Bayesian Modeling of Gene Regulatory Networks in Colorectal Cancer Organoids

Area of Science:

• Computational Biology
• Genomics
• Cancer Research

Background:

• Colorectal cancer poses a significant global health challenge.
• Tumor organoids (tumoroids) are valuable biological models due to their ability to mimic human tissue complexity.
• Analyzing gene expression in tumoroids is difficult due to cellular heterogeneity and temporal changes.

Purpose of the Study:

• To develop a comprehensive Bayesian framework for modeling gene expression dynamics in colorectal tumoroids.
• To identify gene regulatory networks (GRNs) driving tumor progression.
• To enhance the analysis of complex biological systems using advanced statistical methods.

Main Methods:

• A nonparametric Dirichlet process mixture model (DPMM) was used to cluster genes by temporal expression patterns.
• A sparse regression scheme with Horseshoe+ priors was employed to construct GRNs from clustered genes.
• The framework was designed to handle high-dimensional gene expression data from tumoroid developmental trajectories.

Main Results:

• The proposed Bayesian framework effectively models gene expression dynamics in colorectal tumoroids.
• Key regulatory mechanisms underlying tumor progression were elucidated through constructed GRNs.
• The approach demonstrated robust performance in capturing complex, high-dimensional relationships in the data.

Conclusions:

• The developed Bayesian framework provides a powerful tool for analyzing gene expression in tumoroids.
• Insights gained can inform personalized treatment strategies for colorectal cancer.
• This study highlights the utility of Bayesian methods in unraveling complex biological processes.