Table of Contents
ISRN Genomics
Volume 2013, Article ID 307608, 10 pages
http://dx.doi.org/10.1155/2013/307608
Research Article

Multiscale fragPIN Modularity

1Center for Computational Science (CCS), University of Miami, Miami, FL 33136, USA
2Laboratory for Integrative Systems Medicine (LISM), Institute of Clinical Physiology (IFC), National Research Council (CNR), 56124 Pisa, Italy

Received 12 November 2012; Accepted 3 December 2012

Academic Editors: S. Cavallaro, A. Stubbs, and Z. Zhang

Copyright © 2013 Enrico Capobianco. 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.

Abstract

Modularity in protein interactome networks (PINs) is a central theme involving aspects such as the study of the resolution limit, the comparative assessment of module-finding algorithms, and the role of data integration in systems biology. It is less common to study the relationships between the topological hierarchies embedded within the same network. This occurrence is not unusual, in particular with PINs that are considered assemblies of various interactions depending on specialized biological processes. The integrated view offered so far by modularity maps represents in general a synthesis of a variety of possible interaction maps, each reflecting a certain biological level of specialization. The driving hypothesis of this work leverages on such network components. Therefore, subnetworks are generated from fragmentation, a process aimed to isolating parts of a common network source that are here called fragments, from which the acronym fragPIN is used. The characteristics of modularity in each obtained fragPIN are elucidated and compared. Finally, as it was hypothesized that different timescales may underlie the biological processes from which the fragments are computed, the analysis was centered on an example involving the fluctuation dynamics inherent to the signaling process and was aimed to show how timescales can be identified from such dynamics, in particular assigning the interactions based on selected topological properties.