SIGNAL: Stochastic process algebra for biochemical signalling pathway analysis
Lead Research Organisation:
University of Edinburgh
Department Name: Lab. for Foundations of Computer 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.
Publications
Li Z
(2023)
Automatic search intervals for the smoothing parameter in penalized splines.
in Statistics and computing
Jane Hillston (Author)
(2011)
A semi-quantitative equivalence for abstracting from fast reactions
Jane Hillston (Author)
(2010)
Modelling biological systems with delays in Bio-PEPA
Jane Hillston (Author)
(2010)
Modelling and analysis of the NF-kappaB pathway in Bio-PEPA
in Transactions on Computational Systems Biology
Jane Hillston (Author)
(2009)
Equivalence and discretisation in Bio-PEPA
Jane Hillston (Author)
(2010)
Investigating modularity in the analysis of process algebra models of biochemical systems
Hillston J
(2009)
Algorithmic Bioprocesses
Guerriero ML
(2009)
Narrative-based computational modelling of the Gp130/JAK/STAT signalling pathway.
in BMC systems biology
Guerriero ML
(2012)
Stochastic properties of the plant circadian clock.
in Journal of the Royal Society, Interface
Galpin V
(2011)
A semantic equivalence for Bio-PEPA based on discretisation of continuous values
in Theoretical Computer Science
Description | The developed Bio-PEPA process algebra modelling language was shown to support robust modelling for biochemical processes. The language can be equipped with static analysis techniques to allow the automated detection of errors and inconsistencies in models. Semantic equivalences were developed which allowed models to be simplified in useful ways which preserve key aspects of behaviour but reduce the computational burden of system analysis. Moreover an extended version of Bio-PEPA was defined with enriched support for spatial modelling. The language also supported formal stochastic model checking via the PRISM tool. All these features allowed detailed analysis of biological processes. The ease of developing both a stochastic and a deterministic interpretation of a model, allowed these to be compared, and supported the investigation of the impact of stochasticity of biological processes such as the circardian rhythm in the alga Ostreoccocus tauri. |
Exploitation Route | Bio-PEPA is being taken up within the QUANTICOL project for experimental models of smart transport systems. This has informed the design of a new modelling language with enhanced capabilities for spatial modelling. |
Sectors | Aerospace Defence and Marine Digital/Communication/Information Technologies (including Software) Pharmaceuticals and Medical Biotechnology Transport |
URL | http://homepages.inf.ed.ac.uk/stg/research/SIGNAL/Home.html |
Description | Microsoft PhD Scholarship |
Amount | £35,000 (GBP) |
Organisation | Microsoft Research |
Department | Microsoft Research Cambridge |
Sector | Private |
Country | United Kingdom |
Start | 09/2012 |
End | 09/2015 |
Description | Royal Society of London |
Amount | £10,029 (GBP) |
Funding ID | JP090562 |
Organisation | The Royal Society |
Sector | Charity/Non Profit |
Country | United Kingdom |
Start | 03/2010 |
End | 03/2012 |
Title | Bio-PEPA tool |
Description | This Eclipse Plug-in has an editor to support the construction of Bio-PEPA models and a variety of static and dynamic analysis tools. |
Type Of Technology | Software |
Year Produced | 2009 |
Open Source License? | Yes |
Impact | The availability of the tools has encouraged other groups to use the Bio-PEPA language and also the language and tool are used in post-graduate teaching at a number of Universities worldwide. |
URL | http://www.biopepa.org |