Minimal output sets for identifiability
Journal article, 2012

Ordinary differential equation models in biology often contain a large number of parameters that must be determined from measurements by parameter estimation. For a parameter estimation procedure to be successful, there must be a unique set of parameters that can have produced the measured data. This is not the case if a model is not uniquely structurally identifiable with the given set of outputs selected as measurements. In designing an experiment for the purpose of parameter estimation, given a set of feasible but resource-consuming measurements, it is useful to know which ones must be included in order to obtain an identifiable system, or whether the system is unidentifiable from the feasible measurement set. We have developed an algorithm that, from a user-provided set of variables and parameters or functions of them assumed to be measurable or known, determines all subsets that when used as outputs give a locally structurally identifiable system and are such that any output set for which the system is structurally identifiable must contain at least one of the calculated subsets. The algorithm has been implemented in Mathematica and shown to be feasible and efficient. We have successfully applied it in the analysis of large signalling pathway models from the literat

signalling pathways

experimental design

observability

nonlinear systems

structural identifiability

Lie point symmetries

Author

Milena Anguelova

Imego AB - The Institute of Micro and Nanotechnology

Johan Karlsson

Fraunhofer-Chalmers Centre

Mats Jirstrand

Fraunhofer-Chalmers Centre

Mathematical Biosciences

0025-5564 (ISSN)

Subject Categories

Computational Mathematics

Information Science

Control Engineering

Roots

Basic sciences

Driving Forces

Innovation and entrepreneurship

Areas of Advance

Life Science Engineering (2010-2018)

DOI

10.1016/j.mbs.2012.04.005

More information

Latest update

5/25/2020