Statistical Model Checking for Stochastic Hybrid Systems

This paper presents novel extensions and applications of the UPPAAL-SMC model checker. The extensions allow for statistical model checking of stochastic hybrid systems. We show how our race-based stochastic semantics extends to networks of hybrid systems, and indicate the integration technique applied for implementing this semantics in the UPPAAL-SMC simulation engine. We report on two applications of the resulting tool-set coming from systems biology and energy aware buildings.Comment: In Proceedings HSB 2012, arXiv:1208.315

Computational Logic for Biomedicine and Neurosciences

We advocate here the use of computational logic for systems biology, as a \emph{unified and safe} framework well suited for both modeling the dynamic behaviour of biological systems, expressing properties of them, and verifying these properties. The potential candidate logics should have a traditional proof theoretic pedigree (including either induction, or a sequent calculus presentation enjoying cut-elimination and focusing), and should come with certified proof tools. Beyond providing a reliable framework, this allows the correct encodings of our biological systems. % For systems biology in general and biomedicine in particular, we have so far, for the modeling part, three candidate logics: all based on linear logic. The studied properties and their proofs are formalized in a very expressive (non linear) inductive logic: the Calculus of Inductive Constructions (CIC). The examples we have considered so far are relatively simple ones; however, all coming with formal semi-automatic proofs in the Coq system, which implements CIC. In neuroscience, we are directly using CIC and Coq, to model neurons and some simple neuronal circuits and prove some of their dynamic properties. % In biomedicine, the study of multi omic pathway interactions, together with clinical and electronic health record data should help in drug discovery and disease diagnosis. Future work includes using more automatic provers. This should enable us to specify and study more realistic examples, and in the long term to provide a system for disease diagnosis and therapy prognosis

"Antelope": a hybrid-logic model checker for branching-time Boolean GRN analysis

<p>Abstract</p> <p>Background</p> <p>In Thomas' formalism for modeling gene regulatory networks (GRNs), <it>branching time</it>, where a state can have <it>more than one possible future</it>, plays a prominent role. By representing a certain degree of unpredictability, branching time can model several important phenomena, such as (a) asynchrony, (b) incompletely specified behavior, and (c) interaction with the environment. Introducing more than one possible future for a state, however, creates a difficulty for ordinary simulators, because <it>infinitely many </it>paths may appear, limiting ordinary simulators to statistical conclusions. <it>Model checkers </it>for branching time, by contrast, are able to prove properties in the presence of infinitely many paths.</p> <p>Results</p> <p>We have developed <it>Antelope </it>("Analysis of Networks through TEmporal-LOgic sPEcifications", <url>http://turing.iimas.unam.mx:8080/AntelopeWEB/</url>), a model checker for analyzing and constructing Boolean GRNs. Currently, software systems for Boolean GRNs use branching time almost exclusively for asynchrony. <it>Antelope</it>, by contrast, also uses branching time for incompletely specified behavior and environment interaction. We show the usefulness of modeling these two phenomena in the development of a Boolean GRN of the <it>Arabidopsis thaliana </it>root stem cell niche.</p> <p>There are two obstacles to a direct approach when applying model checking to Boolean GRN analysis. First, ordinary model checkers normally only verify whether or not a <it>given </it>set of model states has a given property. In comparison, a model checker for Boolean GRNs is preferable if it <it>reports </it>the set of states having a desired property. Second, for efficiency, the expressiveness of many model checkers is limited, resulting in the inability to express some interesting properties of Boolean GRNs.</p> <p><it>Antelope </it>tries to overcome these two drawbacks: Apart from reporting the set of all states having a given property, our model checker can express, at the expense of efficiency, some properties that ordinary model checkers (e.g., NuSMV) cannot. This additional expressiveness is achieved by employing a logic extending the standard Computation-Tree Logic (CTL) with hybrid-logic operators.</p> <p>Conclusions</p> <p>We illustrate the advantages of <it>Antelope </it>when (a) modeling incomplete networks and environment interaction, (b) exhibiting the set of all states having a given property, and (c) representing Boolean GRN properties with hybrid CTL.</p

10271 Abstracts Collection -- Verification over discrete-continuous boundaries

From 4 July 2010 to 9 July 2010, the Dagstuhl Seminar 10271 ``Verification over discrete-continuous boundaries\u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Computational Modeling, Formal Analysis, and Tools for Systems Biology.

As the amount of biological data in the public domain grows, so does the range of modeling and analysis techniques employed in systems biology. In recent years, a number of theoretical computer science developments have enabled modeling methodology to keep pace. The growing interest in systems biology in executable models and their analysis has necessitated the borrowing of terms and methods from computer science, such as formal analysis, model checking, static analysis, and runtime verification. Here, we discuss the most important and exciting computational methods and tools currently available to systems biologists. We believe that a deeper understanding of the concepts and theory highlighted in this review will produce better software practice, improved investigation of complex biological processes, and even new ideas and better feedback into computer science

Evolving cell models for systems and synthetic biology

This paper proposes a new methodology for the automated design of cell models for systems and synthetic biology. Our modelling framework is based on P systems, a discrete, stochastic and modular formal modelling language. The automated design of biological models comprising the optimization of the model structure and its stochastic kinetic constants is performed using an evolutionary algorithm. The evolutionary algorithm evolves model structures by combining different modules taken from a predefined module library and then it fine-tunes the associated stochastic kinetic constants. We investigate four alternative objective functions for the fitness calculation within the evolutionary algorithm: (1) equally weighted sum method, (2) normalization method, (3) randomly weighted sum method, and (4) equally weighted product method. The effectiveness of the methodology is tested on four case studies of increasing complexity including negative and positive autoregulation as well as two gene networks implementing a pulse generator and a bandwidth detector. We provide a systematic analysis of the evolutionary algorithm’s results as well as of the resulting evolved cell models

Hybrid performance modelling of opportunistic networks

We demonstrate the modelling of opportunistic networks using the process algebra stochastic HYPE. Network traffic is modelled as continuous flows, contact between nodes in the network is modelled stochastically, and instantaneous decisions are modelled as discrete events. Our model describes a network of stationary video sensors with a mobile ferry which collects data from the sensors and delivers it to the base station. We consider different mobility models and different buffer sizes for the ferries. This case study illustrates the flexibility and expressive power of stochastic HYPE. We also discuss the software that enables us to describe stochastic HYPE models and simulate them.Comment: In Proceedings QAPL 2012, arXiv:1207.055

Automating Program Verification and Repair Using Invariant Analysis and Test Input Generation

Software bugs are a persistent feature of daily life---crashing web browsers, allowing cyberattacks, and distorting the results of scientific computations. One approach to improving software uses program invariants---mathematical descriptions of program behaviors---to verify code and detect bugs. Current invariant generation techniques lack support for complex yet important forms of invariants, such as general polynomial relations and properties of arrays. As a result, we lack the ability to conduct precise analysis of programs that use this common data structure. This dissertation presents DIG, a static and dynamic analysis framework for discovering several useful classes of program invariants, including (i) nonlinear polynomial relations, which are fundamental to many scientific applications; disjunctive invariants, (ii) which express branching behaviors in programs; and (iii) properties about multidimensional arrays, which appear in many practical applications. We describe theoretical and empirical results showing that DIG can efficiently and accurately find many important invariants in real-world uses, e.g., polynomial properties in numerical algorithms and array relations in a full AES encryption implementation. Automatic program verification and synthesis are long-standing problems in computer science. However, there has been a lot of work on program verification and less so on program synthesis. Consequently, important synthesis tasks, e.g., generating program repairs, remain difficult and time-consuming. This dissertation proves that certain formulations of verification and synthesis are equivalent, allowing for direct applications of techniques and tools between these two research areas. Based on these ideas, we develop CETI, a tool that leverages existing verification techniques and tools for automatic program repair. Experimental results show that CETI can have higher success rates than many other standard program repair methods

Systems Biology of Cancer: A Challenging Expedition for Clinical and Quantitative Biologists

A systems-biology approach to complex disease (such as cancer) is now complementing traditional experience-based approaches, which have typically been invasive and expensive. The rapid progress in biomedical knowledge is enabling the targeting of disease with therapies that are precise, proactive, preventive, and personalized. In this paper, we summarize and classify models of systems biology and model checking tools, which have been used to great success in computational biology and related fields. We demonstrate how these models and tools have been used to study some of the twelve biochemical pathways implicated in but not unique to pancreatic cancer, and conclude that the resulting mechanistic models will need to be further enhanced by various abstraction techniques to interpret phenomenological models of cancer progression