Sequential design of single-cell experiments to identify discrete stochastic models for gene expression
Joshua Cook1, Eric Ron1, Dmitri Svetlov1
1Department of Chemical Engineering and the School of Biomedical Engineering at Colorado State University, Fort Collins, CO, USA.
Proceedings of the ... IEEE Conference on Decision & Control. IEEE Conference on Decision & Control
|July 10, 2025
Summary
This study introduces a smart experimental design strategy for gene regulation research. It uses preliminary experiments and computational models to reduce the number of costly single-cell experiments needed for accurate predictions.
Area of Science:
- Systems Biology
- Computational Biology
- Molecular Biology
Background:
- Accurate prediction of heterogeneous cellular responses is crucial for controlling gene regulation.
- Single-cell experiments and discrete stochastic models offer predictive capabilities but face challenges due to vast design possibilities and resource limitations.
- Limited prior information and the cost/time of experiments necessitate efficient design strategies.
Purpose of the Study:
- To develop a sequential experiment design strategy for optimizing single-cell experiments in gene regulation studies.
- To reduce the overall number of experiments required to achieve a predictive, quantitative understanding of cellular responses.
- To integrate computational modeling with experimental design for enhanced biological insight.
Main Methods:
- Developed a sequential design strategy starting with preliminary experiments.
- Integrated chemical master equations for computing single-cell data likelihood.
- Employed Bayesian inference for sampling posterior parameter distributions.
- Utilized a finite state projection based Fisher information matrix to estimate expected information for future experiments.
Main Results:
- Demonstrated a practical working principle to minimize the number of required experiments.
- Successfully reduced the experimental burden while achieving predictive, quantitative understanding.
- Validated the strategy using both simulated and real single-cell data.
Conclusions:
- The proposed sequential experiment design strategy efficiently guides research towards quantitative understanding of gene regulation.
- This approach optimizes the use of resources in single-cell biology, making complex studies more feasible.
- The integration of computational methods with experimental design is key to advancing systems-level biological understanding.
