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Updated: Apr 5, 2026

Mimicking the Function of Signaling Proteins: Toward Artificial Signal Transduction Therapy

Mimicking the Function of Signaling Proteins: Toward Artificial Signal Transduction Therapy

Published on: September 29, 2016

Information transfer through a signaling module with feedback: A perturbative approach.

Gerardo Aquino¹, Martin Zapotocky²

¹Department of Life Sciences, Imperial College, SW7 2AZ London, UK.

|August 23, 2015

Summary

Biological cells use feedback loops in signaling pathways. This study quantifies information transfer in a feedback module, finding total information depends on activation/inactivation events, not feedback strength directly.

Keywords:

Communication channel Feedback loop Information theory Non-Markovian process Poisson process Signal transduction

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Area of Science:

Biochemistry
Cell Biology
Systems Biology

Background:

Cellular signaling pathways are crucial for biological functions.
Feedback loops are common in these pathways, regulating cellular responses.
Understanding information transfer through these modules is key to deciphering cellular behavior.

Purpose of the Study:

To analyze information transfer in a biological signaling module with biochemical feedback.
To quantify the rate of information gain about the input from observing the output.
To determine how feedback strength influences information transfer.

Main Methods:

Analysis of a prototypical signaling module with stochastic switching between inactive and active states.
Utilizing a novel perturbative approach to compute information transfer rates.
Deriving analytical results for information gain based on feedback and input strengths.

Main Results:

An explicit analytical result for information gain was obtained, valid to first order in feedback strength.
The information gain rate was computed as a function of input and feedback.
Total information gained over time depends on feedback strength solely through the number of activation/inactivation events.

Conclusions:

The study provides a quantitative framework for understanding information processing in biological signaling with feedback.
Feedback strength's influence on total information is indirect, mediated by the system's dynamic events.
This work contributes to the broader understanding of cellular information processing and signal transduction.