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 ScienceWatch

2009 : June 2009 - Fast Breaking Papers : Vladislav Vyshemirsky

FAST BREAKING PAPERS - 2009

June 2009 Download this article
 
Vladislav Vyshemirsky talks with ScienceWatch.com and answers a few questions about this month's Fast Breaking Paper in the field of Computer Science.
Article Title: Bayesian ranking of biochemical system models
Authors: Vyshemirsky, V;Girolami, MA
Journal: BIOINFORMATICS
Volume: 24
Issue: 6
Page: 833-839
Year: MAR 15 2008
* Univ Glasgow, Dept Comp Sci, Glasgow G12 8QQ, Lanark, Scotland.
* Univ Glasgow, Dept Comp Sci, Glasgow G12 8QQ, Lanark, Scotland.

Why do you think your paper is highly cited?

Our paper is the first ever to describe a formal methodology to assess how well a number of mathematical models describe a specific biological mechanism. This ability to rank plausible models based on experimental evidence is of enormous importance to life scientists as it provides a formal, objective, and rational means with which to study the many possible hypotheses that could describe the mechanisms being studied.

As an example, cancer biologists have been studying the ERK pathway, also known as the p42/p44 MAP kinase pathway, for several decades, and there are many working hypotheses regarding the structure and dynamics of this signalling cascade. Many mathematical models have previously been developed to aid our understanding of ERK signalling, but there has been little attention paid to formally assessing the biological credibility of these descriptions.

"My research in the Inference Research Group at Glasgow has been focused on the development of computational techniques at the life sciences interface."

The work in our paper enables this assessment to be rigorously carried out and has subsequently been successful in identifying the role of BRaf in ERK signalling by the interplay of mathematical modelling, evidence ranking, and subsequent guided experimentation. It is for this reason that the scientific community recognizes the foundational importance of this work and hence the high level of citation of this paper.

Because the methodology described in the paper has a solid statistical foundation, in that it embeds mathematical models within the Bayesian inferential framework, it has attracted a great deal of interest from the computing science and mathematical statistics research communities.

Our development and subsequent use of advanced statistical methods based on thermodynamic integration is at the very cutting edge of statistical and computational methodology. In other words, advanced computational statistics is being used to drive forward scientific enquiry and discovery.

Does it describe a new discovery, methodology, or synthesis of knowledge?

To our knowledge, and to that of the journal editor and referees, our paper is the first to describe the novel synthesis of mathematical modelling and Bayesian statistical inference in providing a novel methodology, which is the cornerstone to support scientific enquiry.

Would you summarize the significance of your paper in layman's terms?

Advances in the availability of affordable computing power have made it possible to build mathematical models of biological systems which control, for example, the beating of a heart, the genesis of serious diseases, and possible new drug therapies. The main issue about these models is that there may well be a number of different model descriptions which all are capable of simulating the behavior of the biological system being studied.

However, when using these models to make predictions, such as what impact a certain drug will have on the control of malignant tumors, they may suggest wildly different outcomes. It is for this reason that it is of the utmost importance that a formal, objective, and rational method to assess the validity of the range of models considered is available.

Our paper describes such a methodology that employs advanced mathematical, statistical, and computing techniques. The power of our method has already been successfully demonstrated by a number of life scientists working in the field of cancer biology.

How did you become involved in this research, and were there any problems along the way?

My research in the Inference Research Group at Glasgow has been focused on the development of computational techniques at the life sciences interface. I was especially drawn to the Bayesian formalism for scientific inference and argued that it was, in effect, a formal representation of the scientific method itself.

The major challenges that had to be addressed were the computational effort required for estimating the evidence in support of a mathematical model when employing Markov Chain Monte Carlo (MCMC) sampling methods. A whole research program in developing efficient MCMC methods has emerged from this initial work. An additional challenge was to make the multidisciplinary collaboration across the mathematical and biological disciplines effective.

Where do you see your research leading in the future?

I am interested in continued research at the mathematical and life sciences interface as there are many opportunities there to make an impact of real scientific and societal significance.

Do you foresee any social or political implications for your research?

There are very clear implications that this work will lead to improvements in healthcare and pharmaceutical research.

Dr. Vladislav Vyshemirsky
Research Associate
Department of Computing Science
University of Glasgow
Glasgow, UK

View a video lecture by Vladislav Vyshemirsky on the subject of this paper.

KEYWORDS: NERVE GROWTH-FACTOR; INFERENCE; NETWORKS; IDENTIFICATION; INTEGRATION; PATHWAY; CASCADE; ERK.

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2009 : June 2009 - Fast Breaking Papers : Vladislav Vyshemirsky

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