SIAN: Software for structural identifiability analysis of ODE models

Hoon Hong; Alexey Ovchinnikov; Gleb Pogudin; Chee Yap

doi:10.1093/bioinformatics/bty1069

SIAN: Software for structural identifiability analysis of ODE models

Hoon Hong, Alexey Ovchinnikov, Gleb Pogudin, Chee Yap

Computer Science

Research output: Contribution to journal › Article › peer-review

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.

Original language	English (US)
Pages (from-to)	2873-2874
Number of pages	2
Journal	Bioinformatics
Volume	35
Issue number	16
DOIs	https://doi.org/10.1093/bioinformatics/bty1069
State	Published - Aug 15 2019

ASJC Scopus subject areas

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

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10.1093/bioinformatics/bty1069

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title = "SIAN: Software for structural identifiability analysis of ODE models",

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.",

author = "Hoon Hong and Alexey Ovchinnikov and Gleb Pogudin and Chee Yap",

year = "2019",

month = aug,

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doi = "10.1093/bioinformatics/bty1069",

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AU - Yap, Chee

PY - 2019/8/15

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N2 - 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.

AB - 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.

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SIAN: Software for structural identifiability analysis of ODE models

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