Estimation of Dynamic Systems for Gene Regulatory Networks from Dependent Time-Course Data

Area of Science:

• Systems Biology
• Computational Biology
• Genomics

Background:

• Gene regulatory networks (GRNs) are often modeled using ordinary differential equations (ODEs).
• Time-course gene expression data is crucial for understanding GRN dynamics but can be complex, exhibiting heteroscedasticity, gene correlations, and time dependence.
• Experimental gene data is frequently noisy and limited in sample size, posing challenges for accurate dynamic modeling.

Purpose of the Study:

• To develop an ODE model that accounts for complex structures in time-course gene expression data.
• To propose a fast and stable parameter estimation method for ODE models using gene expression data.
• To enable statistical inference for ODE estimators in biological network analysis.

Main Methods:

• A novel ODE model incorporating data structures like heteroscedasticity, correlations, and time dependence.
• A generalized profiling approach combined with data smoothing techniques for efficient ODE parameter estimation.
• Application of the developed method to a zebrafish retina cell network.

Main Results:

• The proposed ODE model effectively captures complex dynamics in gene expression data.
• The estimation method demonstrates speed and stability in parameter determination.
• Statistical inference capabilities were successfully demonstrated on a real biological network.

Conclusions:

• The study presents a robust ODE modeling framework for GRNs that handles complex, noisy gene expression data.
• The developed estimation method offers an efficient and statistically sound approach for parameter inference.
• The application to the zebrafish retina network validates the model's utility in biological systems analysis.