Optimal information transfer in enzymatic networks: A field theoretic formulation
Himadri S Samanta1, Michael Hinczewski2, D Thirumalai1
1Department of Chemistry, The University of Texas at Austin, Texas 78712, USA.
Physical Review. E
|January 20, 2018
Summary
This study introduces a field-theoretic approach to quantify signal transmission errors in enzymatic networks, revealing insights into noise reduction and optimal information transfer in biological signaling pathways.
Area of Science:
- Biophysics
- Biochemistry
- Theoretical Biology
Background:
- Enzymatic networks transmit signals via stochastic chemical reactions, susceptible to noise corruption.
- Signal transmission often involves cascades, like kinase-phosphatase pathways, initiated by ligand-receptor binding.
Purpose of the Study:
- To develop a general field-theoretic method for calculating signal transmission error in enzymatic networks.
- To analyze noise reduction and information transfer in complex signaling cascades.
Main Methods:
- Developed a general field-theoretic approach to model signal transmission error.
- Applied the theory to push-pull networks and reaction cascades.
- Used simulations to compare theoretical predictions with discrete molecular behavior.
Main Results:
- The field-theoretic approach accurately reproduces results from umbral calculus for simple networks.
- Optimal information transfer in a two-module cascade involves a time delay.
- Linear theory predictions align with simulations for minimum error but deviate for signal propagation error in certain parameter ranges.
Conclusions:
- A field-theoretic formulation provides a systematic framework for analyzing error propagation in complex stochastic biological signaling networks.
- Second-order perturbative corrections improve agreement between simulations and theoretical predictions.
- The study highlights the utility of field theory in understanding noise and information flow in biological systems.
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