SIGNAL: Stochastic process algebra for biochemical signalling pathway analysis
Lead Research Organisation:
University of Glasgow
Department Name: School of Computing Science
Abstract
The science of computational Systems Biology uses computer modellingof living organisms to help us understand those process at work insidewhich we cannot directly see or measure. Based on experimentallydetermined data, a systems biologist invents a model of how they thinkthat things work. The model is usually analysed by one of two kindsof computer simulation: stochastic or deterministic. In a stochasticsimulation the mathematical theory of probability is used to express adegree of uncertainty about how fast reactions happen or thequantities of reactants which are in use. In a deterministicsimulation the theory of ordinary differential equations is used togive an efficient continuous approximation of very large numbers ofmolecular elements. Computational modelling is helpful here becauselaboratory-based experimentation is an extremely expensive,time-consuming and labour-intensive process. An important subject forcomputational modelling is signal transduction.Signal transduction pathways are biochemical pathways which allowcells to sense a stimulus and communicate a signal to the nucleus,which then makes a suitable response. They are complicated signallingprocesses with built-in feedback mechanisms. Signalling pathways areembedded in larger networks and are involved in important processessuch as proliferation, cell growth, movement, cell communication, andprogrammed cell death (apoptosis). Malfunction results in a largenumber of diseases including cancer, diabetes and many others. Despiteenormous experimental advances in recent years there is still anabsence of good, predictive pathway models which can guideexperimentation and drug development. To date, models either encodestatic aspects such as which proteins have the potential to interact,or provide simulations of system dynamics using ordinary differentialequations.We will develop a novel approach to analytic pathway modelling basedon our experience of modelling concurrent computing systems. The keyidea is that pathways have stochastic, computational content. We willmodel pathways using stochastic process algebras which denotecontinuous time Markov chains thus affording new quantitative analysisand new ways to structure pathways and reason about incompletebehaviour.
Organisations
People |
ORCID iD |
Muffy Calder (Principal Investigator) | |
Walter Kolch (Co-Investigator) |
Publications
Andrei O
(2010)
A Model and Analysis of the AKAP Scaffold
in Electronic Notes in Theoretical Computer Science
Benford S
(2016)
On Lions, Impala, and Bigraphs Modelling Interactions in Physical/Virtual Spaces
in ACM Transactions on Computer-Human Interaction
Boca, Paul P.; Bowen, Jonathan P.; Siddiqi, Jawed I.
(2009)
Formal Methods: State of the Art and New Directions
Calder M
(2010)
Formal Methods: State of the Art and New Directions
Calder M
(2009)
Transactions on Computational Systems Biology XI
Ciocchetta F
(2009)
Some Investigations Concerning the CTMC and the ODE Model Derived From Bio-PEPA
in Electronic Notes in Theoretical Computer Science
Degasperi A
(2010)
Relating PDEs in Cylindrical Coordinates and CTMCs with Levels of Concentration
in Electronic Notes in Theoretical Computer Science
Degasperi A
(2013)
A process algebra framework for multi-scale modelling of biological systems
in Theoretical Computer Science
Donaldson R
(2012)
Modular modelling of signalling pathways and their cross-talk
in Theoretical Computer Science
Description | I tried to say no but system will not accept. Ignore this entry |
Sector | Communities and Social Services/Policy |
Impact Types | Economic |