Computational Approaches to In Vivo Cell Signalling: Inference, Network Structure and Dynamic Decision-Making

How do cells sense their environment and respond to extracellular cues? This has been a central question in molecular biology, and one with far-fetching implications in biomedical science. For example, regarding how signals drive stem cells differentiation and why cancerous cells ignore restraining signals sent from the rest of the body. Cell signalling processes -- responsible for the flow of information from the cell membrane to the nucleus -- have been extensively studied over the last decades and a great deal has been learnt about the underlying molecular mechanisms. Recent technological progress, which enables the study of cellular responses under carefully controlled dynamic environments, opens up the way for a more quantitative understanding of how cells process noisy incoming signals and then make 'decisions'.

The aim of this research project is to provide new and essential computational and statistical approaches for the analysis of cell signalling. These methods will rely on information theory, a mathematical tool set that was developed by C.E. Shannon during the 40s to tackle communication problems. Since then the theory has found applications in many fields of science and engineering, form artificial intelligence to neuroscience, and recently also in cell signalling research.

Our methods will be used to study an important class of cell signalling systems, called MAPK pathways, that have been implicated in many human diseases such as diabetes and cancer. Along with experimental collaborators at the universities of Bristol and Edinburgh we plan to study how these systems process environmental signals and enable cells to commit to particular 'decisions'. Our research will contribute to our understanding of cell signalling and will hopefully bring us one step closer to the ultimate goal of being able to predict and control cellular behaviour.

A major challenge in biomedical science is to analyse the dynamic process by which healthy and diseased cells transfer the information conveyed by extracellular signals, and to understand how this process alters their behaviour. Technological progress in microfluidics and imaging makes it possible to stimulate single cells with carefully controlled input signals and measure their 'noisy' (stohastic) responses over time. Hence, exciting opportunities arise for studying and understanding information transfer through network of cell signalling and gene regulation. Reliable and tailored computational methods are currently needed to quantify information transfer trough these networks; to probe how information transfer relates to their architecture; to describe which features of fluctuating signals are encoded in their dynamics; and to understand how signal processing affects cellular behaviour.

This research proposal aims to provide novel statistical approaches for quantitative analysis of signal processing by single cells. These methods will provide point estimates (along with appropriate confidence intervals) of information-theoretic quantities (entropy and mutual information) that can be used to quantify information transfer in cell signalling pathways and gene regulatory networks. Using these methods I will study signal transduction by mitogen-activated protein kinase (MAPK) cascades. The aim will be two-fold: to probe the relationship between network structure and information transfer, and to analyse the dynamics of cellular decision-making. In collaboration with the McArdle Lab (University of Bristol) I will analyse the effect of feedback control on ERK signalling in mammalian cells. In collaboration with the Swain Lab (University of Edinburgh), I will investigate how both network structure and the features (statistics) of input fluctuations determine the dynamics of cellular decision-making, using yeast's mating response system (driven by MAPKs Fus3 and Kss1).

Who are the potential non-academic beneficiaries of the research?

There are at least two classes of non-academic beneficiaries that will benefit from our research:
1.Medicine regulatory agencies
2.Pharmaceutical industry

In late 2011 the European Medicine Association (EMA) in collaboration with the European Federation of Pharmaceutical Industries and Associations (EFPIA) organised a workshop with the objective "to discuss the role and scope of modelling and simulation in drug-development both from the developer's and the regulator's perspectives" [1]. Since our research is directly concerned with how cells process and respond environmental stimuli, we believe that our methods and results will be of relevance to this effort, with direct applications in early medicine development and in the quantitative assessment of medicines at the single cell level.

How are they likely to benefit?

According to the Association of the British Pharmaceutical Industry (ABPI), tests of candidate compounds on in-vivo disease models constitute a major step in the procedure of medicine development. Such studies enable companies to assess the pharmacodynamic and pharmacokinetic properties of the compounds and optimise them. In this context computational tools and models could provide preliminary guidance for the efficacy of particular compounds and make quantitative predictions of how multicompound drugs are expected to act. Therefore, I believe that my work could be of possible relevance as it provides a framework for quantitatively assessing the relationship between drug effectors and single-cell responses. Such a framework will be a valuable addition in the preclinical stages of drug-development.

[1] http://www.ema.europa.eu/ema/index.jsp?curl=pages/news_and_events/news/2011/12/news_detail_001400.jsp|=WC0b01ac058004d5c1