On algorithmic analysis of transcriptional regulation by LTL model checking

Barnat, Jǐŕı; Brim, Luboš; erná, I.; Draan, S.; Fabrikovà, J.; afránek, D.

doi:10.1016/j.tcs.2009.02.017

Cited by 9 publications

(9 citation statements)

References 24 publications

(50 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several approaches consist in the abstraction of continuous models into discrete transition systems (e.g. [ 3 – 6 ]); this may enable the use of model checking as a state space exploration technique [ 3 , 7 , 8 ]. Our approach is based on Timed Automata models [ 9 ] defined by linear approximations (with arbitrary precision) of ordinary differential equations (ODEs); this has the benefit of using existing mature Timed Automata analysis techniques.…”

Section: Introductionmentioning

confidence: 99%

Modelling with ANIMO: between fuzzy logic and differential equations

et al. 2016

View full text Add to dashboard Cite

BackgroundComputational support is essential in order to reason on the dynamics of biological systems. We have developed the software tool ANIMO (Analysis of Networks with Interactive MOdeling) to provide such computational support and allow insight into the complex networks of signaling events occurring in living cells. ANIMO makes use of timed automata as an underlying model, thereby enabling analysis techniques from computer science like model checking. Biology experts are able to use ANIMO via a user interface specifically tailored for biological applications. In this paper we compare the use of ANIMO with some established formalisms on two case studies.ResultsANIMO is a powerful and user-friendly tool that can compete with existing continuous and discrete paradigms. We show this by presenting ANIMO models for two case studies: Drosophila melanogaster circadian clock, and signal transduction events downstream of TNF α and EGF in HT-29 human colon carcinoma cells. The models were originally developed with ODEs and fuzzy logic, respectively.ConclusionsTwo biological case studies that have been modeled with respectively ODE and fuzzy logic models can be conveniently modeled using ANIMO. The ANIMO models require less parameters than ODEs and are more precise than fuzzy logic. For this reason we position the modelling paradigm of ANIMO between ODEs and fuzzy logic.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-016-0286-z) contains supplementary material, which is available to authorized users.

show abstract

Section: Introductionmentioning

confidence: 99%

Modelling with ANIMO: between fuzzy logic and differential equations

et al. 2016

View full text Add to dashboard Cite

show abstract

“…As a consequence, the derivatives of the concentration variables have a unique sign pattern : for all x , y ∈ D , it holds that sign( F i ( x )) = sign( F i ( y )) ⊆ {−1, 0, 1}, where sign( A ) ≜ {sign( a )∣ a ∈ A } denotes the signs of the elements in A (Batt et al , 2008 ). Notice that this property is not obtained for less fine-grained partitions used in related work (Barnat et al , 2009 ; Bernot et al , 2004 ; Chaves et al , 2009 ; Corblin et al , 2009 ; Fauré et al , 2006 ; Fromentin et al , 2007 ). It will be found critical for the search of parametrized models of IRMA that satisfy the time-series data.…”

Section: Search Of Parameter Space Using Symbolic Model Checkingmentioning

confidence: 87%

“…The computer tool GNA has been extended to export the symbolic encoding of PADE models in the NuSMV language (Cimatti et al , 2002 ). In comparison with related work (Barnat et al , 2009 ; Bernot et al , 2004 ; Corblin et al , 2009 ; Fromentin et al , 2007 ), our method applies to incompletely instead of fully parametrized models, provides more precise results and the encoding is efficient without (strongly) simplifying the PADE dynamics.…”

Section: Introductionmentioning

confidence: 88%

Efficient parameter search for qualitative models of regulatory networks using symbolic model checking

et al. 2010

View full text Add to dashboard Cite

Motivation: Investigating the relation between the structure and behavior of complex biological networks often involves posing the question if the hypothesized structure of a regulatory network is consistent with the observed behavior, or if a proposed structure can generate a desired behavior.Results: The above questions can be cast into a parameter search problem for qualitative models of regulatory networks. We develop a method based on symbolic model checking that avoids enumerating all possible parametrizations, and show that this method performs well on real biological problems, using the IRMA synthetic network and benchmark datasets. We test the consistency between IRMA and time-series expression profiles, and search for parameter modifications that would make the external control of the system behavior more robust.Availability: GNA and the IRMA model are available at http://ibis.inrialpes.fr/Contact: gregory.batt@inria.frSupplementary information: Supplementary data are available at Bioinformatics online.

show abstract

“…In our previous work [9] we have dealt with parallel model checking analysis of piece-wise affine ODE models [10]. The method allows fully qualitative analysis, since in the piece-wise affine approximation generating of the state space does not require to numerically enumerate the equations.…”

Section: A Related Workmentioning

confidence: 99%

Computational Analysis of Large-Scale Multi-affine ODE Models

Brim

Barnat

Černá

et al. 2009

2009 International Workshop on High Performance Computational Systems Biology

View full text Add to dashboard Cite

Abstract-A biological system as considered in systems biology is understood in the form of a network of interactions among individual biochemical species. Complexity of these networks is inherently enormous, even for simple (e.g., procaryotic) organisms. When modeling and analyzing dynamics of these networks, i.e., exploring how the species evolve in time, we have to fight even another level of complexity -the enormous state space. In this paper we deal with a class of biological models that can be described in terms of multi-affine dynamic systems. First, we present a prototype tool for parallel (distributed) analysis of multi-affine systems discretized into rectangles that adapts the approach of Belta et.al.[1], [2]. Secondly, we propose heuristics that significantly increase applicability of the approach to large biological models. Effects of different settings of the heuristics is firstly compared on a set of experiments performed on small models. Subsequently, experiments on large models are provided as well.

show abstract

On algorithmic analysis of transcriptional regulation by LTL model checking

Cited by 9 publications

References 24 publications

Modelling with ANIMO: between fuzzy logic and differential equations

Modelling with ANIMO: between fuzzy logic and differential equations

Efficient parameter search for qualitative models of regulatory networks using symbolic model checking

Computational Analysis of Large-Scale Multi-affine ODE Models

Contact Info

Product

Resources

About