Estimation of non-equilibrium transition rate from gene expression data.

Area of Science:

• Complex Systems Dynamics
• Statistical Physics
• Computational Biology

Background:

• Energy landscape theory is crucial for understanding complex system dynamics.
• Estimating transition rates in non-equilibrium systems requires considering both energy landscapes and probability flux.
• Existing methods often struggle with accurate transition rate estimation from data, especially for non-equilibrium systems.

Purpose of the Study:

• To develop a novel data-driven approach for estimating non-equilibrium transition rates.
• To improve the accuracy of transition rate calculations in complex physical and biological systems.
• To provide a robust method applicable to systems like gene regulatory networks and cell differentiation.

Main Methods:

• Combines kernel density estimation with non-equilibrium transition rate theory.
• Introduces a nonparametric density estimation method for improved landscape reconstruction.
• Incorporates probability flux and a novel saddle point analysis for enhanced accuracy.

Main Results:

• The proposed approach demonstrates superior performance in estimating transition rates from data compared to previous methods.
• Successfully applied to a simplified cell fate decision model.
• Validated on a high-dimensional stem cell differentiation model, showcasing practical applicability.

Conclusions:

• The developed data-driven method offers a significant advancement in quantifying transition rates for non-equilibrium systems.
• This approach provides a powerful tool for analyzing complex biological and physical phenomena.
• The method's flexibility allows for broad application across diverse scientific domains.