Highly Constrained Kinetic Models for Single-Cell Gene Expression Analysis
Hyeon Jin Cho1,2, Christopher H Bohrer2, Pawel Trzaskoma3
1Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, USA.
Biorxiv : the Preprint Server for Biology
|June 5, 2026
Summary
A new kinetic model integrates single-cell RNA sequencing and imaging data to study gene transcription dynamics. This model reveals that 3-state models offer improved insights into gene regulation and transcription factor mechanisms.
Area of Science:
- Molecular Biology
- Systems Biology
- Biophysics
Background:
- Single-cell RNA sequencing (scRNA-seq) and single-molecule tracking (SMT) offer high-resolution views of cellular processes.
- Integrating these techniques into a unified model for gene transcription dynamics remains a challenge.
Purpose of the Study:
- To develop a kinetic model integrating diverse datasets for simulating gene transcription.
- To compare the efficacy of 3-state versus 2-state models in describing gene transcription.
- To uncover conserved regulatory principles and transcription factor mechanisms.
Main Methods:
- Development of a novel kinetic model for gene transcription.
- Integration of steady-state and time-resolved experimental data.
- Application of the model to single-cell RNA sequencing (scRNA-seq) datasets.
Main Results:
- A 3-state model significantly improves upon the traditional 2-state model for gene transcription.
- Identification of two conserved dimensionless quantities from rate equations.
- Inference of kinetic rates and biochemical insights into transcription factor action.
Conclusions:
- The proposed kinetic model provides a powerful framework for analyzing gene transcription dynamics.
- 3-state models offer advantages, including kinetic proofreading, for cellular gene regulation.
- The model facilitates the study of transcription factor mechanisms and responses to perturbations.
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