A Calculus for Modelling, Simulating and Analysing Compartmentalized Biological Systems (original) (raw)
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Bioinformatics, 2007
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2005
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Arxiv preprint arXiv:0911.4513, 2009
We introduce the Bioβ Framework, a meta-model for both protein-level and membrane-level interactions of living cells. This formalism aims to provide a formal setting where to encode, compare and merge models at different abstraction levels; in particular, higher-level (e.g. membrane) activities can be given a formal biological justification in terms of low-level (i.e., protein) interactions.
A simple calculus for proteins and cells
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AMB Express, 2011
Systems Biology has taken advantage of computational tools and high-throughput experimental data to model several biological processes. These include signaling, gene regulatory, and metabolic networks. However, most of these models are specific to each kind of network. Their interconnection demands a whole-cell modeling framework for a complete understanding of cellular systems. We describe the features required by an integrated framework for modeling, analyzing and simulating biological processes, and review several modeling formalisms that have been used in Systems Biology including Boolean networks, Bayesian networks, Petri nets, process algebras, constraint-based models, differential equations, rule-based models, interacting state machines, cellular automata, and agent-based models. We compare the features provided by different formalisms, and discuss recent approaches in the integration of these formalisms, as well as possible directions for the future.
Modelling Biological Compartments in Bio-PEPA
Electronic Notes in Theoretical Computer Science, 2009
Compartments and membranes play an important role in cell biology. Therefore it is highly desirable to be able to represent them in modelling languages for biology. Bio-PEPA is a language for the modelling and analysis of biochemical networks; in its present version compartments can be defined but they are only used as labels to express the location of molecular species. In this work we present an extension of Bio-PEPA with some features in order to represent more details about locations of species and reactions. With the term location we mean either a membrane or a compartment. We describe how models involving compartments and membranes can be expressed in the language and, consequently, analysed. We limit our attention to static locations (i.e. whose structure is fixed) whose size can depend on time. We illustrate our approach via a classical model used to represent intracellular Ca 2+ oscillations.
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Abstract This report introduces some of the ongoing work in the application of process algebra in systems biology. This survey reveals that although progress has been made in using process algebra to create stochastic simulations of the systems in question, no work has been done to emphasise the importance of refinement theory. A brief description of the notation and some of the semantics of Communicating Sequential Processes, a process algebra, are presented, with the aim of applying refinement in biological models.
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Process algebras are widely used for defining the formal semantics of concurrent communicating processes. In process algebra, concurrent processes can be specified to execute distinct programs. Processes can also communicate repeatedly with other processes via handshake communication protocols that requires acknowledgment of message reception. Furthermore, processes can branch into multiple processes. This paper considers stochastic π-calculus which is a particularly expressive kind of process algebra providing a specification of probabilities of process behavior such as stochastic delays, communication and branching, as well as rates of execution. Previously, stochastic π-calculus has been used to specify a wide variety of chemical and biological processes. In this paper, we implement stochastic π-calculus at the molecular 1 Motivation
Process Calculi Abstractions for Biology
Natural Computing Series, 2009
Several approaches have been proposed to model biological systems by means of the formal techniques and tools available in computer science. To mention just a few of them, some representations are inspired by Petri Nets theory, and some other by stochastic processes. A most recent approach consists in interpreting the living entities as terms of process calculi where the behavior of the represented systems can be inferred by applying syntaxdriven rules.