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Quantifying Spatiotemporal Parameters of Cellular Exocytosis in Micropatterned Cells

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Published on: September 16, 2020

Experimental design for dynamics identification of cellular processes.

Vu Dinh¹, Ann E Rundell, Gregery T Buzzard

¹Department of Mathematics, Purdue University, 150 N. University Street, West Lafayette, IN, 47907, USA, vdinh@purdue.edu.

Bulletin of Mathematical Biology

|February 14, 2014

Summary

This summary is machine-generated.

We introduce a new method to design experiments for studying cellular processes using nonlinear models. This approach improves upon the Maximally Informative Next Experiment (MINE) to accurately estimate system dynamics.

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Area of Science:

Systems biology
Computational biology
Biophysics

Background:

Designing experiments to characterize complex cellular processes often relies on nonlinear models.
Existing methods like Maximally Informative Next Experiment (MINE) guide experimental design but can be computationally intensive.

Purpose of the Study:

To develop a more computationally tractable and robust experimental design method for nonlinear dynamical systems.
To introduce the Expected Dynamics Estimator (EDE) for improved characterization of cellular process dynamics.

Main Methods:

Utilizing existing data to establish a probability distribution on model parameters.
Selecting the next measurement point that maximizes model output variance.
Introducing the Expected Dynamics Estimator (EDE) based on this distribution.
Proving the consistency of the EDE under various practical conditions, including noisy data and model mismatch.

Main Results:

The Expected Dynamics Estimator (EDE) demonstrates consistency, converging uniformly to the true system dynamics.
A relaxed, more computationally tractable, and robust version of the MINE approach is derived.
Numerical examples using nonlinear ordinary differential equation models of biomolecular and cellular processes illustrate the method's effectiveness.

Conclusions:

The developed EDE and relaxed MINE offer a computationally efficient and robust framework for experimental design in systems biology.
The findings advance the ability to accurately characterize the dynamics of complex cellular processes through informed experimentation.