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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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Experimental Design for Vector Output Systems.

H T Banks¹, K L Rehm¹

¹Center for Research in Scientific Computation Center for Quantitative Sciences in Biomedicine N.C. State University Raleigh, NC.

Inverse Problems in Science and Engineering

|February 25, 2014

Summary

This summary is machine-generated.

This study presents a method for selecting optimal states and sampling times for parameter estimation in complex nonlinear dynamical systems. The approach is validated using models of HIV progression and the Calvin cycle.

Keywords:

HIV models Optimal design inverse problems optimal selection of observables and sampling times plant metabolism and growth

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Area of Science:

Dynamical Systems Theory
Computational Biology
Systems Biology

Background:

Parameter estimation and inverse problems are crucial for understanding complex nonlinear dynamical systems.
Identifying optimal states and sampling times is challenging but essential for accurate model calibration.
Applications span diverse fields, including biomedical research and plant metabolism.

Purpose of the Study:

To develop an optimal design framework for selecting informative states and sampling times.
To facilitate accurate parameter estimation in complex nonlinear dynamical systems.
To provide a practical methodology for applied scientific problems.

Main Methods:

Formulation of an optimal design problem for state selection and sampling time optimization.
Development of an iterative algorithm for implementing the proposed methodology.
Application and validation of the method on two distinct case studies.

Main Results:

The proposed methodology effectively identifies optimal states and sampling times for parameter estimation.
Demonstrated efficacy in dynamic models of Human Immunodeficiency Virus (HIV) progression.
Successful application to modeling the Calvin cycle in plant metabolism and growth.

Conclusions:

The developed optimal design approach enhances parameter estimation accuracy for nonlinear dynamical systems.
The iterative algorithm provides an effective implementation strategy.
The methodology is broadly applicable to complex biological and metabolic models.