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Proceedings 2017, 1(3), 207; doi:10.3390/IS4SI-2017-04107

Information Dynamics, Computation and Causality in Reprogramming Artificial and Biological Systems

Department of Computer Science, University of Oxford, Oxford OX1 3BD, UK
Department of Medicina Solna, Karolinska Institute, Stockholm 171 76, Sweden
Presented at the IS4SI 2017 Summit DIGITALISATION FOR A SUSTAINABLE SOCIETY, Gothenburg, Sweden, 12–16 June 2017.
Published: 9 June 2017
(This article belongs to the Proceedings of the IS4SI 2017 Summit DIGITALISATION FOR A SUSTAINABLE SOCIETY)
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Note: In lieu of an abstract, this is an excerpt from the first page.


In this talk, I will explain how algorithmic information theory, which is the mathematical theory of randomness; and algorithmic probability, which is the theory of optimal induction, can be used in molecular biology to study and steer artificial and biological systems such as genetic networks to even reveal some key properties of the cell Waddington landscape, and how these aspects help in tackling the challenge of causal discovery in science.[...]
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zenil, H. Information Dynamics, Computation and Causality in Reprogramming Artificial and Biological Systems. Proceedings 2017, 1, 207.

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