164 research outputs found
Static Safety for an Actor Dedicated Process Calculus by Abstract Interpretation
The actor model eases the definition of concurrent programs with non uniform
behaviors. Static analysis of such a model was previously done in a data-flow
oriented way, with type systems. This approach was based on constraint set
resolution and was not able to deal with precise properties for communications
of behaviors. We present here a new approach, control-flow oriented, based on
the abstract interpretation framework, able to deal with communication of
behaviors. Within our new analyses, we are able to verify most of the previous
properties we observed as well as new ones, principally based on occurrence
counting
Automatic Verification of Erlang-Style Concurrency
This paper presents an approach to verify safety properties of Erlang-style,
higher-order concurrent programs automatically. Inspired by Core Erlang, we
introduce Lambda-Actor, a prototypical functional language with
pattern-matching algebraic data types, augmented with process creation and
asynchronous message-passing primitives. We formalise an abstract model of
Lambda-Actor programs called Actor Communicating System (ACS) which has a
natural interpretation as a vector addition system, for which some verification
problems are decidable. We give a parametric abstract interpretation framework
for Lambda-Actor and use it to build a polytime computable, flow-based,
abstract semantics of Lambda-Actor programs, which we then use to bootstrap the
ACS construction, thus deriving a more accurate abstract model of the input
program. We have constructed Soter, a tool implementation of the verification
method, thereby obtaining the first fully-automatic, infinite-state model
checker for a core fragment of Erlang. We find that in practice our abstraction
technique is accurate enough to verify an interesting range of safety
properties. Though the ACS coverability problem is Expspace-complete, Soter can
analyse these verification problems surprisingly efficiently.Comment: 12 pages plus appendix, 4 figures, 1 table. The tool is available at
http://mjolnir.cs.ox.ac.uk/soter
Rate Equations for Graphs
In this paper, we combine ideas from two different scientific traditions: 1)
graph transformation systems (GTSs) stemming from the theory of formal
languages and concurrency, and 2) mean field approximations (MFAs), a
collection of approximation techniques ubiquitous in the study of complex
dynamics. Using existing tools from algebraic graph rewriting, as well as new
ones, we build a framework which generates rate equations for stochastic GTSs
and from which one can derive MFAs of any order (no longer limited to the
humanly computable). The procedure for deriving rate equations and their
approximations can be automated. An implementation and example models are
available online at https://rhz.github.io/fragger. We apply our techniques and
tools to derive an expression for the mean velocity of a two-legged walker
protein on DNA.Comment: to be presented at the 18th International Conference on Computational
Methods in Systems Biology (CMSB 2020
Toward a comprehensive language for biological systems
Rule-based modeling has become a powerful approach for modeling intracellular networks, which are characterized by rich molecular diversity. Truly comprehensive models of cell behavior, however, must address spatial complexity at both the intracellular level and at the level of interacting populations of cells, and will require richer modeling languages and tools. A recent paper in BMC Systems Biology represents a signifcant step toward the development of a unified modeling language and software platform for the development of multi-level, multiscale biological models
Coarse-grained brownian dynamics simulation of rule-based models
International audienceStudying spatial effects in signal transduction, such as co-localization along scaffold molecules, comes at a cost of complexity. In this paper, we propose a coarse-grained, particle-based spatial simulator, suited for large signal transduction models. Our approach is to combine the particle-based reaction and diffusion method, and (non-spatial) rule-based modeling: the location of each molecular complex is abstracted by a spheric particle, while its internal structure in terms of a site-graph is maintained explicit. The particles diffuse inside the cellular compartment and the colliding complexes stochastically interact according to a rule-based scheme. Since rules operate over molecular motifs (instead of full complexes), the rule set compactly describes a combinatorial or even infinite number of reactions. The method is tested on a model of Mitogen Activated Protein Kinase (MAPK) cascade of yeast pheromone response signaling. Results demonstrate that the molecules of the MAPK cascade co-localize along scaffold molecules, while the scaffold binds to a plasma membrane bound upstream component, localizing the whole signaling complex to the plasma membrane. Especially we show, how rings stabilize the resulting molecular complexes and derive the effective dissociation rate constant for it
Syntactic Markovian Bisimulation for Chemical Reaction Networks
In chemical reaction networks (CRNs) with stochastic semantics based on
continuous-time Markov chains (CTMCs), the typically large populations of
species cause combinatorially large state spaces. This makes the analysis very
difficult in practice and represents the major bottleneck for the applicability
of minimization techniques based, for instance, on lumpability. In this paper
we present syntactic Markovian bisimulation (SMB), a notion of bisimulation
developed in the Larsen-Skou style of probabilistic bisimulation, defined over
the structure of a CRN rather than over its underlying CTMC. SMB identifies a
lumpable partition of the CTMC state space a priori, in the sense that it is an
equivalence relation over species implying that two CTMC states are lumpable
when they are invariant with respect to the total population of species within
the same equivalence class. We develop an efficient partition-refinement
algorithm which computes the largest SMB of a CRN in polynomial time in the
number of species and reactions. We also provide an algorithm for obtaining a
quotient network from an SMB that induces the lumped CTMC directly, thus
avoiding the generation of the state space of the original CRN altogether. In
practice, we show that SMB allows significant reductions in a number of models
from the literature. Finally, we study SMB with respect to the deterministic
semantics of CRNs based on ordinary differential equations (ODEs), where each
equation gives the time-course evolution of the concentration of a species. SMB
implies forward CRN bisimulation, a recently developed behavioral notion of
equivalence for the ODE semantics, in an analogous sense: it yields a smaller
ODE system that keeps track of the sums of the solutions for equivalent
species.Comment: Extended version (with proofs), of the corresponding paper published
at KimFest 2017 (http://kimfest.cs.aau.dk/
Rate Equations for Graphs
International audienceIn this paper, we combine ideas from two different scientifictraditions: 1) graph transformation systems (GTSs) stemming from thetheory of formal languages and concurrency, and 2) mean field approx-imations (MFAs), a collection of approximation techniques ubiquitousin the study of complex dynamics. Using existing tools from algebraicgraph rewriting, as well as new ones, we build a framework which gener-ates rate equations for stochastic GTSs and from which one can deriveMFAs of any order (no longer limited to the humanly computable). Theprocedure for deriving rate equations and their approximations can beautomated. An implementation and example models are available onlineat https://rhz.github.io/fragger. We apply our techniques and tools toderive an expression for the mean velocity of a two-legged walker proteinon DNA
Sharing Ghost Variables in a Collection of Abstract Domains
International audienceWe propose a framework in which we share ghost variables across a collection of abstract domains allowing precise proofs of complex properties. In abstract interpretation, it is often necessary to be able to express complex properties while doing a precise analysis. A way to achieve that is to combine a collection of domains, each handling some kind of properties, using a reduced product. Separating domains allows an easier and more modular implementation, and eases soundness and termination proofs. This way, we can add a domain for any kind of property that is interesting. The reduced product, or an approximation of it, is in charge of refining abstract states, making the analysis precise. In program verification, ghost variables can be used to ease proofs of properties by storing intermediate values that do not appear directly in the execution. We propose a reduced product of abstract domains that allows domains to use ghost variables to ease the representation of their internal state. Domains must be totally agnostic with respect to other existing domains. In particular the handling of ghost variables must be entirely decentralized while still ensuring soundness and termination of the analysis
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