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On the relationship between sloppiness and identifiability.

Oana-Teodora Chis1, Alejandro F Villaverde2, Julio R Banga2

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Dynamic systems biology models are often "sloppy," but this doesn't mean parameters are unidentifiable. Identifiability analyses are better for assessing parameter confidence and designing informative experiments.

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Area of Science:

  • Systems Biology
  • Biochemical Network Modeling
  • Computational Biology

Background:

  • Dynamic models of biochemical networks use non-linear ordinary differential equations.
  • These models often have numerous kinetic parameters requiring experimental calibration.
  • The concept of 'sloppiness' suggests parameters can vary widely without affecting model output.

Purpose of the Study:

  • To investigate the relationship between model sloppiness and parameter identifiability.
  • To clarify if sloppy models are inherently unidentifiable.
  • To guide the design of experiments for reducing parameter uncertainty.

Main Methods:

  • Analysis of dynamic systems biology models.
  • Examination of case studies.
  • Comparison of sloppiness with structural and practical identifiability.
  • Evaluation of experimental design strategies.

Main Results:

  • Sloppiness is not equivalent to a lack of identifiability; sloppy models can be identifiable.
  • Relying on sloppiness alone to assess parameter estimation can be misleading.
  • Structural and practical identifiability analyses are superior for evaluating parameter confidence.
  • Optimizing practical identifiability criteria leads to more informative experimental designs than minimizing sloppiness.

Conclusions:

  • Model sloppiness does not preclude parameter identifiability.
  • Identifiability analyses are crucial for robust parameter estimation in systems biology.
  • Experimental design should prioritize improving identifiability over reducing sloppiness.