Development and validation of computational models of cellular interaction

In this paper we take the view that computational models of biological systems should satisfy two conditions – they should be able to predict function at a systems biology level, and robust techniques of validation against biological models must be available. A modelling paradigm for developing a predictive computational model of cellular interaction is described, and methods of providing robust validation against biological models are explored, followed by a consideration of software issues

Hybrid automata dicretising agents for formal modelling of robots

Some of the fundamental capabilities required by autonomous vehicles and systems for their intelligent decision making are: modelling of the environment and forming data abstractions for symbolic, logic based reasoning. The paper formulates a discrete agent framework that abstracts and controls a hybrid system that is a composition of hybrid automata modelled continuous individual processes. Theoretical foundations are laid down for a class of general model composition agents (MCAs) with an advanced subclass of rational physical agents (RPAs). We define MCAs as the most basic structures for the description of complex autonomous robotic systems. The RPA’s have logic based decision making that is obtained by an extension of the hybrid systems concepts using a set of abstractions. The theory presented helps the creation of robots with reliable performance and safe operation in their environment. The paper emphasizes the abstraction aspects of the overall hybrid system that emerges from parallel composition of sets of RPAs and MCAs

Using formal methods to support testing

Formal methods and testing are two important approaches that assist in the development of high quality software. While traditionally these approaches have been seen as rivals, in recent years a new consensus has developed in which they are seen as complementary. This article reviews the state of the art regarding ways in which the presence of a formal specification can be used to assist testing

A multidisciplinary survey of modeling techniques for biochemical networks

All processes of life are dominated by networks of interacting biochemical components. The purpose of modeling these networks is manifold. From a theoretical point of view it allows the exploration of network structures and dynamics, to find emergent properties or to explain the organization and evolution of networks. From a practical point of view, in silico experiments can be performed that would be very expensive or impossible to achieve in the laboratory, such as hypothesis-testing with regard to knockout experiments or overexpression, or checking the validity of a proposed molecular mechanism. The literature on modeling biochemical networks is growing rapidly and the motivations behind different modeling techniques are sometimes quite distant from each other. To clarify the current context, we present a systematic overview of the different philosophies to model biochemical networks. We put particular emphasis on three main domains which have been playing a major role in the past, namely: mathematics with ordinary and partial differential equations, statistics with stochastic simulation algorithms, Bayesian networks and Markov chains, and the field of computer science with process calculi, term rewriting systems and state based systems. For each school, we evaluate advantages and disadvantages such as the granularity of representation, scalability, accessibility or availability of analysis tools. Following this, we describe how one can combine some of those techniques and thus take advantages of several techniques through the use of bridging tools. Finally, we propose a next step for modeling biochemical networks by using artificial chemistries and evolutionary computation. This work was funded by ESIGNET (Evolving Cell Signaling Networks in Silico), an European Integrated Project in the EU FP6 NEST Initiative (contract no. 12789)

Formal Modeling of Connectionism using Concurrency Theory, an Approach Based on Automata and Model Checking

This paper illustrates a framework for applying formal methods techniques, which are symbolic in nature, to specifying and verifying neural networks, which are sub-symbolic in nature. The paper describes a communicating automata [Bowman & Gomez, 2006] model of neural networks. We also implement the model using timed automata [Alur & Dill, 1994] and then undertake a verification of these models using the model checker Uppaal [Pettersson, 2000] in order to evaluate the performance of learning algorithms. This paper also presents discussion of a number of broad issues concerning cognitive neuroscience and the debate as to whether symbolic processing or connectionism is a suitable representation of cognitive systems. Additionally, the issue of integrating symbolic techniques, such as formal methods, with complex neural networks is discussed. We then argue that symbolic verifications may give theoretically well-founded ways to evaluate and justify neural learning systems in the field of both theoretical research and real world applications

Amorphous slicing of extended finite state machines

Slicing is useful for many Software Engineering applications and has been widely studied for three decades, but there has been comparatively little work on slicing Extended Finite State Machines (EFSMs). This paper introduces a set of dependency based EFSM slicing algorithms and an accompanying tool. We demonstrate that our algorithms are suitable for dependence based slicing. We use our tool to conduct experiments on ten EFSMs, including benchmarks and industrial EFSMs. Ours is the first empirical study of dependence based program slicing for EFSMs. Compared to the only previously published dependence based algorithm, our average slice is smaller 40% of the time and larger only 10% of the time, with an average slice size of 35% for termination insensitive slicing

Towards the Correctness of Software Behavior in UML: A Model Checking Approach Based on Slicing

Embedded systems are systems which have ongoing interactions with their environments, accepting requests and producing responses. Such systems are increasingly used in applications where failure is unacceptable: traffic control systems, avionics, automobiles, etc. Correct and highly dependable construction of such systems is particularly important and challenging. A very promising and increasingly attractive method for achieving this goal is using the approach of formal verification. A formal verification method consists of three major components: a model for describing the behavior of the system, a specification language to embody correctness requirements, and an analysis method to verify the behavior against the correctness requirements. This Ph.D. addresses the correctness of the behavioral design of embedded systems, using model checking as the verification technology. More precisely, we present an UML-based verification method that checks whether the conditions on the evolution of the embedded system are met by the model. Unfortunately, model checking is limited to medium size systems because of its high space requirements. To overcome this problem, this Ph.D. suggests the integration of the slicing (reduction) technique