Results 1  10
of
59
BioPEPA: a framework for the modelling and analysis of biological systems
, 2008
"... In this work we present BioPEPA, a process algebra for the modelling and the analysis of biochemical networks. It is a modification of PEPA, originally defined for the performance analysis of computer systems, in order to handle some features of biological models, such as stoichiometry and the use ..."
Abstract

Cited by 94 (25 self)
 Add to MetaCart
In this work we present BioPEPA, a process algebra for the modelling and the analysis of biochemical networks. It is a modification of PEPA, originally defined for the performance analysis of computer systems, in order to handle some features of biological models, such as stoichiometry and the use of general kinetic laws. The domain of application is the one of biochemical networks. BioPEPA may be seen as an intermediate, formal, compositional representation of biological systems, on which different kinds of analysis can be carried out. BioPEPA is enriched with some notions of equivalence. Specifically, the isomorphism and strong bisimulation for PEPA have been considered. Finally, we show the translation of three biological models into the new language and we report some analysis results.
Rulebased modeling of biochemical systems with BioNetGen
 IN METHODS IN MOLECULAR BIOLOGY: SYSTEMS BIOLOGY
, 2009
"... Rulebased modeling involves the representation of molecules as structured objects and molecular interactions as rules for transforming the attributes of these objects. The approach is notable in that it allows one to systematically incorporate sitespecific details about proteinprotein interactio ..."
Abstract

Cited by 43 (10 self)
 Add to MetaCart
(Show Context)
Rulebased modeling involves the representation of molecules as structured objects and molecular interactions as rules for transforming the attributes of these objects. The approach is notable in that it allows one to systematically incorporate sitespecific details about proteinprotein interactions into a model for the dynamics of a signaltransduction system, but the method has other applications as well, such as following the fates of individual carbon atoms in metabolic reactions. The consequences of proteinprotein interactions are difficult to specify and track with a conventional modeling approach because of the large number of protein phosphoforms and protein complexes that these interactions potentially generate. Here, we focus on how a rulebased model is specified in the BioNetGen language (BNGL) and how a model specification is analyzed using the BioNetGen software tool. We also discuss new developments in rulebased modeling that should enable the construction and analyses of comprehensive models for signal transduction pathways and similarly largescale models for other biochemical systems.
Statistical model checking in BioLab: applications to the automated analysis of TCell receptor signaling pathway
 In CMSB’08
, 2008
"... Abstract. We present an algorithm, called BioLab, for verifying temporal properties of rulebased models of cellular signalling networks. BioLab models are encoded in the BioNetGen language, and properties are expressed as formulae in probabilistic bounded linear temporal logic. Temporal logic is a ..."
Abstract

Cited by 25 (7 self)
 Add to MetaCart
(Show Context)
Abstract. We present an algorithm, called BioLab, for verifying temporal properties of rulebased models of cellular signalling networks. BioLab models are encoded in the BioNetGen language, and properties are expressed as formulae in probabilistic bounded linear temporal logic. Temporal logic is a formalism for representing and reasoning about propositions qualified in terms of time. Properties are then verified using sequential hypothesis testing on executions generated using stochastic simulation. BioLab is optimal, in the sense that it generates the minimum number of executions necessary to verify the given property. BioLab also provides guarantees on the probability of it generating TypeI (i.e., falsepositive) and TypeII (i.e., falsenegative) errors. Moreover, these error bounds are prespecified by the user. We demonstrate BioLab by verifying stochastic effects and bistability in the dynamics of the Tcell receptor signaling network.
Abstracting the differential semantics of rulebased models: exact and automated model reduction (revised version)
, 2010
"... ..."
Rulebased modelling, symmetries, refinements
"... Abstract. Rulebased modelling is particularly effective for handling the highly combinatorial aspects of cellular signalling. The dynamics is described in terms of interactions between partial complexes, and the ability to write rules with such partial complexesi.e., not to have to specify all the ..."
Abstract

Cited by 20 (9 self)
 Add to MetaCart
(Show Context)
Abstract. Rulebased modelling is particularly effective for handling the highly combinatorial aspects of cellular signalling. The dynamics is described in terms of interactions between partial complexes, and the ability to write rules with such partial complexesi.e., not to have to specify all the traits of the entitities partaking in a reaction but just those that matter is the key to obtaining compact descriptions of what otherwise could be nearly infinite dimensional dynamical systems. This also makes these descriptions easier to read, write and modify. In the course of modelling a particular signalling system it will often happen that more traits matter in a given interaction than previously thought, and one will need to strengthen the conditions under which that interaction may happen. This is a process that we call rule refinement and which we set out in this paper to study. Specifically we present a method to refine rule sets in a way that preserves the implied stochastic semantics.
Process algebras in systems biology
"... Abstract. In this chapter we introduce process algebras, a class of formal modelling techniques developed in theoretical computer science, and discuss their use within systems biology. These formalisms have a number of attractive features which make them ideal candidates to be intermediate, formal, ..."
Abstract

Cited by 10 (2 self)
 Add to MetaCart
(Show Context)
Abstract. In this chapter we introduce process algebras, a class of formal modelling techniques developed in theoretical computer science, and discuss their use within systems biology. These formalisms have a number of attractive features which make them ideal candidates to be intermediate, formal, compositional representations of biological systems. As we will show, when modelling is carried out at a suitable level of abstraction, the constructed model can be amenable to analysis using a variety of different approaches, encompassing both individualsbased stochastic simulation and populationbased ordinary differential equations. We focus particularly on BioPEPA, a recently defined extension of the PEPA stochastic process algebra, which has features to capture both stoichiometry and general kinetic laws. We present the definition of the language, some equivalence relations and the mappings to underlying mathematical models for analysis. We demonstrate the use of BioPEPA on two biological examples.
Rulebased modelling and model perturbation
 In Transactions on Computational Systems Biology
, 2009
"... Abstract. Rulebased modelling has already proved to be successful for taming the combinatorial complexity, typical of cellular signalling networks, caused by the combination of physical proteinprotein interactions and modifications that generate astronomical numbers of distinct molecular species. ..."
Abstract

Cited by 9 (4 self)
 Add to MetaCart
(Show Context)
Abstract. Rulebased modelling has already proved to be successful for taming the combinatorial complexity, typical of cellular signalling networks, caused by the combination of physical proteinprotein interactions and modifications that generate astronomical numbers of distinct molecular species. However, traditional rulebased approaches, based on an unstructured space of agents and rules, remain susceptible to other combinatorial explosions caused by mutated and/or splice variant agents, that share most but not all of their rules with their wildtype counterparts; and by drugs, which must be clearly distinguished from physiological ligands. In this paper, we define a syntactic extension of Kappa, an established rulebased modelling platform, that enables the expression of a structured space of agents and rules that allows us to express mutated agents, splice variants, families of related proteins and ligand/drug interventions uniformly. This also enables a mode of model construction where, starting from the current consensus model, we attempt to reproduce in numero the mutational—and more generally the ligand/drug perturbational—analyses that were used in the process of inferring those pathways in the first place. 1
Biochemical reaction rules with constraints
 OF LECTURE NOTES IN COMPUTER SCIENCE
, 2011
"... We propose React(C), an expressive programming language for stochastic modeling and simulation in systems biology that is based on biochemical reactions with constraints. We prove that React(C) can express the stochastic picalculus, in contrast to previous rulebased programming languages, and fur ..."
Abstract

Cited by 7 (3 self)
 Add to MetaCart
We propose React(C), an expressive programming language for stochastic modeling and simulation in systems biology that is based on biochemical reactions with constraints. We prove that React(C) can express the stochastic picalculus, in contrast to previous rulebased programming languages, and further illustrate the high expressiveness of React(C). We present a stochastic simulator for React(C) independently of the choice of the constraint language C. Our simulator decides for a given reaction rule whether it can be applied to the current biochemical solution. We show that this decision problem is NPcomplete for arbitrary constraint systems C and that it can be solved in polynomial time for rules of bounded arity. In practice, we propose to solve this problem by constraint programming.
A Process Model of Actin Polymerisation
 FBTC 2008
, 2008
"... Actin is the monomeric subunit of actin filaments which form one of the three major cytoskeletal networks in eukaryotic cells. Actin dynamics, be it the polymerisation of actin monomers into filaments or the reverse process, plays a key role in many cellular activities such as cell motility and phag ..."
Abstract

Cited by 7 (3 self)
 Add to MetaCart
Actin is the monomeric subunit of actin filaments which form one of the three major cytoskeletal networks in eukaryotic cells. Actin dynamics, be it the polymerisation of actin monomers into filaments or the reverse process, plays a key role in many cellular activities such as cell motility and phagocytosis. There is a growing number of experimental, theoretical and mathematical studies on the components of actin polymerisation and depolymerisation. However, it remains a challenge to develop compositional models of actin dynamics, e.g., by using differential equations. In this paper, we propose compositional process algebra models of actin polymerisation, and present a geometric representation of these models that allows to generate movies reflecting their dynamics.