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An Algorithmic Information Calculus for Causal Discovery and Reprogramming Systems.

Hector Zenil1, Narsis A Kiani2, Francesco Marabita3

  • 1Algorithmic Dynamics Lab, Center for Molecular Medicine, Karolinska Institutet, Stockholm 171 76, Sweden; Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, Karolinska Institutet, Solna, Stockholm 171 76, Sweden; Oxford Immune Algorithmics, Reading RG1 3EU, UK; Science for Life Laboratory, Solna 171 65, Sweden; Algorithmic Nature Group, LABORES for the Natural and Digital Sciences, Paris 75006, France.

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Summary
This summary is machine-generated.

We present a novel method using algorithmic information content to control and reprogram complex systems. This approach involves targeted interventions and information estimation, validated on diverse networks including biological systems.

Keywords:
AlgorithmsComplex SystemsComputer ScienceGene NetworkSystems Biology

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Area of Science:

  • Complex systems analysis
  • Information theory
  • Computational biology

Background:

  • Understanding and controlling complex dynamical systems is a significant challenge.
  • Algorithmic information content offers a theoretical framework for system complexity.
  • Existing methods lack effective control mechanisms for networked systems.

Purpose of the Study:

  • To introduce and develop a method for controlling systems via algorithmic information content.
  • To demonstrate the feasibility of using algorithmic information as a steering handle in dynamical phase space.
  • To validate the method on various discrete and biological networks.

Main Methods:

  • Applying controlled interventions to networked systems.
  • Estimating the effect of interventions on algorithmic information content.
  • Reconstructing phase space and generative rules of discrete dynamical systems.
  • Validating the causal calculus on graphs and biological networks (E. coli, human cells).

Main Results:

  • Demonstrated that algorithmic information content can steer dynamical phase space.
  • Successfully reconstructed phase spaces and generative rules for cellular automata.
  • Validated the interventional calculus on large sets of small graphs, larger networks, and biological networks.
  • Showcased applicability to genetic networks and human cellular differentiation data.

Conclusions:

  • Algorithmic information content provides a powerful tool for system control and reprogramming.
  • The developed method offers a robust framework for analyzing and manipulating complex systems.
  • This approach has significant implications for computational biology and network science.