Electronic Notes in Theoretical Computer Science最新文献

Multiparty interactions are common place in today's distributed systems. An agent usually communicates, in a single session, with other agents to accomplish a given task. Take for instance an online transaction including the vendor, the client, the credit card system and the bank. When specifying this kind of system, we probably observe a single transaction including several (binary) communications leading to changes in the state of all the involved agents. Multiway synchronization process calculi, that move from a binary to a multiparty synchronization discipline, have been proposed to formally study the behavior of those systems. However, adopting models such as Bodei, Brodo, and Bruni's Core Network Algebra (CNA), where the number of participants in an interaction is not fixed a priori, leads to an exponential blow-up in the number of states/behaviors that can be observed from the system. In this paper we explore mechanisms to tackle this problem. We extend CNA with constraints that declaratively allow the modeler to restrict the interaction that should actually happen. Our extended process algebra, called CCNA, finds application in balancing the interactions in a concurrent system, leading to a simple, deadlock-free and fair solution for the Dinning Philosopher problem. Our definition of constraints is general enough and it offers the possibility of accumulating costs in a multiparty negotiation. Hence, only computations respecting the thresholds imposed by the modeler are observed. We use this machinery to neatly model a Service Level Agreement protocol. We develop the theory of CCNA including its operational semantics and a behavioral equivalence that we prove to be a congruence. We also propose a prototypical implementation that allows us to verify, automatically, some of the systems explored in the paper.

The Jacobian matrix of a dynamic system and its principal minors play a prominent role in the study of qualitative dynamics and bifurcation analysis. When interpreting the Jacobian as an adjacency matrix of an interaction graph, its principal minors reate to sets of disjoint cycles in this graph and conditions for qualitative dynamic behaviors can be inferred from its cycle structure. The Jacobian of chemical reaction systems decomposes into the product of two matrices, which allows more fine-grained analyses by studying a corresponding bipartite species-reaction graph. Several different bipartite graphs have been proposed and results on injectivity, multistationarity, and bifurcations have been derived. Here, we present a new definition of the species-reaction graph that directly connects the cycle structure with determinant expansion terms, principal minors, and the coefficients of the characteristic polynomial. It encompasses previous graph constructions as special cases. This graph has a direct relation to the interaction graph, and properties of cycles and sub-graphs can be translated in both directions. A simple equivalence relation enables simplified decomposition of determinant expansions and allows simpler and more direct proofs of previous results.

The bond-calculus is a language for modelling interactions between continuous populations of biomolecular agents. The calculus combines process-algebra descriptions of individual agent behaviour with affinity patterns, which can specify a wide variety of patterns of interactions between the sites of different agents. These affinity patterns extend binary molecular affinities to multiway reactions, general kinetic laws, and cooperative interactions. In this paper we explore bond-calculus modelling of gene regulation at both the molecular and network levels. At the molecular level, we show how affinity patterns can succinctly describe the λ-switch, a prototypical example of cooperative regulation. Moving to the network level, we develop a general model of gene regulatory networks using affinity patterns and an expanded Hill kinetic law. We illustrate the approach with a specific example: the complex plant circadian clock. We analyse these models via the bond-calculus's differential equation and stochastic semantics, and validate our results against existing models from the literature.

We present the second generation of a rule-based language called Biochemical Space Language (BCSL) that combines the advantages of different approaches and thus makes an effort to overcome several problems with existing solutions. The key aspect of the language is the level of abstraction it uses, which allows scalable and compact hierarchical specification of biochemical entities. This abstraction enables unique analysis techniques to reason about properties of models written in the language on the semantic and syntactic level.

Rule-based languages, such as Kappa and BNGL, allow for the description of very combinatorial models of interactions between proteins. A huge (when not infinite) number of different kinds of bio-molecular compounds may arise due to proteins with multiple binding and phosphorylation sites. Knowing beforehand whether a model may involve an infinite number of different kinds of bio-molecular compounds is crucial for the modeller. On the first hand, having an infinite number of kinds of bio-molecular compounds is sometimes a hint for modelling flaws: forgetting to specify the conflicts among binding rules is a common mistake. On the second hand, it impacts the choice of the semantics for the models (among stochastic, differential, hybrid).

In this paper, we introduce a data-structure to abstract the potential unbounded polymers that may be formed in a rule-based model. This data-structure is a graph, the nodes and the edges of which are labelled with patterns. By construction, every potentially unbounded polymer is associated to at least one cycle in that graph. This data-structure has two main advantages. Firstly, as opposed to site-graphs, one can reason about cycles without enumerating them (by the means of Tarjan's algorithm for detecting strongly connected components). Secondly, this data-structures may be combined easily with information coming from additional reachability analysis: the edges that are labelled with an overlap that is proved unreachable in the model may be safely discarded.

Biochemical molecules interact through modification and binding reactions, giving raise to a combinatorial number of possible biochemical species. The time-dependent evolution of concentrations of the species is commonly described by a system of coupled ordinary differential equations (ODEs). However, the analysis of such high-dimensional, non-linear system of equations is often computationally expensive and even prohibitive in practice. The major challenge towards reducing such models is providing the guarantees as to how the solution of the reduced model relates to that of the original model, while avoiding to solve the original model.

In this paper, we have designed and tested an approximation method for ODE models of biochemical reaction systems, in which the guarantees are our major requirement. Borrowing from tropical analysis techniques, we look at the dominance relations among terms of each species' ODE. These dominance relations can be exploited to simplify the original model, by neglecting the dominated terms. As the dominant subsystems can change during the system's dynamics, depending on which species dominate the others, several possible modes exist. Thus, simpler models consisting of only the dominant subsystems can be assembled into hybrid, piecewise smooth models, which approximate the behavior of the initial system. By combining the detection of dominated terms with symbolic bounds propagation, we show how to approximate the original model by an assembly of simpler models, consisting in ordinary differential equations that provide time-dependent lower and upper bounds for the concentrations of the initial model's species.

The utility of our method is twofold. On the one hand, it provides a reduction heuristics that performs without any prior knowledge of the initial system's behavior (i.e., no simulation of the initial system is needed in order to reduce it). On the other hand, our method provides sound interval bounds for each species, and hence can serve to evaluate the faithfulness of tropicalization reduction heuristics for ODE models of biochemical reduction systems. The method is tested on several case studies.

This paper focuses on a major improvement on the analysis of reachability properties in large-scale dynamical biological models. To tackle such models, where classical model checkers fail due to state space explosion led by exhaustive search. Alternative static analysis approaches have been proposed, but they may also fail in certain cases due to non-exhaustive search. In this paper, we introduce a hybrid approach ASPReach, which combines static analysis and stochastic search to break the limits of both approaches. We tackle this issue on a modeling framework we recently introduced, Asynchronous Binary Automata Network (ABAN). We show that ASPReach is able to analyze efficiently some reachability properties which could not be solved by existing methods. We studied also various cases from biological literature, emphasizing the merits of our approach in terms of conclusiveness and performance.

We address the problem of selecting a model from a list of potential models in the field of dynamical systems. The selection is based on model behaviour specified in temporal logic rather than time series. This provides more global constraints on the system dynamics. Not only to select one model but also to create an ordered structure, we propose the model ordering problem. We suggest and apply several ordering relations comparing models given property specification. To provide a formal method with global results for the proposed setting we employ and adapt model checking and parameter synthesis methods. To evaluate the method, we apply the proposed method to several qualitative models of regulatory networks.

APUs (Accelerated Processing Units) are widely available in personal computers as low-cost processors that have a CPU and an integrated GPU for displaying graphics, in the same die. Using a ray tracing algorithm as a computationally intensive workload and taking advantage of the APU specific characteristics, we compare the performance of this SoC against CPU and GPU solutions in the same price range.

The Robust Transform based on the Weighted Median operator algorithm calculates the transform of a signal when it has been exposed to impulsive noise. Since this algorithm demands very long execution time, it is not useful for real time signal processing systems. In this context, this work presents several strategies to improve its performance, such as the reduction of redundant calculations, optimization in the memory access, and a multithreads version of the algorithm. Besides, the original estimation method is modified to decrease even more the average execution time, keeping the quality level of the numeric results. The experimental results show a 30% performance improvement by reducing redundant calculations and optimizing the memory access, without making modifications to the estimation method and without using multi-threaded processing; 93% performance improvement by introducing modifications to the estimation method; and 97% performance improvement by incorporating the multi-threaded processing.