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Optimal Design of Single-Cell Experiments within Temporally Fluctuating Environments.

Zachary R Fox¹, Gregor Neuert², Brian Munsky³

¹Inria Saclay Ile-de-France, Palaiseau 91120, France Institut Pasteur, USR 3756 IP CNRS Paris, 75015, France School of Biomedical Engineering, Colorado State University Fort Collins, CO 80523, USA.

|September 28, 2020

Summary

This summary is machine-generated.

This study introduces a new computational method, the Finite State Projection based Fisher Information Matrix (FSP-FIM), to optimize complex biological experiments. The FSP-FIM enhances quantitative insight and reduces uncertainty in parameter estimation for stochastic gene regulatory systems.

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Area of Science:

Systems Biology
Computational Biology
Molecular Biology

Background:

Modern biological experiments generate complex data, posing challenges for quantitative analysis and experimental design.
Computational models of stochastic biological systems are crucial for understanding behavior and inferring parameters.
Traditional methods like the Fisher Information Matrix (FIM) have limitations with non-Gaussian biological systems.

Purpose of the Study:

To develop and apply the Finite State Projection based Fisher Information Matrix (FSP-FIM) for quantitative analysis of stochastic gene regulatory systems.
To optimize experimental design for complex biological systems, aiming to maximize quantitative insight and minimize parameter estimation uncertainty.
To validate the FSP-FIM approach experimentally in a yeast stress response model.

Main Methods:

Developed the FSP-FIM analysis for stochastic gene regulatory models, specifically a stress response model in *S. cerevisae* under time-varying MAPK induction.
Utilized FSP-FIM to optimize cell quantification timing and number for maximal parameter learning.
Extended FSP-FIM to assess the impact of measurement times and genetic modifications on environmental sensing uncertainty.

Main Results:

Verified the FSP-FIM analysis for a yeast osmotic shock model.
Optimized experimental parameters (cell number, time points) to maximize information gain about model parameters.
Demonstrated FSP-FIM's ability to rank single-cell experiments for minimizing estimation uncertainty of NaCl concentrations.

Conclusions:

The FSP-FIM is a powerful tool for designing and optimizing quantitative biological experiments involving complex stochastic systems.
This approach effectively bridges quantitative modeling with experimental data collection, enabling more insightful biological discoveries.
The FSP-FIM facilitates the reduction of uncertainty in parameter estimation and environmental sensing in biological systems.