Model Checking of Biological Systems

Brim, Luboš; Češka, Milan; Šafránek, David

doi:10.1007/978-3-642-38874-3_3

Cited by 33 publications

(30 citation statements)

References 143 publications

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“…A survey of modeling and analysis of biological systems using hybrid models can be found in [21]. Analyzing the properties of biochemical networks using formal verification techniques is being actively pursued by a number of researchers, for which we refer to Brim's et al recent survey [22]. Of particular interest in our context are parameter synthesis methods for qualitative behavior specifications (e.g., temporal logic formulas).…”

Section: Introductionmentioning

confidence: 99%

Parameter Synthesis for Cardiac Cell Hybrid Models Using δ-Decisions

Liu

Kong

Gao

et al. 2014

Lecture Notes in Computer Science

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A central problem in systems biology is to identify parameter values such that a biological model satisfies some behavioral constraints (e.g., time series). In this paper we focus on parameter synthesis for hybrid (continuous/discrete) models, as many biological systems can possess multiple operational modes with specific continuous dynamics in each mode. These biological systems are naturally modeled as hybrid automata, most often with nonlinear continuous dynamics. However, hybrid automata are notoriously hard to analyze -even simple reachability for hybrid systems with linear differential dynamics is an undecidable problem. In this paper we present a parameter synthesis framework based on δ-complete decision procedures that sidesteps undecidability. We demonstrate our method on two highly nonlinear hybrid models of the cardiac cell action potential. The results show that our parameter synthesis framework is convenient and efficient, and it enabled us to select a suitable model to study and identify crucial parameter ranges related to cardiac disorders.

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Section: Introductionmentioning

confidence: 99%

Parameter Synthesis for Cardiac Cell Hybrid Models Using δ-Decisions

Liu

Kong

Gao

et al. 2014

Lecture Notes in Computer Science

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“…Such effort makes sense only once the original method proved itself fruitful. This is certainly the case for the model checking method that has been used in numerous studies in the past decade, across many frameworks and biological systems [3].…”

Section: Discussionmentioning

confidence: 99%

“…To verify that a model is in line with the expectations of the modeller, and that it fits the experimental A c c e p t e d M a n u s c r i p t data, the model checking (MC) method is commonly used [3]. This very strong and generic method considers every possible simulation of the system, of which there is again only a finite number, and compares it to the properties formulated by the modeller.…”

Section: Introductionmentioning

confidence: 98%

Data-driven optimizations for model checking of multi-valued regulatory networks

2016

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“…Systems biology is a recent, successful application field for model checking techniques, covering a variety of modeling formalisms and/or type of properties to be verified (for details see Brim et al (2013)). Here, we use GINsim, our modeling tool, which automatically exports biological models into NuSMV specifications.…”

Section: Methodsmentioning

confidence: 99%

“…Model checking is a computer science technique for the verification of large discrete dynamical systems (Clarke et al, 1999). It has been recently applied to the analysis of biological networks (Arellano et al, 2011; Batt et al, 2005; Brim et al, 2013; Chabrier and Fages, 2003; Schwarick and Heiner, 2009). Properties are formalized in terms of temporal logic statements, and the verification process explores (restricted) regions of the state space, in order to check the truthfulness of the properties.…”

Section: Introductionmentioning

confidence: 99%

Model checking to assess T-helper cell plasticity

Abou-Jaoudé

Monteiro

Naldi

et al. 2014

Preprint

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Computational modeling constitutes a crucial step toward the functional understanding of complex cellular networks. In particular, logical modeling has proven suitable for the dynamical analysis of large signaling and transcriptional regulatory networks. In this context, signaling input components are generally meant to convey external stimuli, or environmental cues. In response to such external signals, cells acquire specific gene expression patterns modeled in terms of attractors (e.g., stable states).The capacity for cells to alter or reprogram their differentiated states upon changes in environmental conditions is referred to as cell plasticity. In this article, we present a multivalued logical framework along with computational methods recently developed to efficiently analyze large models. We mainly focus on a symbolic model checking approach to investigate switches between attractors subsequent to changes of input conditions. As a case study, we consider the cellular network regulating the differentiation of T-helper (Th) cells, which orchestrate many physiological and pathological immune responses. To account for novel cellular subtypes, we present an extended version of a published model of Th cell differentiation. We then use symbolic model checking to analyze reachability properties between Th subtypes upon changes of environmental cues. This allows for the construction of a synthetic view of Th cell plasticity in terms of a graph connecting subtypes with arcs labeled by input conditions. Finally, we explore novel strategies enabling specificTh cell polarizing or reprograming events.

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Model Checking of Biological Systems

Cited by 33 publications

References 143 publications

Parameter Synthesis for Cardiac Cell Hybrid Models Using δ-Decisions

Parameter Synthesis for Cardiac Cell Hybrid Models Using δ-Decisions

Data-driven optimizations for model checking of multi-valued regulatory networks

Model checking to assess T-helper cell plasticity

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