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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.
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| 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 structure pathways and reason about incomplete behaviour. |
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- Modelling the influence of RKIP on the ERK signalling pathway using the stochastic process algebra PEPA
- This paper examines the influence of the Raf Kinase Inhibitor Protein (RKIP) on the Extracellular signal Regulated Kinase (ERK) signalling pathway
through modelling in a Markovian process algebra, PEPA. Two models of the system are presented, a reagent-centric view and a pathway-centric view. Each
model affords a different perspective of the pathway and analysis. We demonstrate the two models to be formally equivalent using the timing-aware
bisimulation defined over PEPA models and discuss the biological significance.
This paper appeared in the proceedings of the Bioconcur workshop in London in September 2004.
(PostScript version)
An extended version of this paper is to appear in Transactions on Computational Systems Biology in 2006.
(PDF version)
- Automatically deriving ODEs from process algebra models of signalling pathways
- Differential equations are a classical approach for biochemical system modelling and have frequently been used to describe reactions of interest in
biochemical pathways. Process algebras have also been applied in a small number of cases to describe such systems. In this paper we establish a
connection between these approaches. This has the benefit of allowing process algebra models to be validated against trusted ODEs or, conversely,
allowing ODEs derived from process algebra models to be evaluated and compared using bisimulation or other methods. In addition the process algebra
models may now be efficiently solved using numerical differential equations procedures such as adaptive fifth-order Runge-Kutta.
This paper appeared in the proceedings of the Computational Methods in Systems Biology workshop (CMSB) in Edinburgh in April 2005.
(PDF version).
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- Adventures in Systems Biology : Jane Hillston, Newcastle, June 2005
- The stochastic process algebra PEPA is well-established as a formalism for performance modelling of computer and communication systems. However in
recent years we have been investigating the potential of PEPA, or a similar stochastic process algebra, for modelling signal tranduction pathways
within cells. In this talk I will present some of our initial work in this area. I will also explain how this work has also changed our perspectives on
performance modelling.
(PDF slides)
- Formal Methods Meets Biochemical Pathways : Muffy Calder, London, September 2005
- In this talk I will consider how theories and tools from Computing Science can be used to model and reason about signal transduction pathways --
the fundamental biochemical pathways that control important cell responses such as growth, movement, and (cell) death. The key idea is that pathways
have a stochastic, computational content. If we accept this, then why don't we try to model and reason about them using techniques for computer
networks?
In this talk I will use the stochastic process algebra PEPA, and the model checker PRISM, to develop and analyse a number of novel, predictive
models for parts of the ERK pathway (this pathway plays an important role in cancer). I will discuss how these new models compare with traditional
biochemical models, what the biologists say about them, and the implications for Computing Science.
(PDF handout)
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- The influence of RKIP on the ERK signalling pathway
- The most fundamental cellular processes are controlled by extracellular signalling. This signalling, or communication between cells, is based upon
the release of signalling molecules, which migrate to other cells and deliver stimuli to them (e.g. protein phosphorylation). Cell signalling is of
special interest to cancer researchers because when cell signalling pathways operate abnormally, cells divide uncontrollably.
The Ras/Raf-1/MEK/ERK pathway (also called Ras/Raf, or ERK pathway) is a ubiquitous pathway that conveys mitogenic and differentiation signals from
the cell membrane to the nucleus. Briefly, Ras is activated by an external stimulus, it then binds to and activates Raf-1 which in turn activates MEK
and then ERK. This cascade of protein interaction controls cell differentiation, the effect being dependent upon the activity of ERK.
A current area of experimental scientific investigation is the role the kinase inhibitor protein RKIP plays in the behaviour of this pathway: the
hypothesis is that it inhibits activation of Raf and thus can dampen down the ERK pathway. Thus good models of these pathways are required to
understand the role of RKIP and develop new therapies. Moreover, an understanding of the functioning and structure of this pathway may lead to more
general results applicable to other pathways.
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- Performance Evaluation Process Algebra
- Our work to date has used the stochastic process algebra PEPA in the modelling process and software tools such as the PEPA Workbench for the
analysis undertaken. Papers on PEPA, software tools and example models are available from the PEPA Web
site.
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- Reasoning about Biochemical Signalling
- Quantitative methods are one way of seeking to understand signalling pathways but logical methods are just as important. Using the BioSigNet-RR
representation and reasoning tool others have studied the ERK pathway (Reasoning about the ERK
signal transduction pathway using BioSigNet-RR).
- Biochemical Pathway Simulator
- The Beacon-funded Biochemical Pathway Simulator project is a complementary project which
uses both simulation and verification to investigate the behaviour of biochemical networks, focussing on programmed cell death (apoptosis) and growth
factor activated kinase (MAPK).
- PRISM
- Several case studies on biological process modelling have been conducted
with the PRISM Probabilistic Model-Checker, applying process calculi and probabilistic model
checking technology to the study of biological processes.
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