System identification of signaling dependent gene expression with different time-scale data

Area of Science:

• Cellular signaling dynamics
• Systems biology
• Gene expression analysis

Background:

• Cells process signals over minutes to hours, influencing fate decisions like differentiation.
• Existing system identification methods struggle with disparate time scales (minutes vs. hours/days).

Purpose of the Study:

• To develop a system identification method capable of analyzing biological data across vastly different time scales.
• To reveal input-output relationships between cellular signaling and gene expression.
• To enable prediction and validation of cellular responses.

Main Methods:

• Utilized compressed sensing technology to recover signals from data with missing time points.
• Measured ERK and CREB phosphorylation, immediate early gene expression, and decoder gene mRNA levels.
• Applied system identification to PC12 cell differentiation data.

Main Results:

• Identified input-output relationships between signaling and gene expression, characterizing sensitivity (graded/switch-like), time delay, and gain.
• Demonstrated signal transfer efficiency in cellular differentiation.
• Successfully predicted and validated the identified system using pharmacological perturbations.

Conclusions:

• Developed a versatile system identification method for analyzing data across different time scales.
• Provided insights into the quantitative dynamics of signaling pathways controlling cell fate.
• Enabled prediction of cellular responses to perturbations.