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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.
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.
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.
