From a modelling perspective, the challenges are around developing suitably realistic models, of generating experimentally testable hypotheses, and of gaining recognition of evolutionary computing as a means to test hypothesis; the opportunities are afforded by the capacity for long-term evolution by high performance computation.

Every talk was interesting and of a high standard – and the discussion was lively and engaging. A few points that came up that I found especially salient were: (i) microbes are not just interesting/important in themselves, but also as models for general biological processes, especially social processes, including host-pathogen interactions, evolution of cooperativity and evolution of communication. (ii) The challenge of genomic diversity is greater than I even imagined. I’m already used to genomic diversity in E. coli strains, but it was amazing to see it across so many bacteria studied, including Pseudomonas aeruginosa from a single patient’s lung, or Rhizobium from root nodules in clover and vetch in a single tiny patch. (iii) The importance of considering horizontal gene transfer, and thus different levels of selection, in models of mircobial evolution. (iv) The criticality of spatial organization in both real and simulated evolution, and thus the opportunity to use high performance computation for simulation of spatial scales as well as time scales.

I was heartened by the genuine sense of good will to work together at this interface, and a shared view that ‘Systems Biology’ needs to move beyond modelling how systems are now, into being able to explain why systems might have evolved to be the way they are, and predict how they might evolve.