Statistical model checking for biological applications

Zuliani, Paolo

doi:10.1007/s10009-014-0343-0

Cited by 28 publications

(15 citation statements)

References 43 publications

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“…These tools have been used for analysing a wide range of systems, including computer, network and biological systems. The applicability of these SMC tools to a broad range of biological systems has been intensively investigated (Jansen et al , 2008; Bakir et al , 2017; Boyer et al , 2013; Donaldson and Gilbert, 2008; Zuliani, 2015). …”

Section: Methodsmentioning

confidence: 99%

Automatic selection of verification tools for efficient analysis of biochemical models

et al. 2018

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Motivation Formal verification is a computational approach that checks system correctness (in relation to a desired functionality). It has been widely used in engineering applications to verify that systems work correctly. Model checking, an algorithmic approach to verification, looks at whether a system model satisfies its requirements specification. This approach has been applied to a large number of models in systems and synthetic biology as well as in systems medicine. Model checking is, however, computationally very expensive, and is not scalable to large models and systems. Consequently, statistical model checking (SMC), which relaxes some of the constraints of model checking, has been introduced to address this drawback. Several SMC tools have been developed; however, the performance of each tool significantly varies according to the system model in question and the type of requirements being verified. This makes it hard to know, a priori, which one to use for a given model and requirement, as choosing the most efficient tool for any biological application requires a significant degree of computational expertise, not usually available in biology labs. The objective of this article is to introduce a method and provide a tool leading to the automatic selection of the most appropriate model checker for the system of interest.ResultsWe provide a system that can automatically predict the fastest model checking tool for a given biological model. Our results show that one can make predictions of high confidence, with over 90% accuracy. This implies significant performance gain in verification time and substantially reduces the ‘usability barrier’ enabling biologists to have access to this powerful computational technology.Availability and implementationSMC Predictor tool is available at http://www.smcpredictor.com.Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Methodsmentioning

confidence: 99%

Automatic selection of verification tools for efficient analysis of biochemical models

et al. 2018

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“…For example, the interconnection of quantitative models of the human physiology (e.g., Physiomodel, Mateják and Kofránek, 2015), drugs pharmacokinetics/pharmacodynamics (e.g., Open Systems Pharmacology Suite, Eissing et al, 2011), (possibly semi-autonomous) biomedical devices, pharmacological protocol guidelines or treatment schemes, enables the set-up of in silico clinical trials for the (model-based) safety and efficacy pre-clinical assessment of such drugs, protocols, treatments, devices, using standard system engineering approaches to perform their simulation-based analysis at system level (see, e.g., Kanade et al, 2009;Mancini et al, 2013Mancini et al, , 2014Zuliani et al, 2013;Zuliani, 2015;Mancini et al, 2016aMancini et al, , 2017. Works in this direction include, e.g., (Schaller et al, 2016;Messori et al, 2018), where a model-based verification activity of a sensor-augmented insulin pump is conducted against a model of the human glucose metabolism in patients with diabetes mellitus, (Madec et al, 2019), where a model of a penicillin bio-sensor (integrating biochemistry, electrochemistry, and electronics models) is simulated to compute a first dimensioning of the sensor, and (Tronci et al, 2014;Mancini et al, 2015), where representative populations of virtual patients are generated from parametric models of the human physiology, a key step to enable in silico clinical trials (see, e.g., Mancini et al, 2018).…”

Section: Motivationsmentioning

confidence: 99%

SBML2Modelica: integrating biochemical models within open-standard simulation ecosystems

2019

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Motivation SBML is the most widespread language for the definition of biochemical models. Although dozens of SBML simulators are available, there is a general lack of support to the integration of SBML models within open-standard general-purpose simulation ecosystems. This hinders co-simulation and integration of SBML models within larger model networks, in order to, e.g. enable in silico clinical trials of drugs, pharmacological protocols, or engineering artefacts such as biomedical devices against Virtual Physiological Human models. Modelica is one of the most popular existing open-standard general-purpose simulation languages, supported by many simulators. Modelica models are especially suited for the definition of complex networks of heterogeneous models from virtually all application domains. Models written in Modelica (and in 100+ other languages) can be readily exported into black-box Functional Mock-Up Units (FMUs), and seamlessly co-simulated and integrated into larger model networks within open-standard language-independent simulation ecosystems. Results In order to enable SBML model integration within heterogeneous model networks, we present SBML2Modelica, a software system translating SBML models into well-structured, user-intelligible, easily modifiable Modelica models. SBML2Modelica is SBML Level 3 Version 2—compliant and succeeds on 96.47% of the SBML Test Suite Core (with a few rare, intricate and easily avoidable combinations of constructs unsupported and cleanly signalled to the user). Our experimental campaign on 613 models from the BioModels database (with up to 5438 variables) shows that the major open-source (general-purpose) Modelica and FMU simulators achieve performance comparable to state-of-the-art specialized SBML simulators. Availability and implementation SBML2Modelica is written in Java and is freely available for non-commercial use at https://bitbucket.org/mclab/sbml2modelica.

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“…Both probabilistic and statistical model checking have been applied to biological models [31,32,51], with tools for parameter synthesis [11,12]. Although the parameter synthesis approach in [11] rigorously calculates the satisfaction probability over the whole parameter space, it suffers from scalability issues.…”

Section: Introductionmentioning

confidence: 99%

ABC(SMC)$$^2$$: Simultaneous Inference and Model Checking of Chemical Reaction Networks

Molyneux

Abate

2020

Computational Methods in Systems Biology

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We present an approach that simultaneously infers model parameters while statistically verifying properties of interest to chemical reaction networks, which we observe through data and we model as parametrised continuous-time Markov Chains. The new approach simultaneously integrates learning models from data, done by likelihoodfree Bayesian inference, specifically Approximate Bayesian Computation, with formal verification over models, done by statistically model checking properties expressed as logical specifications (in CSL). The approach generates a probability (or credibility calculation) on whether a given chemical reaction network satisfies a property of interest.

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Statistical model checking for biological applications

Cited by 28 publications

References 43 publications

Automatic selection of verification tools for efficient analysis of biochemical models

Automatic selection of verification tools for efficient analysis of biochemical models

SBML2Modelica: integrating biochemical models within open-standard simulation ecosystems

ABC(SMC)$$^2$$: Simultaneous Inference and Model Checking of Chemical Reaction Networks

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