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An Algorithmic Information Calculus for Causal Discovery and Reprogramming Systems

Hector Zenil, View ORCID ProfileNarsis A. Kiani, View ORCID ProfileFrancesco Marabita, Yue Deng, Szabolcs Elias, Angelika Schmidt, Gordon Ball, Jesper Tegnér
doi: https://doi.org/10.1101/185637
Hector Zenil
aAlgorithmic Dynamics Lab, Center for Molecular Medicine, Karolinska Institutet, Stockholm, 171 76, Sweden
bUnit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, Solna, Karolinska Institutet, Stockholm, 171 76, Sweden
cDepartment of Computer Science, University of Oxford, Oxford, OX1 3QD, UK.
dScience for Life Laboratory, Solna, 171 65, Sweden
eAlgorithmic Nature Group, LABORES for the Natural and Digital Sciences, Paris, 75006, France.
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  • For correspondence: hector.zenil@algorithmicnaturelab.org jesper.tegner@kaust.edu.sa
Narsis A. Kiani
aAlgorithmic Dynamics Lab, Center for Molecular Medicine, Karolinska Institutet, Stockholm, 171 76, Sweden
bUnit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, Solna, Karolinska Institutet, Stockholm, 171 76, Sweden
dScience for Life Laboratory, Solna, 171 65, Sweden
eAlgorithmic Nature Group, LABORES for the Natural and Digital Sciences, Paris, 75006, France.
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  • ORCID record for Narsis A. Kiani
Francesco Marabita
bUnit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, Solna, Karolinska Institutet, Stockholm, 171 76, Sweden
dScience for Life Laboratory, Solna, 171 65, Sweden
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Yue Deng
bUnit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, Solna, Karolinska Institutet, Stockholm, 171 76, Sweden
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Szabolcs Elias
bUnit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, Solna, Karolinska Institutet, Stockholm, 171 76, Sweden
dScience for Life Laboratory, Solna, 171 65, Sweden
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Angelika Schmidt
bUnit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, Solna, Karolinska Institutet, Stockholm, 171 76, Sweden
dScience for Life Laboratory, Solna, 171 65, Sweden
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Gordon Ball
bUnit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, Solna, Karolinska Institutet, Stockholm, 171 76, Sweden
dScience for Life Laboratory, Solna, 171 65, Sweden
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Jesper Tegnér
bUnit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, Solna, Karolinska Institutet, Stockholm, 171 76, Sweden
dScience for Life Laboratory, Solna, 171 65, Sweden
fBiological and Environmental Sciences and Engineering Division, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955–6900, Kingdom of Saudi Arabia
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  • For correspondence: hector.zenil@algorithmicnaturelab.org jesper.tegner@kaust.edu.sa
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Abstract

We introduce a new conceptual framework and a model-based interventional calculus to steer, manipulate, and reconstruct the dynamics and generating mechanisms of non-linear dynamical systems from partial and disordered observations based on the contributions of each of the systems, by exploiting first principles from the theory of computability and algorithmic information. This calculus entails finding and applying controlled interventions to an evolving object to estimate how its algorithmic information content is affected in terms of positive or negative shifts towards and away from randomness in connection to causation. The approach is an alternative to statistical approaches for inferring causal relationships and formulating theoretical expectations from perturbation analysis. We find that the algorithmic information landscape of a system runs parallel to its dynamic attractor landscape, affording an avenue for moving systems on one plane so they can be controlled on the other plane. Based on these methods, we advance tools for reprogramming a system that do not require full knowledge or access to the system’s actual kinetic equations or to probability distributions. This new approach yields a suite of universal parameter-free algorithms of wide applicability, ranging from the discovery of causality, dimension reduction, feature selection, model generation, a maximal algorithmic-randomness principle and a system’s (re)programmability index. We apply these methods to static (e.coli Transcription Factor network) and to evolving genetic regulatory networks (differentiating naïve from Th17 cells, and the CellNet database). We highlight their ability to pinpoint key elements (genes) related to cell function and cell development, conforming to biological knowledge from experimentally validated data and the literature, and demonstrate how the method can reshape a system’s dynamics in a controlled manner through algorithmic causal mechanisms.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted March 16, 2018.
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An Algorithmic Information Calculus for Causal Discovery and Reprogramming Systems
Hector Zenil, Narsis A. Kiani, Francesco Marabita, Yue Deng, Szabolcs Elias, Angelika Schmidt, Gordon Ball, Jesper Tegnér
bioRxiv 185637; doi: https://doi.org/10.1101/185637
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An Algorithmic Information Calculus for Causal Discovery and Reprogramming Systems
Hector Zenil, Narsis A. Kiani, Francesco Marabita, Yue Deng, Szabolcs Elias, Angelika Schmidt, Gordon Ball, Jesper Tegnér
bioRxiv 185637; doi: https://doi.org/10.1101/185637

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