Real-time gene expression: statistical challenges in design and inference.

Area of Science:

• * Computational Biology and Systems Biology
• * Statistical Modeling and Data Analysis

Background:

• * Microtechnologies enable high-throughput, real-time monitoring of gene expression in live cells.
• * Analyzing this dynamic data presents statistical challenges, particularly confounding between experimental treatments and natural cell cycle variations.
• * Accurate inference of treatment effects requires methods that can disentangle these biological processes.

Purpose of the Study:

• * To develop and validate a statistical model for inferring gene expression modulation in real-time.
• * To specifically address and correct for confounding effects of cell cycle variation in single-shock experiments.
• * To provide a robust framework for analyzing dynamic gene expression data.

Main Methods:

• * Development of a semi-wavelet non-linear dynamic regression model.
• * Application of a Bayesian approach for model estimation and inference.
• * Validation using a case study with publicly available gene expression data.

Main Results:

• * The proposed model successfully infers gene expression modulation due to treatment shocks.
• * The model demonstrates improved accuracy by accounting for cell cycle variation compared to analyses that ignore it.
• * Bayesian inference provides a robust framework for parameter estimation and uncertainty quantification.

Conclusions:

• * The semi-wavelet dynamic regression model offers a powerful tool for analyzing real-time gene expression data.
• * Accounting for cell cycle variation is crucial for accurate interpretation of treatment effects in dynamic gene expression studies.
• * This approach enhances our ability to understand gene regulation in response to stimuli.