Quantitative Imaging Analysis of NF-κB for Mathematical Modeling Applications

Area of Science:

• Systems biology
• Molecular biology
• Bioinformatics

Background:

• Mathematical models integrate diverse experimental data for in silico biological system reconstitution.
• Next-generation sequencing (NGS) provides high-dimensional snapshots but lacks temporal dynamics.
• Live-cell imaging offers temporal data crucial for complementing NGS in modeling.

Purpose of the Study:

• To introduce an analytical method for quantifying transcription factor dynamics.
• To bridge the gap between NGS data limitations and mathematical modeling.
• To enable more accurate predictions of transcriptional regulation.

Main Methods:

• Development of an analytical method for quantifying transcription factor dynamics.
• Integration of fluorescence live-cell imaging with NGS data analysis.
• Application to nuclear factor kappaB (NF-κB) aggregate dynamics.

Main Results:

• The proposed method quantifies the dynamics of transcription factors, such as NF-κB.
• It successfully supplements temporal information missing in NGS data.
• The approach facilitates the integration of NGS data into mathematical models.

Conclusions:

• Fluorescence live-cell imaging is vital for enhancing NGS data in mathematical modeling.
• The developed analytical method can quantify transcription factor dynamics.
• This method shows potential applicability to other similarly regulated transcription factors.