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Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

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Published on: January 31, 2014

On linear models and parameter identifiability in experimental biological systems.

Timothy O Lamberton¹, Nicholas D Condon², Jennifer L Stow²

¹Division of Genomics & Computational Biology, Institute for Molecular Biosciences, The University of Queensland, Brisbane, QLD 4072, Australia.

Journal of Theoretical Biology

|June 3, 2014

Summary

This summary is machine-generated.

This study introduces methods for designing optimal biological experiments to ensure parameter identifiability. The approach helps determine where and how to measure to maximize parameter information from complex models.

Keywords:

Experimental design Modeling Ordinary differential equations Protein trafficking

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Area of Science:

Systems Biology
Mathematical Biology
Experimental Design

Background:

Estimating model parameters from experimental data is crucial in biological sciences.
Parameter identifiability, the ability to uniquely determine model parameters, remains a significant challenge.

Purpose of the Study:

To develop methods for designing optimal experiments that ensure structural parameter identifiability.
To provide biologists and modelers with tools for effective experimental design.

Main Methods:

A general methodology for extracting parameters of linear models using transfer functions.
A framework for identifiability analysis of complex models via linked models.
Linked models: output of one model serves as input to an experimentally measured model.

Main Results:

Demonstrated applicability of the linked model framework for identifying measured sub-models and recovering inputs from unmeasured sub-models.
Showcased insights into "where to measure" and "which experimental scheme" through parameter extraction and linked model frameworks.
Provided tools to guide experimental design for maximizing parameter information based on model structure.

Conclusions:

The developed methodologies enhance the ability to reliably estimate model parameters from experimental data.
These tools are valuable for optimizing experimental design in biological modeling.
The approach effectively addresses parameter identifiability challenges in complex biological systems.