GenSSI: a software toolbox for structural identifiability analysis of biological models. Mathematical modeling has a key role in systems biology. Model building is often regarded as an iterative loop involving several tasks, among which the estimation of unknown parameters of the model from a certain set of experimental data is of central importance. This problem of parameter estimation has many possible pitfalls, and modelers should be very careful to avoid them. Many of such difficulties arise from a fundamental (yet often overlooked) property: the so-called structural (or a priori) identifiability, which considers the uniqueness of the estimated parameters. Obviously, the structural identifiability of any tentative model should be checked at the beginning of the model building loop. However, checking this property for arbitrary non-linear dynamic models is not an easy task. Here we present a software toolbox, GenSSI (Generating Series for testing Structural Identifiability), which enables non-expert users to carry out such analysis. The toolbox runs under the popular MATLAB environment and is accompanied by detailed documentation and relevant examples.

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  1. Simpson, Matthew J.; Browning, Alexander P.; Warne, David J.; Maclaren, Oliver J.; Baker, Ruth E.: Parameter identifiability and model selection for sigmoid population growth models (2022)
  2. Schmiester, Leonard; Weindl, Daniel; Hasenauer, Jan: Parameterization of mechanistic models from qualitative data using an efficient optimal scaling approach (2020)
  3. Jeronimo, Gabriela; Pérez Millán, Mercedes; Solernó, Pablo: Identifiability from a few species for a class of biochemical reaction networks (2019)
  4. Lund, Alana; Dyke, Shirley J.; Song, Wei; Bilionis, Ilias: Global sensitivity analysis for the design of nonlinear identification experiments (2019)
  5. Saccomani, Maria Pia; Thomaseth, Karl: Calculating all multiple parameter solutions of ODE models to avoid biological misinterpretations (2019)
  6. Villaverde, Alejandro F.: Observability and structural identifiability of nonlinear biological systems (2019)
  7. Gallaher, Jill; Larripa, Kamila; Renardy, Marissa; Shtylla, Blerta; Tania, Nessy; White, Diana; Wood, Karen; Zhu, Li; Passey, Chaitali; Robbins, Michael; Bezman, Natalie; Shelat, Suresh; Jay Cho, Hearn; Moore, Helen: Methods for determining key components in a mathematical model for tumor-immune dynamics in multiple myeloma (2018)
  8. Ruiz Velázquez, Eduardo; Sánchez, Oscar D.; Quiroz, Griselda; Pulido, Guillermo O.: Parametric identification of Sorensen model for glucose-insulin-carbohydrates dynamics using evolutive algorithms. (2018)
  9. Nimmegeers, Philippe; Lauwers, Joost; Telen, Dries; Logist, Filip; van Impe, Jan: Identifiability of large-scale non-linear dynamic network models applied to the ADM1-case study (2017)
  10. Chis, Oana-Teodora; Villaverde, Alejandro F.; Banga, Julio R.; Balsa-Canto, Eva: On the relationship between sloppiness and identifiability (2016)
  11. Letham, Benjamin; Letham, Portia A.; Rudin, Cynthia; Browne, Edward P.: Prediction uncertainty and optimal experimental design for learning dynamical systems (2016)
  12. Clermont, Gilles; Zenker, Sven: The inverse problem in mathematical biology (2015)
  13. Berthoumieux, Sara; Brilli, Matteo; Kahn, Daniel; de Jong, Hidde; Cinquemani, Eugenio: On the identifiability of metabolic network models (2013)