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Identifying regulation with adversarial surrogates.

Ron Teichner1,2, Aseel Shomar2,3, Omri Barak2,4

  • 1Viterbi Department of Electrical & Computer Engineering, Technion, Israel Institute of Technology, 32000 Haifa, Israel.

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Summary
This summary is machine-generated.

This study introduces a new data-driven algorithm, Identifying Regulation with Adversarial Surrogates (IRAS), to uncover the control objectives of homeostatic mechanisms. The method identifies what biological and physical systems prioritize maintaining, even in complex scenarios.

Keywords:
artificial neural networksbiological controlbiological regulationcomputational biologydata analysis

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Area of Science:

  • Systems Biology
  • Control Theory
  • Data Science

Background:

  • Homeostasis is crucial for biological systems, involving complex internal regulation and control processes.
  • Identifying the specific control objectives of these homeostatic mechanisms from system data is challenging.
  • Understanding these objectives is key to comprehending system resilience and function.

Purpose of the Study:

  • To develop a robust, data-driven method for identifying the control objectives of homeostatic mechanisms.
  • To answer the fundamental question: "What does the system care about?" by analyzing temporal measurements.
  • To create an algorithm capable of inferring system priorities from observed data.

Main Methods:

  • Proposes the Identifying Regulation with Adversarial Surrogates (IRAS) algorithm, a data-driven approach.
  • Utilizes an iterative, two-player adversarial framework with deep neural networks.
  • The first player minimizes a "coefficient of regulation" (invariance measure), while the second player introduces temporal complexity via surrogate data.

Main Results:

  • Demonstrates excellent empirical results on four synthetic and one natural dataset.
  • Successfully identifies control objectives as combinations of observed variables.
  • Shows the algorithm's versatility by extracting conserved quantities (e.g., energy, momentum) in physical systems.

Conclusions:

  • The IRAS algorithm provides a powerful new tool for uncovering hidden control objectives in complex systems.
  • This method advances our understanding of biological homeostasis and physical conservation laws.
  • The data-driven, adversarial approach offers a novel way to analyze system dynamics and priorities.