An information-theoretic framework for deciphering pleiotropic and noisy biochemical signaling

Area of Science:

• Biochemistry
• Systems Biology
• Information Theory

Background:

• Cell signaling pathways exhibit pleiotropy and heterogeneity, complicating biochemical descriptions.
• Quantitative models are needed to link complex stimuli (inputs) to effector activities (outputs).
• Existing information theory models lack tools for multi-input/output biochemical signaling complexity.

Purpose of the Study:

• To develop an information theory modeling framework for multi-input/output biochemical signaling.
• To analyze temporal dynamics and signal flow through shared network components.
• To enable analysis without limitations from response variability.

Main Methods:

• Developed a novel information theory modeling framework.
• Incorporated analysis of temporal dynamics.
• Enabled assessment of signal flow in complex networks.

Main Results:

• The framework efficiently analyzes models with multiple inputs and outputs.
• It accounts for temporal dynamics and signal flow through shared components.
• It successfully explains how cells differentiate between type I and type III interferon variants despite shared signaling effectors.

Conclusions:

• The developed framework enhances the application of information theory to complex cell signaling.
• It provides a robust method for understanding signal encoding and discrimination in biological systems.
• This approach offers insights into how cells recognize specific signaling molecules like interferon variants.