SIGNAL

Stochastic process algebra for biochemical signalling pathway analysis
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Aims
We are developing a novel approach to analytic pathway modelling
based on our experience of modelling concurrent computing systems. The key idea is that pathways have stochastic, computational content. We model pathways using stochastic process algebras which denote continuous time Markov chains thus affording new quantitative analysis and new ways to reason about incomplete behaviour.
Background

Signal transduction pathways are biochemical pathways which allow cells to sense a stimulus and communicate a signal to the nucleus, which then makes a suitable response. They are complicated signalling processes with built-in feedback mechanisms.

Signalling pathways are embedded in larger networks and are involved in important processes such as proliferation, cell growth, movement, cell communication, and programmed cell death (apoptosis). Malfunction results in a large number of diseases including cancer, diabetes and many others.

Despite enormous experimental advances in recent years there is still an absence of good, predictive pathway models which can guide experimentation and drug development. To date, models either encode static aspects such as which proteins have the potential to interact, or provide simulations of system dynamics using ordinary differential equations.

Members of the SIGNAL project

 

Oana Andrei

Department of Computing Science, University of Glasgow

 

Muffy Calder 
Department of Computing Science, University of Glasgow

 

Federica Ciocchetta

Laboratory for Foundations of Computer Science,

University of  Edinburgh

 

Andrea Degasperi

Department of Computing Science, University of Glasgow

 

Adam Duguid
Laboratory for Foundations of Computer Science,

University of  Edinburgh


Vashti Galpin

Laboratory for Foundations of Computer Science,

University of Edinburgh

 

Stephen Gilmore
Laboratory for Foundations of Computer Science,

University of  Edinburgh

 

Maria Luisa Guerriero

Laboratory for Foundations of Computer Science,

University of  Edinburgh


Jane Hillston
Laboratory for Foundations of Computer Science,

University of Edinburgh

 

 

Collaborators

 

 

Anna Dominiczak

BHF Glasgow Cardiovascular Research Centre

University of Glasgow

 

Walter Kolch

Beatson Institute for Cancer Research, Glasgow

 

Peter Saffrey

Department of Computing Science, University of Glasgow