Table of Contents
ISRN Biomathematics
Volume 2012 (2012), Article ID 192031, 7 pages
http://dx.doi.org/10.5402/2012/192031
Research Article

Constrained Network Modularity

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

Received 15 August 2012; Accepted 23 September 2012

Academic Editors: J. Chow and J. M. Peregrin-Alvarez

Copyright © 2012 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

Static representations of protein interactions networks or PIN reflect measurements referred to a variety of conditions, including time. To partially bypass such limitation, gene expression information is usually integrated in the network to measure its “activity level.” In general, the entire PIN modular organization (complexes, pathways) can reveal changes of configuration whose functional significance depends on biological annotation. However, since network dynamics are based on the presence of different conditions leading to comparisons between normal and disease states, or between networks observed sequentially in time, our working hypothesis refers to the analysis of differential networks based on varying modularity and uncertainty. Two popular methods were applied and evaluated, k-core and Q-modularity, over a reference yeast dataset comprising a PIN of literature-curated data obtained from the fusion of heterogeneous measurements sources. While the functional aspect of interest is cell cycle and the corresponding interactions were isolated, the PIN dynamics were externally induced by time-course measured gene expression values, which we consider one of the “modularity drivers.” Notably, due to the nature of such expression values referred to the “just-in-time method,” we could specialize our approach according to three constrained modular configurations then comparatively assessed through local entropy measures.