Redundancy in information transmission in a two-step cascade

Area of Science:

• Biophysics
• Biochemical Systems Analysis
• Information Theory

Background:

• Signal transmission in biological systems often involves multi-step cascades.
• Understanding information flow and noise in these cascades is crucial for cellular function.
• Stochasticity and noise significantly impact the fidelity and efficiency of biological signaling.

Purpose of the Study:

• To develop a stochastic framework for analyzing signal transmission in a generic two-step cascade (S→X→Y).
• To quantify information transmission using variance, covariance, and partial information decomposition.
• To investigate the role of Markovian properties and redundancy in information transmission.

Main Methods:

• Utilizing a set of Langevin equations with Gaussian noise processes.
• Calculating variance and covariance for biochemical species.
• Employing partial information decomposition to assess information synergy and redundancy.
• Analyzing linear and nonlinear production terms within the cascade.

Main Results:

• The framework quantifies information transmission in a two-step cascade.
• Redundancy in information transmission is shown to be a consequence of the Markovian property.
• Calculated variance and covariance reveal insights into signal processing.
• Net synergy is determined using partial information decomposition.

Conclusions:

• The stochastic framework provides a quantitative method to study signal transmission.
• The Markovian nature of two-step cascades inherently leads to information redundancy.
• Increased redundancy positively correlates with enhanced fidelity of the signaling pathway.