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New scaling relation for information transfer in biological networks
Hyunju Kim1, Paul Davies1, Sara Imari Walker2
1BEYOND: Center for Fundamental Concepts in Science, Arizona State University, Tempe, AZ, USA.
Journal of the Royal Society, Interface
|December 25, 2015
Summary
Biological networks, like yeast cell cycles, process more information and exhibit unique scaling relations compared to random networks. This distinct informational architecture arises from topology and dynamics, particularly in control nodes.
Area of Science:
- Systems Biology
- Network Science
- Computational Biology
Background:
- Biological networks exhibit complex informational architectures.
- Understanding these architectures is key to deciphering cellular function.
Purpose of the Study:
- To quantify and compare the informational architecture of biological networks (yeast cell cycle) with random network models.
- To identify unique information processing characteristics of evolved biological networks.
Main Methods:
- Analysis of Boolean network models for fission yeast (Schizosaccharomyces pombe) and budding yeast (Saccharomyces cerevisiae) cell-cycle networks.
- Comparison with Erdös-Rényi and scale-free random network ensembles.
- Quantification of information transfer and scaling relations between network nodes.
Main Results:
- Biological networks process more information on average than random networks.
- Biological networks display a distinct scaling relation in information transfer, differentiating them from random networks even with similar topological properties.
- A specific regime of this scaling relation, linked to control nodes, highlights biological distinctiveness.
Conclusions:
- The informational architecture of biological networks is an emergent property of their topology and dynamics.
- Biologically evolved networks possess unique informational processing capabilities that distinguish them from random network models.
- Quantitative analysis of information processing provides insights into the functional properties of biological systems.
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