SIAN: software for structural identifiability analysis of ODE models

Hoon Hong, Alexey Ovchinnikov, Gleb Pogudin, Chee Yap

Research output: Contribution to journalArticle

Abstract

SUMMARY: Biological processes are often modeled by ordinary differential equations with unknown parameters. The unknown parameters are usually estimated from experimental data. In some cases, due to the structure of the model, this estimation problem does not have a unique solution even in the case of continuous noise-free data. It is therefore desirable to check the uniqueness a priori before carrying out actual experiments. We present a new software SIAN (Structural Identifiability ANalyser) that does this. Our software can tackle problems that could not be tackled by previously developed packages. AVAILABILITY AND IMPLEMENTATION: SIAN is open-source software written in Maple and is available at https://github.com/pogudingleb/SIAN. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Original languageEnglish (US)
Pages (from-to)2873-2874
Number of pages2
JournalBioinformatics (Oxford, England)
Volume35
Issue number16
DOIs
StatePublished - Aug 15 2019

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Identifiability
Bioinformatics
Ordinary differential equations
Structural analysis
Software
Availability
Unknown Parameters
Acer
Biological Phenomena
Open Source Software
Experiments
Maple
Computational Biology
Unique Solution
Noise
Ordinary differential equation
Uniqueness
Experimental Data
Model
Experiment

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

Cite this

SIAN : software for structural identifiability analysis of ODE models. / Hong, Hoon; Ovchinnikov, Alexey; Pogudin, Gleb; Yap, Chee.

In: Bioinformatics (Oxford, England), Vol. 35, No. 16, 15.08.2019, p. 2873-2874.

Research output: Contribution to journalArticle

Hong, Hoon ; Ovchinnikov, Alexey ; Pogudin, Gleb ; Yap, Chee. / SIAN : software for structural identifiability analysis of ODE models. In: Bioinformatics (Oxford, England). 2019 ; Vol. 35, No. 16. pp. 2873-2874.
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