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Stochastic Simulation of Process Calculi for Biology
"... Biological systems typically involve large numbers of components with complex, highly parallel interactions and intrinsic stochasticity. To model this complexity, numerous programming languages based on process calculi have been developed, many of which are expressive enough to generate unbounded nu ..."
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Biological systems typically involve large numbers of components with complex, highly parallel interactions and intrinsic stochasticity. To model this complexity, numerous programming languages based on process calculi have been developed, many of which are expressive enough to generate unbounded numbers of molecular species and reactions. As a result of this expressiveness, such calculi cannot rely on standard reactionbased simulation methods, which require fixed numbers of species and reactions. Rather than implementing custom stochastic simulation algorithms for each process calculus, we propose to use a generic abstract machine that can be instantiated to a range of process calculi and a range of reactionbased simulation algorithms. The abstract machine functions as a justintime compiler, which dynamically updates the set of possible reactions and chooses the machine, and show how it can be instantiated with the stochastic simulation algorithm known as Gillespie’s Direct Method. We also discuss the wider implications of such an abstract machine, and outline how it can be used to simulate multiple calculi simultaneously within a common framework. 1
Types for BioAmbients
 In FBTC’10, volume 19 of EPTCS
, 2010
"... The BioAmbients calculus is a process algebra suitable for representing compartmentalization, molecular localization and movements between compartments. In this paper we enrich this calculus with a static type system classifying each ambient with group types specifying the kind of compartments in wh ..."
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The BioAmbients calculus is a process algebra suitable for representing compartmentalization, molecular localization and movements between compartments. In this paper we enrich this calculus with a static type system classifying each ambient with group types specifying the kind of compartments in which the ambient can stay. The type system ensures that, in a welltyped process, ambients cannot be nested in a way that violates the type hierarchy. Exploiting the information given by the group types, we also extend the operational semantics of BioAmbients with rules signalling errors that may derive from undesired ambients ’ moves (i.e. merging incompatible tissues). Thus, the signal of errors can help the modeller to detect and locate unwanted situations that may arise in a biological system, and give practical hints on how to avoid the undesired behaviour. 1
Genetic networks described in stochastic Pi Machine (SPiM) programming language: compositional design
 Journal of Computer Science & Systems Biology
"... If biological objects are created by natural selection, why are they composed of discrete modules? What has been the nature of mutations since the Darwinian epoch? This paper presents examples of genetic circuits in terms of stochastic picalculus; a new mathematical language for nanosystems. The au ..."
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If biological objects are created by natural selection, why are they composed of discrete modules? What has been the nature of mutations since the Darwinian epoch? This paper presents examples of genetic circuits in terms of stochastic picalculus; a new mathematical language for nanosystems. The author used a constructor of five elements such as decay, null gate, gene product, and negative and positive gates. These primitives were applied to design genetic switches, oscillators, feedforward and feedback loops, pulse generators, memory elements, and combinatorial logics. The behaviors of those circuits were investigated – functions, such as oscillations or a spontaneous pulse generation were performed simply, flipflops between stable states occurred in the noisy environment. The modular essence of picalculus and the following up features of Stochastic Pi Machine (SPiM) programming language allowed us to change the topology of networks that resembled a gene exchange in nature. Other types of mutations were considered as variations in parameters. Perturbations modified system behavior in unpredictable ways that generated diversity for a possible future design by selection of appropriative variants. Research Article OPEN ACCESS Freely available online doi:10.4172/jcsb.1000042
Dynamic Compartments in the Imperative πCalculus
, 2009
"... Dynamic compartments with mutable configurations and variable volumes are of basic interest for the stochastic modeling of biochemistry in cells. We propose a new language to express dynamic compartments that we call the imperative πcalculus. It is obtained from the attributed πcalculus by adding ..."
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Dynamic compartments with mutable configurations and variable volumes are of basic interest for the stochastic modeling of biochemistry in cells. We propose a new language to express dynamic compartments that we call the imperative πcalculus. It is obtained from the attributed πcalculus by adding imperative assignment operations to a global store. Previous approaches to dynamic compartments are improved in flexibility or efficiency. This is illustrated by an appropriate model of osmosis and a correct encoding of BioAmbients.
A Visual Process Calculus for Biology
"... Abstract. This chapter presents a visual process calculus for designing and simulating computer models of biological systems. The calculus is based on a graphical variant of stochastic picalculus, extended with mobile compartments, and the simulation algorithm is based on standard kinetic theory of ..."
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Abstract. This chapter presents a visual process calculus for designing and simulating computer models of biological systems. The calculus is based on a graphical variant of stochastic picalculus, extended with mobile compartments, and the simulation algorithm is based on standard kinetic theory of physical chemistry. The calculus forms the basis of a formal visual programming language for biology. The basic primitives of the calculus are first introduced by a series of examples involving genes and proteins. More complex features of the calculus are then illustrated by examples involving gene networks, cell differentiation, and immune system response. The main benefit of the calculus is its ability to model large systems incrementally, by directly composing simpler models of subsystems. The formal nature of the calculus also facilitates mathematical analysis of models, which in future could help provide insight into In many respects, biological systems are like massively parallel, highly complex,
Stochastic Simulation of Multiple Process Calculi for Biology
"... Numerous programming languages based on process calculi have been developed for biological modelling, many of which can generate potentially unbounded numbers of molecular species and reactions. As a result, such languages cannot rely on standard reactionbased simulation methods, and are generally ..."
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Numerous programming languages based on process calculi have been developed for biological modelling, many of which can generate potentially unbounded numbers of molecular species and reactions. As a result, such languages cannot rely on standard reactionbased simulation methods, and are generally implemented using custom stochastic simulation algorithms. As an alternative, this paper proposes a generic abstract machine that can be instantiated to simulate machine functions as a justintime compiler, which dynamically updates the set of possible reactions and chooses the next reaction in an iterative cycle. We instantiate the generic abstract machine with two Markovian simulation methods and provide encodings for four process calculi: the agentbased picalculus, the compartmentbased bioambient calculus, the rulebased kappa calculus and the domainspecific DNA strand displacement calculus. We present a generic method for proving that the encoding of an arbitrary process calculus into the abstract machine is correct, and we use this method to prove the correctness of all four calculus encodings. Finally, we demonstrate how the generic abstract machine can be used to simulate heterogeneous models in which discrete communicating submodels are written using different domainspecific languages and then simulated together. Our approach forms the basis of a multilanguage environment for the simulation of heterogeneous biological models.
A COMPOSITIONAL APPROACH FOR MODELING AND SIMULATION OF BIOMOLECULAR SYSTEMS
"... The simulation of biological systems is a challenge to modeling methodologies. Living entities are supported by a complex hierarchical network of chemical reactions. The accurate representation of organisms require the use of stochastic chemical equations (SCE) organized into compartments in order t ..."
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The simulation of biological systems is a challenge to modeling methodologies. Living entities are supported by a complex hierarchical network of chemical reactions. The accurate representation of organisms require the use of stochastic chemical equations (SCE) organized into compartments in order to reflect the organization into cells, tissues and organs. In this paper we introduce a modeling and simulation framework based on the Heterogeneous Flow System Specification formalism (HFSS). HFSS provides an hybrid representation of dynamic systems, being able to describe sampled and discrete event systems. These features enable a modular representation of stochastic chemical reactions. In particular, we show that SCE require only two types of HFSS models defined in this paper: molecule holders, and chemical reactors. We provide a description of a HFSS implementation based on pluggable software units (PUs), a componentbased framework that supports the development of SCE libraries. 1
Systems Biology of Antigen Processing: From Structures to Mechanisms
"... in the endoplasmic reticulum (ER) of cells. They are unstable in this empty ‘open ’ conformation. MHC Class I form a stable ‘closed ’ conformation when bound to an antigen peptide and can travel to the cell surface. ..."
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in the endoplasmic reticulum (ER) of cells. They are unstable in this empty ‘open ’ conformation. MHC Class I form a stable ‘closed ’ conformation when bound to an antigen peptide and can travel to the cell surface.
This work is licensed under the Creative Commons Attribution License. Types for BioAmbients∗
"... The BioAmbients calculus is a process algebra suitable for representing compartmentalization, molecular localization and movements between compartments. In this paper we enrich this calculus with a static type system classifying each ambient with group types specifying the kind of compartments in w ..."
Abstract
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The BioAmbients calculus is a process algebra suitable for representing compartmentalization, molecular localization and movements between compartments. In this paper we enrich this calculus with a static type system classifying each ambient with group types specifying the kind of compartments in which the ambient can stay. The type system ensures that, in a welltyped process, ambients cannot be nested in a way that violates the type hierarchy. Exploiting the information given by the group types, we also extend the operational semantics of BioAmbients with rules signalling errors that may derive from undesired ambients ’ moves (i.e. merging incompatible tissues). Thus, the signal of errors can help the modeller to detect and locate unwanted situations that may arise in a biological system, and give practical hints on how to avoid the undesired behaviour. 1
This work is licensed under the Creative Commons Attribution License. Stochastic Simulation of Process Calculi for Biology
"... Biological systems typically involve large numbers of components with complex, highly parallel interactions and intrinsic stochasticity. To model this complexity, numerous programming languages based on process calculi have been developed, many of which are expressive enough to generate unbounded nu ..."
Abstract
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Biological systems typically involve large numbers of components with complex, highly parallel interactions and intrinsic stochasticity. To model this complexity, numerous programming languages based on process calculi have been developed, many of which are expressive enough to generate unbounded numbers of molecular species and reactions. As a result of this expressiveness, such calculi cannot rely on standard reactionbased simulation methods, which require fixed numbers of species and reactions. Rather than implementing custom stochastic simulation algorithms for each process calculus, we propose to use a generic abstract machine that can be instantiated to a range of process calculi and a range of reactionbased simulation algorithms. The abstract machine functions as a justintime compiler, which dynamically updates the set of possible reactions and chooses the machine, and show how it can be instantiated with the stochastic simulation algorithm known as Gillespie’s Direct Method. We also discuss the wider implications of such an abstract machine, and outline how it can be used to simulate multiple calculi simultaneously within a common framework. 1