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SIAN: software for structural identifiability analysis of ODE models.

Hoon Hong1, Alexey Ovchinnikov2, Gleb Pogudin3

  • 1Department of Mathematics, North Carolina State University, Raleigh, NC, USA.

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|January 3, 2019
PubMed
Summary
This summary is machine-generated.

We developed SIAN (Structural Identifiability ANalyser), new open-source software to assess parameter uniqueness in biological models. SIAN can analyze complex models that were previously untackleable, improving experimental design.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Mathematical Modeling

Background:

  • Biological processes are frequently modeled using ordinary differential equations (ODEs).
  • Estimating ODE parameters from experimental data is crucial but can be ill-posed.
  • Model structural identifiability, the theoretical possibility of unique parameter estimation, is a key challenge.

Purpose of the Study:

  • To introduce SIAN (Structural Identifiability ANalyser), a novel software tool.
  • To enable a priori assessment of parameter identifiability in ODE models.
  • To address limitations of existing software in analyzing complex biological models.

Main Methods:

  • Development of SIAN software utilizing Maple.
  • Implementation of algorithms for structural identifiability analysis.
  • Testing SIAN on biological models with challenging identifiability properties.

Main Results:

  • SIAN successfully analyzes structural identifiability for complex ODE models.
  • The software identifies cases where parameter estimation is non-unique, even with ideal data.
  • SIAN extends the scope of identifiability analysis beyond previously available tools.

Conclusions:

  • SIAN provides a valuable tool for researchers modeling biological systems.
  • A priori identifiability analysis with SIAN can guide experimental design and resource allocation.
  • The open-source availability of SIAN promotes wider adoption and advancement in the field.