Information Theory, Kolmogorov Complexity and Algorithmic Probability in Network Biology
Year: 2014
Type: preprint
Abstract: Networktheoryistodayacentraltopicincomputationalsystemsbiology as a framework to understand and reconstruct relationsamong biological components. For example, constructing networksfrom a gene expression dataset provides a set of possible hypothesesexplaining connections among genes, vital knowledge to advancingour understanding of living organisms as systems. Here we brieflysurvey aspects atthe intersectionof information theoryand networkbiology. We show that Shannon’s information entropy, Kolmogorovcomplexity and algorithmic probability quantify different aspectsof biological networks at the interplay of local and global patterndetection. Weprovideapproximationstothealgorithmicprobabilityand Kolmogorov complexity of motifs connected to the asymptotictopologicalpropertiesofnetworks.Keywords: information theory, complex networks, network mo-tifs, Kolmogorov complexity, algorithmic randomness, algorithmicprobability,biologicalnetworks. Supplemental material with Mathematica source code is available at http://www.complexitycalculator.com/zeniltegnerWiley2013.zip
Source: arXiv (Cornell University)
Authors: Héctor Zenil, Jesper Tegnér
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W165803399