36,721 research outputs found
Petri nets for systems and synthetic biology
We give a description of a Petri net-based framework for
modelling and analysing biochemical pathways, which uniĀÆes the qualita-
tive, stochastic and continuous paradigms. Each perspective adds its con-
tribution to the understanding of the system, thus the three approaches
do not compete, but complement each other. We illustrate our approach
by applying it to an extended model of the three stage cascade, which
forms the core of the ERK signal transduction pathway. Consequently
our focus is on transient behaviour analysis. We demonstrate how quali-
tative descriptions are abstractions over stochastic or continuous descrip-
tions, and show that the stochastic and continuous models approximate
each other. Although our framework is based on Petri nets, it can be
applied more widely to other formalisms which are used to model and
analyse biochemical networks
Recommended from our members
An introduction to Biomodel engineering, illustrated for signal transduction pathways
BioModel Engineering is the science of designing, constructing
and analyzing computational models of biological systems. It is inspired
by concepts from software engineering and computing science.
This paper illustrates a major theme in BioModel Engineering, namely
that identifying a quantitative model of a dynamic system means building
the structure, finding an initial state, and parameter fitting. In our
approach, the structure is obtained by piecewise construction of models
from modular parts, the initial state is obtained by analysis of the structure
and parameter fitting comprises determining the rate parameters of
the kinetic equations. We illustrate this with an example in the area of
intracellular signalling pathways
Bank reserve requirements and their enforcement: a comparison across streets
Bank reserves ; Money supply
Analysis of signalling pathways using the prism model checker
We describe a new modelling and analysis approach for signal
transduction networks in the presence of incomplete data. We illustrate
the approach with an example, the RKIP inhibited ERK pathway
[1]. Our models are based on high level descriptions of continuous time
Markov chains: reactions are modelled as synchronous processes and concentrations
are modelled by discrete, abstract quantities. The main advantage
of our approach is that using a (continuous time) stochastic logic
and the PRISM model checker, we can perform quantitative analysis of
queries such as if a concentration reaches a certain level, will it remain at
that level thereafter? We also perform standard simulations and compare
our results with a traditional ordinary differential equation model. An
interesting result is that for the example pathway, only a small number
of discrete data values is required to render the simulations practically
indistinguishable
- ā¦