A Graphical Representation for Biological Processes in the Stochastic pi-Calculus (original) (raw)

Simulating Biological Systems in the Stochastic Pi-calculus

2004

This course presents a programming language for designing and simulating com- puter models of biological systems. The language is based on a mathematical formalism known as the pi-calculus, and the simulation algorithm is based on standard kinetic theory of physical chemistry. The language will first be presented using a simple graphical notation, and will subsequently be used to model and

An Intuitive Automated Modelling Interface for Systems Biology

We introduce a natural language interface for building stochastic pi calculus models of biological systems. In this language, complex constructs describing biochemical events are built from basic primitives of association, dissociation and transformation. This language thus allows us to model biochemical systems modularly by describing their dynamics in a narrative-style language, while making amendments, refinements and extensions on the models easy. We demonstrate the language on a model of Fc-gamma receptor phosphorylation during phagocytosis. We provide a tool implementation of the translation into a stochastic pi calculus language, Microsoft Research's SPiM.

Modelling the Structure and Dynamics of Biological Pathways

PLOS Biology, 2016

There is a need for formalised diagrams that both summarise current biological pathway knowledge and support modelling approaches that explain and predict their behaviour. Here, we present a new, freely available modelling framework that includes a biologistfriendly pathway modelling language (mEPN), a simple but sophisticated method to support model parameterisation using available biological information; a stochastic flow algorithm that simulates the dynamics of pathway activity; and a 3-D visualisation engine that aids understanding of the complexities of a system's dynamics. We present example pathway models that illustrate of the power of approach to depict a diverse range of systems.

Biocharts: A Visual Formalism for Complex Biological Systems

2010

We address one of the central issues in devising languages, methods and tools for the modelling and analysis of complex biological systems, that of linking high-level (e.g. intercellular) information with lower-level (e.g. intracellular) information. Adequate ways of dealing with this issue are crucial for understanding biological networks and pathways, which typically contain huge amounts of data that continue to grow as our knowledge and understanding of a system increases. Trying to comprehend such data using the standard methods currently in use is often virtually impossible. We propose a two-tier compound visual language, which we call Biocharts, that is geared towards building fully executable models of biological systems. One of the main goals of our approach is to enable biologists to actively participate in the computational modelling effort, in a natural way. The high-level part of our language is a version of statecharts, which have been shown to be extremely successful in software and systems engineering. The statecharts can be combined with any appropriately well-defined language ( preferably a diagrammatic one) for specifying the low-level dynamics of the pathways and networks. We illustrate the language and our general modelling approach using the well-studied process of bacterial chemotaxis.

Using process diagrams for the graphical representation of biological networks

2005

With the increased interest in understanding biological networks, such as protein-protein interaction networks and gene regulatory networks, methods for representing and communicating such networks in both human-and machine-readable form have become increasingly important. Although there has been significant progress in machine-readable representation of networks, as exemplified by the Systems Biology Mark-up Language (SBML) (http://www.sbml.org) issues in humanreadable representation have been largely ignored. This article discusses human-readable diagrammatic representations and proposes a set of notations that enhances the formality and richness of the information represented. The process diagram is a fully state transition-based diagram that can be translated into machine-readable forms such as SBML in a straightforward way. It is supported by CellDesigner, a diagrammatic network editing software (http://www.celldesigner.org/), and has been used to represent a variety of networks of various sizes (from only a few components to several hundred components).

Gene regulation in the pi calculus: simulating cooperativity at the Lambda switch

Tcsb, 2004

We propose to model the dynamics of gene regulatory networks as concurrent processes in the stochastic pi calculus. As a first case study, we show how to express the control of transcription initiation at the lambda switch, a prototypical example where cooperative enhancement is crucial. This requires concurrent programming techniques that are new to systems biology, and necessitates stochastic parameters that we derive from the literature. We test all components of our model by exhaustive stochastic simulations. A comparison with previous results reported in the literature, experimental and simulation based, confirms the appropriateness of our modeling approach. Interdisciplinary Research Institute, FRE 2963 of CNRS, in cooperation with the University of Lille 1 and supported by the Conseil Régional Nord-Pas de Calais. Mostrare Project of INRIA Futurs at the LIFL, in cooperation with the Universities of Lille 1 and 3. systems they have been determined in series of experiments, and reported in the research literature.

Stochastic Calculus of Looping Sequences for the Modelling and Simulation of Cellular Pathways

Transactions on Computational Systems Biology, 2008

The paper presents the Stochastic Calculus of Looping Se- quences (SCLS) suitable to describe microbiological systems, such as cel- lular pathways, and their evolution. Systems are represented by terms. The terms of the calculus are constructed by basic constituent elements and operators of sequencing, looping, containment and parallel compo- sition. The looping operator allows tying up the ends of a

Modelling the dynamics of biosystems

Briefings in Bioinformatics, 2004

The need for a more formal handling of biological information processing with stochastic and mobile process algebras is addressed. Biology can benefit this approach, yielding a better understanding of behavioural properties of cells, and computer science can benefit this approach, obtaining new computational models inspired by nature.

Simplifying the Stochastic Petri Nets Formalism for Representing Biological Phenomena

This paper proposes a simplification of the stochastic Petri nets graphical notation with the purpose of defining a more compact and clearer graphical way of building formal models of biological phenomena. Three biological examples are first presented, then modeled with the classical SPN modeling formalism, and their key modeling patterns distilled to identify the main features that need to be represented in a stochastic model. The key features are then the object of the original part of the paper, in which a simplified and more concise, although formal, graphical notation, is proposed, and applied to the selected examples. The paper demonstrates the effectiveness of the simplified notation in producing more compact and understandable models of biological phenomena, still retaining the nice properties of Stochastic Petri Nets, i.e., their flexible abstraction level and formal semantics.

Formal Executable Descriptions of Biological Systems

2005

The similarities between systems of living entities and systems of concurrent processes may support biological experiments in silico. Process calculi offer a formal framework to describe biological systems, as well as to analyse their behaviour, both from a qualitative and a quantitative point of view. A couple of little examples help us in showing how this can be done. We mainly focus our attention on the qualitative and quantitative aspects of the considered biological systems, and briefly illustrate which kinds of analysis are possible. We use a known stochastic calculus for the first example. We then present some statistics collected by repeatedly running the specification, that turn out to agree with those obtained by experiments in vivo. Our second example motivates a richer calculus. Its stochastic extension requires a non trivial machinery to faithfully reflect the real dynamic behaviour of biological systems.