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Quantifying collectivity.

Bryan C Daniels1, Christopher J Ellison2, David C Krakauer3

  • 1ASU-SFI Center for Biosocial Complex Systems, Arizona State University, United States.

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|February 15, 2016
PubMed
Summary
This summary is machine-generated.

Biological systems achieve coherent function through component interactions. We introduce amplification and decomposability to quantify collective behavior in biological circuits, aiding in understanding and manipulating these systems.

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Area of Science:

  • Neuroscience
  • Systems Biology
  • Computational Biology

Background:

  • Biological functions arise from component interactions, even with misaligned interests.
  • The brain exemplifies complex systems producing unified output, yet theories explaining this remain incomplete.
  • Understanding the collectivity of component behavior is crucial for deciphering system-level function.

Purpose of the Study:

  • To introduce and quantify two novel properties of collectivity: amplification and decomposability.
  • To provide a framework for analyzing how component interactions lead to emergent system behavior.
  • To identify key components and subgroups for experimental manipulation in biological circuits.

Main Methods:

  • Utilizing information theory and statistical physics approaches to quantify collectivity.
  • Defining amplification as the sensitivity of large-scale behavior to small-scale information.
  • Defining decomposability to assess the reducibility of aggregate behavior to individual or subgroup contributions.

Main Results:

  • Developed quantitative measures for amplification and decomposability.
  • Demonstrated how these measures can be applied to analyze collective behavior in biological systems.
  • These properties offer insights into the causal structure and controllability of biological circuits.

Conclusions:

  • Amplification and decomposability are key metrics for understanding collective behavior in biological systems.
  • These measures can help identify critical components and interactions within complex biological circuits.
  • The framework facilitates experimental studies on the evolution and control of biological functions.