Estimation of the fractal network properties of multicellular life

Multicellular life is based on the ability of cells to divide, differentiate, cooperate and die in a controlled and organised manner, generating and maintaining an organism. The temporal distribution of division, differentiation and death determines the cellular composition of the organism at any particular point in time. Like these ontogenetic events, phylogenetic development takes place with the changes in total cell numbers, the allocation of these cells to different tissues and the disappearance of certain tissues. Fractal properties of complex networks, a result of growth, can be estimated by box counting, whereby the topological properties of the network are mapped by changing the resolution of examination, that is changing the size of the boxes used to identify and group network components. Here we develop the concept of cellular box-counting, referring to the fact that cells can be grouped on various levels of hierarchy and these various levels can be interpreted as boxes of different linear sizes. We apply the method to data representing distinct stages and groups of evolution of life and interpret the network properties of brown algae, green plants and animals. The results are in agreement with previously established values of degree exponent of biological networks and provide clues to the differences in the network organization of multicellular life. József Prechl ( ) Corresponding author: [email protected] : Conceptualization, Data Curation, Formal Analysis, Methodology, Visualization, Writing – Original Draft Preparation, Author roles: Prechl J Writing – Review & Editing No competing interests were disclosed. Competing interests: Prechl J. How to cite this article: Estimation of the fractal network properties of multicellular life by cellular box-counting [version 1; 2017, :1980 (doi: ) referees: awaiting peer review] F1000Research 6 10.12688/f1000research.13070.1 © 2017 Prechl J. This is an open access article distributed under the terms of the , which Copyright: Creative Commons Attribution Licence permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The author(s) declared that no grants were involved in supporting this work. Grant information: 09 Nov 2017, :1980 (doi: ) First published: 6 10.12688/f1000research.13070.1 Referee Status: AWAITING PEER REVIEW 09 Nov 2017, :1980 (doi: ) First published: 6 10.12688/f1000research.13070.1 09 Nov 2017, :1980 (doi: ) Latest published: 6 10.12688/f1000research.13070.1 v1 Page 1 of 5 F1000Research 2017, 6:1980 Last updated: 09 NOV 2017