### Abstract

Many important real-world processes are modeled using systems of ordinary differential equations (ODEs) involving unknown parameters. The values of these parameters are usually inferred from experimental data. However, due to the structure of the model, there might be multiple parameter values that yield the same observed behavior even in the case of continuous noise-free data. It is important to detect such situations a priori, before collecting actual data. In this case, the only input is the model itself, so it is natural to tackle this question by methods of symbolic computation. We present new software SIAN (Structural Identifiability ANalyser) that solves this problem. Our software allows to tackle problems that could not be tackled before. It is written in Maple and available at https://github.com/pogudingleb/SIAN.

Original language | English (US) |
---|---|

Pages (from-to) | 37-40 |

Number of pages | 4 |

Journal | ACM Communications in Computer Algebra |

Volume | 53 |

Issue number | 2 |

DOIs | |

State | Published - Jun 2019 |

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### ASJC Scopus subject areas

- Computational Theory and Mathematics
- Computational Mathematics

### Cite this

*ACM Communications in Computer Algebra*,

*53*(2), 37-40. https://doi.org/10.1145/3371991.3371993

**Sian : A tool for assessing structural identifiability of parametric ODEs.** / Hong, Hoon; Ovchinnikov, Alexey; Pogudin, Gleb; Yap, Chee.

Research output: Contribution to journal › Article

*ACM Communications in Computer Algebra*, vol. 53, no. 2, pp. 37-40. https://doi.org/10.1145/3371991.3371993

}

TY - JOUR

T1 - Sian

T2 - A tool for assessing structural identifiability of parametric ODEs

AU - Hong, Hoon

AU - Ovchinnikov, Alexey

AU - Pogudin, Gleb

AU - Yap, Chee

PY - 2019/6

Y1 - 2019/6

N2 - Many important real-world processes are modeled using systems of ordinary differential equations (ODEs) involving unknown parameters. The values of these parameters are usually inferred from experimental data. However, due to the structure of the model, there might be multiple parameter values that yield the same observed behavior even in the case of continuous noise-free data. It is important to detect such situations a priori, before collecting actual data. In this case, the only input is the model itself, so it is natural to tackle this question by methods of symbolic computation. We present new software SIAN (Structural Identifiability ANalyser) that solves this problem. Our software allows to tackle problems that could not be tackled before. It is written in Maple and available at https://github.com/pogudingleb/SIAN.

AB - Many important real-world processes are modeled using systems of ordinary differential equations (ODEs) involving unknown parameters. The values of these parameters are usually inferred from experimental data. However, due to the structure of the model, there might be multiple parameter values that yield the same observed behavior even in the case of continuous noise-free data. It is important to detect such situations a priori, before collecting actual data. In this case, the only input is the model itself, so it is natural to tackle this question by methods of symbolic computation. We present new software SIAN (Structural Identifiability ANalyser) that solves this problem. Our software allows to tackle problems that could not be tackled before. It is written in Maple and available at https://github.com/pogudingleb/SIAN.

UR - http://www.scopus.com/inward/record.url?scp=85075056739&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85075056739&partnerID=8YFLogxK

U2 - 10.1145/3371991.3371993

DO - 10.1145/3371991.3371993

M3 - Article

AN - SCOPUS:85075056739

VL - 53

SP - 37

EP - 40

JO - ACM Communications in Computer Algebra

JF - ACM Communications in Computer Algebra

SN - 1932-2232

IS - 2

ER -