%0 Journal Article
%T Multiscale fragPIN Modularity
%A Enrico Capobianco
%J ISRN Genomics
%D 2013
%R 10.1155/2013/307608
%X 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. 1. Introduction PIN [1] are almost pervasively studied in genomics, but especially when H. Sapiens is considered they present limitations due to sparse coverage and suboptimal accuracy of both experimental (yeast two-hybrid, for instance) and in silico measurements (literature mining, orthology, etc.) [2, 3]. This overall uncertainty is reflected in a pathological presence of false positives and negatives and ultimately complicates data mining and analysis tasks. In order to bypass the complexities induced by such factors, data integration strategies are widely pursued (for instance, studies in [4, 5] have become quite popular). However, a difficulty comes from the fact that the integrated entities are usually heterogeneous, and thus normalization and rescaling need to be considered. An excellent example of the complexity underlying a sequence of integrative omics tasks is offered by the personal omics profiling work recently published by Chen et al. [6], soon considered a reference for personalized medicine research. The working hypothesis of this short paper is to adopt an opposite investigation strategy compared to
%U http://www.hindawi.com/journals/isrn.genomics/2013/307608/