A Bayesian Approach to Model Checking Biological Systems

Jha, Sumit Kumar; Clarke, Edmund M.; Langmead, Christopher J.; Legay, Axel; Platzer, André; Zuliani, Paolo

doi:10.1007/978-3-642-03845-7_15

Cited by 190 publications

(162 citation statements)

References 42 publications

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“…To verify a model via statistical model checking against interesting properties, we first need to encode each property into temporal logic formulae. Here we use Bounded Linear Temporal Logic (BLTL) [7]. BLTL is a variant of Linear Temporal Logic [8], where the future condition of certain logic expressions is encoded as a formula with a time bound (see the supplementary material (http://ppt.cc/XlWF7) for BLTL's formal syntax and semantics).…”

Section: Statistical Model Checkingmentioning

confidence: 99%

Methods to Expand Cell Signaling Models Using Automated Reading and Model Checking

Liang

Wang

Telmer

et al. 2017

Computational Methods in Systems Biology

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Abstract. Biomedical research results are being published at a high rate, and with existing search engines, the vast amount of published work is usually easily accessible. However, reproducing published results, either experimental data or observations is often not viable. In this work, we propose a framework to overcome some of the issues of reproducing previous research, and to ensure re-usability of published information. We present here a framework that utilizes the results from state-of-theart biomedical literature mining, biological system modeling and analysis techniques, and provides means to scientists to assemble and reason about information from voluminous, fragmented and sometimes inconsistent literature. The overall process of automated reading, assembly and reasoning can speed up discoveries from the order of decades to the order of hours or days. Our framework described here allows for rapidly conducting thousands of in silico experiments that are designed as part of this process.

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Section: Statistical Model Checkingmentioning

confidence: 99%

Methods to Expand Cell Signaling Models Using Automated Reading and Model Checking

Liang

Wang

Telmer

et al. 2017

Computational Methods in Systems Biology

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“…In the first case one seeks to estimate probabilistically (i.e., compute with high probability a value close to) the probability that the property holds and then compare that estimate to θ [25,41] (in statistics such estimates are known as confidence intervals). In the second case, the PMC problem is directly treated as a hypothesis testing problem [49,41,29], i.e., deciding between the null hypothesis H 0 : M |= P ≥θ (φ) (M satisfies φ with probability greater than or equal to θ) versus the alternative hypothesis H 1 : M |= P <θ (φ) (M satisfies φ with probability less than θ).…”

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confidence: 99%

Bayesian Statistical Model Checking with Application to Stateflow/Simulink Verification

Zuliani¹,

Platzer²,

Clarke³

2010

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144

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We address the problem of model checking stochastic systems, i.e., checking whether a stochastic system satisfies a certain temporal property with a probability greater (or smaller) than a fixed threshold. In particular, we present a Statistical Model Checking (SMC) approach based on Bayesian statistics. We show that our approach is feasible for a certain class of hybrid systems with stochastic transitions, a generalization of Simulink/Stateflow models. Standard approaches to stochastic discrete systems require numerical solutions for large optimization problems and quickly become infeasible with larger state spaces. Generalizations of these techniques to hybrid systems with stochastic effects are even more challenging. The SMC approach was pioneered by Younes and Simmons in the discrete and non-Bayesian case. It solves the verification problem by combining randomized sampling of system traces (which is very efficient for Simulink/Stateflow) with hypothesis testing (i.e., testing against a probability threshold) or estimation (i.e., computing with high probability a value close to the true probability). We believe SMC is essential for scaling up to large Stateflow/Simulink models. While the answer to the verification problem is not guaranteed to be correct, we prove that Bayesian SMC can make the probability of giving a wrong answer arbitrarily small. The advantage is that answers can usually be obtained much faster than with standard, exhaustive model checking techniques. We apply our Bayesian SMC approach to a representative example of stochastic discrete-time hybrid system models in Stateflow/Simulink: a fuel control system featuring hybrid behavior and fault tolerance. We show that our technique enables faster verification than state-of-the-art statistical techniques. We emphasize that Bayesian SMC is by no means restricted to Stateflow/Simulink models. It is in principle applicable to a variety of stochastic models from other domains, e.g., systems biology.

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“…Such algorithms sequentially execute simulations until either H or K can be returned with a confidence α or β, which is dynamically detected. Other sequential hypothesis testing approaches exists, which are based on Bayesian approach [21].…”

Section: Statistical Model Checkingmentioning

confidence: 99%

An Application of SMC to continuous validation of heterogeneous systems

Arnold¹,

Baleani²,

Ferrari³

et al. 2017

EAI Endorsed Transactions on Industrial Networks and Intelligen

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This paper considers the rigorous design of Systems of Systems (SoS), i.e. systems composed of a set of heterogeneous components whose number evolves with time. Such components cooperate to accomplish functions that they could not achieve in isolation. Examples of SoS include smart cities or airport management system. The dynamical evolution of SoS behavior and architecture makes it impossible to design an appropriate solution beforehand. Consequently, existing approaches build on an iterative process that takes SoS evolution into account. A key challenge in this process is the ability to reason about and analyze a given view of the SoS (on a fixed number of SoS constituents) with respect to a set of goals, and use the results to eventually predict the evolution of the SoS. To address this challenge, we rely on a scalable formal verification technique known as Statistical Model Checking (SMC). SMC quantifies how close the current view is from achieving a given mission. We integrate SMC with existing industrial practice, by addressing both methodological and technological issues. Our contribution is: (1) a methodology for validation of SoS formal requirements; (2) a formal specification language able to express complex SoS requirements; (3) the adoption of current industry standards for simulation and heterogeneous systems integration ; (4) a robust SMC tool-chain integrated with system design tools used in practice. We illustrate the application of our SMC tool-chain and the obtained results on a case study.

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A Bayesian Approach to Model Checking Biological Systems

Cited by 190 publications

References 42 publications

Methods to Expand Cell Signaling Models Using Automated Reading and Model Checking

Methods to Expand Cell Signaling Models Using Automated Reading and Model Checking

Bayesian Statistical Model Checking with Application to Stateflow/Simulink Verification

An Application of SMC to continuous validation of heterogeneous systems

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