An Abstract Machine for the Stochastic Bioambient calculus

AbstractThis paper presents an abstract machine for the stochastic bioambient calculus. The abstract machine is proved sound and complete with respect to a novel stochastic semantics, and is also shown to preserve the reduction probabilities of the calculus. The machine is implemented as an extension to an existing simulator for stochastic pi-calculus

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 numbers of molecular species and reactions. As a result of this expressiveness, such calculi cannot rely on standard reaction-based 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 reaction-based simulation algorithms. The abstract machine functions as a just-in-time compiler, which dynamically updates the set of possible reactions and chooses the next reaction in an iterative cycle. In this short paper we give a brief summary of the generic abstract 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.Comment: In Proceedings MeCBIC 2010, arXiv:1011.005

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 which the ambient can stay. The type system ensures that, in a well-typed 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

Dynamic Compartments in the Imperative Pi Calculus

International audienceDynamic 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 π-calculus. It is obtained from the attributed π-calculus 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

Continuous-time temporal logic specification and verification for nonlinear biological systems in uncertain contexts

In this thesis we introduce a complete framework for modelling and verification of biological systems in uncertain contexts based on the bond-calculus process algebra and the LBUC spatio-temporal logic. The bond-calculus is a biological process algebra which captures complex patterns of interaction based on affinity patterns, a novel communication mechanism using pattern matching to express multiway interaction affinities and general kinetic laws, whilst retaining an agent-centric modelling style for biomolecular species. The bond-calculus is equipped with a novel continuous semantics which maps models to systems of Ordinary Differential Equations (ODEs) in a compositional way. We then extend the bond-calculus to handle uncertain models, featuring interval uncertainties in their species concentrations and reaction rate parameters. Our semantics is also extended to handle uncertainty in every aspect of a model, producing non-deterministic continuous systems whose behaviour depends either on time-independent uncertain parameters and initial conditions, corresponding to our partial knowledge of the system at hand, or time-varying uncertain inputs, corresponding to genuine variability in a system’s behaviour based on environmental factors. This language is then coupled with the LBUC spatio-temporal logic which combines Signal Temporal Logic (STL) temporal operators with an uncertain context operator which quantifies over an uncertain context model describing the range of environments over which a property must hold. We develop model-checking procedures for STL and LBUC properties based on verified signal monitoring over flowpipes produced by the Flow* verified integrator, including the technique of masking which directs monitoring for atomic propositions to time regions relevant to the overall verification problem at hand. This allows us to monitor many interesting nested contextual properties and frequently reduces monitoring costs by an order of magnitude. Finally, we explore the technique of contextual signal monitoring which can use a single Flow* flowpipe representing a functional dependency to complete a whole tree of signals corresponding to different uncertain contexts. This allows us to produce refined monitoring results over the whole space and to explore the variation in system behaviour in different contexts