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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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A global parallel model based design of experiments method to minimize model output uncertainty.

Jason N Bazil¹, Gregory T Buzzard, Ann E Rundell

¹Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907, USA.

Bulletin of Mathematical Biology

|October 13, 2011

Summary

This study introduces a novel model-based experiment design method using sparse grids and scenario trees. It effectively constrains biological system uncertainty even with limited data, outperforming traditional approaches.

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Area of Science:

Systems Biology
Computational Biology
Experimental Design

Background:

Traditional model-based experiment design often relies on local methods like Fisher Information Matrix, requiring uncertain initial parameter estimates.
Limited data and high parameter uncertainty in biological systems pose significant challenges for existing design of experiment algorithms.
Sequential experimental design can be inefficient and may fail when initial parameter estimates are unreliable.

Purpose of the Study:

To develop a novel, computationally efficient approach for designing informative sequences of experiments (parallel design).
To constrain dynamical uncertainty in biological system responses within experimentally detectable limits, irrespective of initial parameter estimates.
To provide a robust method for experiment design in highly uncertain biological systems with limited data.

Main Methods:

Utilizes computationally efficient sparse grids and scenario trees for parallel experimental design.
Requires only a bounded uncertain parameter space, eliminating the need for initial parameter estimates.
Selects experimental design points to minimize uncertainty in predicted dynamics of measurable system responses.

Main Results:

Demonstrates the ability to extract useful information from mathematical models where traditional methods fail.
Successfully constrains model output dynamics within experimentally resolvable limits for a T cell activation model (2D to 19D).
The designed experiments effectively reduce uncertainty in biological system characterization.

Conclusions:

The proposed method offers a robust and effective approach for model-based experiment design in highly uncertain biological systems.
It overcomes limitations of traditional methods, particularly when dealing with limited data and unknown parameter values.
The modular design allows for integration with other methodologies like input design and model discrimination.