A framework for modelling Molecular Interaction Maps

Metabolic networks, formed by a series of metabolic pathways, are made of intracellular and extracellular reactions that determine the biochemical properties of a cell, and by a set of interactions that guide and regulate the activity of these reactions. Most of these pathways are formed by an intricate and complex network of chain reactions, and can be represented in a human readable form using graphs which describe the cell cycle checkpoint pathways. This paper proposes a method to represent Molecular Interaction Maps (graphical representations of complex metabolic networks) in Linear Temporal Logic. The logical representation of such networks allows one to reason about them, in order to check, for instance, whether a graph satisfies a given property $\phi$, as well as to find out which initial conditons would guarantee $\phi$, or else how can the the graph be updated in order to satisfy $\phi$. Both the translation and resolution methods have been implemented in a tool capable of addressing such questions thanks to a reduction to propositional logic which allows exploiting classical SAT solvers.Comment: 31 pages, 12 figure

Formal methods for dynamical systems : 13th International School on Formal Methods for the Design of Computer, Communication, and Software Systems, SFM 2013, Bertinoro, Italy, June 17-22, 2013 : advanced lectures

Preface. This volume presents a set of papers accompanying the lectures of the 13th International School on Formal Methods for the Design of Computer, Communication, and Software Systems (SFM). This series of schools addresses the use of formal methods in computer science as a prominent approach to the rigorous design of the above-mentioned systems. The main aim of the SFM series is to offer a good spectrum of current research in foundations as well as applications of formal methods, which can be of help for graduate students and young researchers who intend to approach the field. SFM 2013 was devoted to dynamicalsystems and covered several topics including chaotic dynamics, information theory, systems biology, hybrid systems, quantum computing, and automata-based models and model checking. The five papers collected in this volume represent the broad range of topics of the school. The paper by Köpf and Rybalchenko addresses the automation of the analysis of quantitative information-theoretic confidentiality properties through approximation and randomization techniques. Gratie, Iancu, and Petre introduce some of the basics of modeling with ODEs in biology by focussing on computational, numerical techniques for reaction-based models. The paper by Brim, Ceska, and Safranek presents a selection of approaches used for modeling biological systems and formalizing their interesting properties in temporal logics, together with high-performance model-checking techniques. Bortolussi and Hillston describe recent work on the use of fluid approximation techniques in the context of stochastic model checking for population models in which a large number of individual agents interact. Finally, Pachos’s paper is an introduction to topological quantum computation. We believe that this book offers a useful view of what has been done and what is going on worldwide in the field of formal methods for dynamical systems. We wish to thank all the speakers and all the participants for a lively and fruitful school.We also wish to thank the entire staff of the University Residential Center of Bertinoro for the organizational and administrative support. June 2013 Marco Bernardo Erik de Vink Alessandra Di Pierro Herbert Wiklick

Formal methods for dynamical systems : 13th International School on Formal Methods for the Design of Computer, Communication, and Software Systems, SFM 2013, Bertinoro, Italy, June 17-22, 2013 : advanced lectures

Preface. This volume presents a set of papers accompanying the lectures of the 13th International School on Formal Methods for the Design of Computer, Communication, and Software Systems (SFM). This series of schools addresses the use of formal methods in computer science as a prominent approach to the rigorous design of the above-mentioned systems. The main aim of the SFM series is to offer a good spectrum of current research in foundations as well as applications of formal methods, which can be of help for graduate students and young researchers who intend to approach the field. SFM 2013 was devoted to dynamicalsystems and covered several topics including chaotic dynamics, information theory, systems biology, hybrid systems, quantum computing, and automata-based models and model checking. The five papers collected in this volume represent the broad range of topics of the school. The paper by Köpf and Rybalchenko addresses the automation of the analysis of quantitative information-theoretic confidentiality properties through approximation and randomization techniques. Gratie, Iancu, and Petre introduce some of the basics of modeling with ODEs in biology by focussing on computational, numerical techniques for reaction-based models. The paper by Brim, Ceska, and Safranek presents a selection of approaches used for modeling biological systems and formalizing their interesting properties in temporal logics, together with high-performance model-checking techniques. Bortolussi and Hillston describe recent work on the use of fluid approximation techniques in the context of stochastic model checking for population models in which a large number of individual agents interact. Finally, Pachos’s paper is an introduction to topological quantum computation. We believe that this book offers a useful view of what has been done and what is going on worldwide in the field of formal methods for dynamical systems. We wish to thank all the speakers and all the participants for a lively and fruitful school.We also wish to thank the entire staff of the University Residential Center of Bertinoro for the organizational and administrative support. June 2013 Marco Bernardo Erik de Vink Alessandra Di Pierro Herbert Wiklick

Checking Individual Agent Behaviours in Markov Population Models by Fluid Approximation

In this chapter, we will describe, in a tutorial style, recent work on the use of fluid approximation techniques in the context of stochastic model checking. We will discuss the theoretical background and the algorithms working out an example. This approach is designed for population models, in which a (large) number of individual agents interact, which give rise to continuous time Markov chain (CTMC) models with a very large state space. We then focus on properties of individual agents in the system, specified by Continuous Stochastic Logic (CSL) formulae, and use fluid approximation techniques (specifically, the so called fast simulation) to check those properties. We will show that verification of such CSL formulae reduces to the computation of reachability probabilities in a special kind of time-inhomogeneous CTMC with a small state space, in which both the rates and the structure of the CTMC can change (discontinuously) with time. In this tutorial, we will discuss only briefly the theoretical issues behind the approach, like the decidability of the method and the consistency of the approximation scheme

Automatic Selection of Statistical Model Checkers for Analysis of Biological Models

Statistical Model Checking (SMC) blends the speed of simulation with the rigorous analytical capabilities of model checking, and its success has prompted researchers to implement a number of SMC tools whose availability provides flexibility and fine-tuned control over model analysis. However, each tool has its own practical limitations, and different tools have different requirements and performance characteristics. The performance of different tools may also depend on the specific features of the input model or the type of query to be verified. Consequently, choosing the most suitable tool for verifying any given model requires a significant degree of experience, and in most cases, it is challenging to predict the right one. The aim of our research has been to simplify the model checking process for researchers in biological systems modelling by simplifying and rationalising the model selection process. This has been achieved through delivery of the various key contributions listed below. • We have developed a software component for verification of kernel P (kP) system models, using the NuSMV model checker. We integrated it into a larger software platform (www.kpworkbench.org). • We surveyed five popular SMC tools, comparing their modelling languages, external dependencies, expressibility of specification languages, and performance. To best of our knowledge, this is the first known attempt to categorise the performance of SMC tools based on the commonly used property specifications (property patterns) for model checking. • We have proposed a set of model features which can be used for predicting the fastest SMC for biological model verification, and have shown, moreover, that the proposed features both reduce computation time and increase predictive power. • We used machine learning algorithms for predicting the fastest SMC tool for verification of biological models, and have shown that this approach can successfully predict the fastest SMC tool with over 90% accuracy. • We have developed a software tool, SMC Predictor, that predicts the fastest SMC tool for a given model and property query, and have made this freely available to the wider research community (www.smcpredictor.com). Our results show that using our methodology can generate significant savings in the amount of time and resources required for model verification