Open Access Research

Relations between the set-complexity and the structure of graphs and their sub-graphs

Tomasz M Ignac12*, Nikita A Sakhanenko1 and David J Galas12

Author Affiliations

1 Institute for Systems Biology, 401 N. Terry Avenue, Seattle, WA 98109, USA

2 Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, 7, Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg

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EURASIP Journal on Bioinformatics and Systems Biology 2012, 2012:13  doi:10.1186/1687-4153-2012-13

Published: 21 September 2012

Abstract

We describe some new conceptual tools for the rigorous, mathematical description of the “set-complexity” of graphs. This set-complexity has been shown previously to be a useful measure for analyzing some biological networks, and in discussing biological information in a quantitative fashion. The advances described here allow us to define some significant relationships between the set-complexity measure and the structure of graphs, and of their component sub-graphs. We show here that modular graph structures tend to maximize the set-complexity of graphs. We point out the relationship between modularity and redundancy, and discuss the significance of set-complexity in this regard. We specifically discuss the relationship between complexity and entropy in the case of complete-bipartite graphs, and present a new method for constructing highly complex, binary graphs. These results can be extended to the case of ternary graphs, and to other multi-edge graphs, which are fundamentally more relevant to biological structures and systems. Finally, our results lead us to an approach for extracting high complexity modular graphs from large, noisy graphs with low information content. We illustrate this approach with two examples.

Keywords:
Set-complexity; Biological networks; Modularity; Modular graphs; Bipartite graphs; Multi-partite graphs