Information Thermodynamics and Reducibility of Large Gene Networks
Swarnavo Sarkar1, Joseph B Hubbard1, Michael Halter1
1National Institute of Standards and Technology, Gaithersburg, MD 20899, USA.
Entropy (Basel, Switzerland)
|January 6, 2021
Summary
This study reveals how gene regulatory network (GRN) topology indicates if a few genes control the entire network. Information theory shows that reduced mutual information in GRNs corresponds to a thermodynamic cost, or free energy reduction.
Area of Science:
- Systems Biology
- Computational Biology
- Genetics
Background:
- Gene regulatory networks (GRNs) orchestrate crucial biological processes.
- Omics technologies identify numerous GRN components, but a core subset may govern network states.
- Understanding GRN reducibility is key to deciphering complex biological regulation.
Purpose of the Study:
- To investigate if GRN topology predicts reducibility to a small gene subset.
- To develop an information-theoretic framework for analyzing GRN states.
- To link information loss and thermodynamic properties within GRNs.
Main Methods:
- Modeling GRN interactions as information channels using information theory.
- Proposing an information loss function to identify key regulatory genes.
- Extending analysis to calculate free energy changes related to network communication.
Main Results:
- GRN topology can indicate if a small gene subset effectively represents the entire network state.
- An information loss function quantifies the conditions for GRN reducibility.
- Reduced mutual information in GRNs is coupled to a thermodynamic cost (free energy reduction).
Conclusions:
- The study provides a novel information-theoretic and thermodynamic perspective on GRN reducibility.
- Network density and communication errors influence information loss and free energy.
- This framework offers insights into the fundamental principles governing biological network organization and function.
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