Modelling with ANIMO: between fuzzy logic and differential equations

Stefano Schivo, Jetse Scholma, Paul E van der Vet, Marcel Karperien, Janine N Post, Jaco van de Pol, Rom Langerak*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

BACKGROUND: Computational 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.

RESULTS: ANIMO 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.

CONCLUSIONS: Two 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.

Original languageEnglish
Article number56
Number of pages15
JournalBMC Systems Biology
Volume10
DOIs
Publication statusPublished - 27 Jul 2016
Externally publishedYes

Keywords

  • Animals
  • Circadian Clocks
  • Computational Biology/methods
  • Drosophila melanogaster/cytology
  • Epidermal Growth Factor/metabolism
  • Fuzzy Logic
  • HT29 Cells
  • Humans
  • Signal Transduction
  • Software
  • Tumor Necrosis Factor-alpha/metabolism

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  • Cite this

    Schivo, S., Scholma, J., van der Vet, P. E., Karperien, M., Post, J. N., van de Pol, J., & Langerak, R. (2016). Modelling with ANIMO: between fuzzy logic and differential equations. BMC Systems Biology, 10, [56]. https://doi.org/10.1186/s12918-016-0286-z