Model Checking Approach to the Analysis of Biological Systems

Beneš, Nikola; Brim, Luboš; Pastva, Samuel; Šafránek, David

doi:10.1007/978-3-030-17297-8_1

Cited by 4 publications

(4 citation statements)

References 81 publications

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“…Due to its novel features to infer information about system behaviour, there is growing interest in applying this technique in systems biology [38,39]. Model checking has been applied to the analysis of various biological systems such as ERK/MAPK or FGF signalling pathway [40][41][42], EGFR pathway [43,44], T-cell receptor signalling pathway [45][46][47][48], cell cycle in eukaryotes [49,50], cell cycle control [51][52][53][54][55], mammalian cell cycle regulation [56,57], apoptosis network [58,59], bladder tumorigenesis [60], quorum sensing [61][62][63], biological control mechanisms [64], DNA computing [65][66][67], genetic oscillator [68,69], genetic Boolean gates [61,[70][71][72][73] and switches [73][74][75].…”

Section: Introductionmentioning

confidence: 99%

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Verifiable biology

Konur

Gheorghe

Krasnogor

2023

J. R. Soc. Interface.

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The formalization of biological systems using computational modelling approaches as an alternative to mathematical-based methods has recently received much interest because computational models provide a deeper mechanistic understanding of biological systems. In particular, formal verification, complementary approach to standard computational techniques such as simulation, is used to validate the system correctness and obtain critical information about system behaviour. In this study, we survey the most frequently used computational modelling approaches and formal verification techniques for computational biology. We compare a number of verification tools and software suites used to analyse biological systems and biochemical networks, and to verify a wide range of biological properties. For users who have no expertise in formal verification, we present a novel methodology that allows them to easily apply formal verification techniques to analyse their biological or biochemical system of interest.

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Section: Introductionmentioning

confidence: 99%

“…Some studies have evaluated the methods used in modelling biological and biochemical networks [1,2,[76][77][78][79][80][81] and the use of formal methods in systems biology [69,[82][83][84][85][86]. However, these studies cover only limited and very specific methods, which are usually context-dependent.…”

Section: Introductionmentioning

confidence: 99%

Verifiable biology

Konur

Gheorghe

Krasnogor

2023

J. R. Soc. Interface.

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“…As such, it has been applied mostly as a tool to decide correctness and compliance in system design. Relevant examples are software verification (e.g., Bérard et al, 2001), system communication and also computational biology (e.g., Brim et al, 2013;Benes et al, 2019). Following Baier and Katoen (2008), we intend a typical model-checking workflow as: (i) specifying desirable requirements for a system design by specific logical languages; (ii) formalizing the system in terms of an appropriate semantic model; and (iii) checking whether the formalized model satisfies the given requirements by means of satisfiability of semantic clauses of the language.…”

Section: Introductionmentioning

confidence: 99%

“…This might represent an issue in many real-case scenarios. In computational biology, for instance, nonhomogeneous processes are quite common (Benes et al, 2019). Similarly, ignorance about probabilities represents an important challenge for epistemic multi-agent systems.…”

Section: Introductionmentioning

confidence: 99%

Robust Model Checking with Imprecise Markov Reward Models

Termine,

Antonucci,

Facchini

et al. 2021

Preprint

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Because of the widespread diffusion of computational systems of stochastic nature, in recent years probabilistic model checking has become an important area of research. However, despite its great success, standard probabilistic model checking suffers the limitation of requiring a sharp specification of the probabilities governing the model behaviour. Imprecise probability theory offers a natural approach to overcome such limitation by a sensitivity analysis with respect to the values of these parameters. However, only extensions based on discrete-time imprecise Markov chains have been considered so far for such a robust approach to model checking. Here we present a further extension based on imprecise Markov reward models. In particular, we derive efficient algorithms to compute lower and upper bounds of the expected cumulative reward and probabilistic bounded rewards based on existing results for imprecise Markov chains. These ideas are finally tested on a real case study involving the spenddown costs of geriatric medicine departments.

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Logic and Model Checking by Imprecise Probabilistic Interpreted Systems

Termine

Antonucci

Primiero

et al. 2021

Multi-Agent Systems

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Stochastic multi-agent systems raise the necessity to extend probabilistic model checking to the epistemic domain. Results in this direction have been achieved by epistemic extensions of Probabilistic Computation Tree Logic and related Probabilistic Interpreted Systems. The latter, however, suffer of an important limitation: they require the probabilities governing the system's behaviour to be fully specified. A promising way to overcome this limitation is represented by imprecise probabilities. In this paper we introduce imprecise probabilistic interpreted systems and present a related logical language and model-checking procedures based on recent advances in the study of imprecise Markov processes.

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Model Checking Approach to the Analysis of Biological Systems

Cited by 4 publications

References 81 publications

Verifiable biology

Verifiable biology

Robust Model Checking with Imprecise Markov Reward Models

Logic and Model Checking by Imprecise Probabilistic Interpreted Systems

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