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!

Invitation to Contribute a Talk to Modelling Biological Evolution 2013: Recent Progress, Current Challenges and Future Directions

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.

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.

Seminars this week at Nottingham and Leicester

I will be delivering two seminars this week.

On Wednesday I will be giving a talk in the local “Knowledge Transfer” series about mathematical modelling in biology. This talk is for general audience: all are welcome. 11:30am, Rushcliffe Restaurant, Sutton Bonington Campus.

Closer to home, on Thursday I will be speaking in the Applied Maths seminar series at the University of Leicester on dynamical models and inference for bacterial gene regulation. 2pm, Room 119, Michael Atiyah Building.

Please come to either or both if you are interested.