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.
Highlights
protein interactome networks (PINs) [1] are almost pervasively studied in genomics, but especially when H
Each subinteractome was analyzed according to the characterizing biological process. is process was called PIN fragmentation. e natural consequence of fragmentation is that speci c PINs are built whose connectivity patterns re ect the dynamics inherent to the separately involved biological process. e list is reported below
As a rst check, distributional properties are computed through the power laws, that is, pp(kk) ∝ kk−αα, and reported in Figure 1 with reference to each fragPIN and the corresponding estimated exponents too. e distributions appear quite different, as expected, and this depends on the structure and size of the fragPIN which is considered
Summary
PIN [1] are almost pervasively studied in genomics, but especially when H. A PIN map consists of three main types of constituent entities: positive data, that is, the measured physical interactions, which represent the real evidence; negative data, that is, the interactions that are not present, considered as latent variables; and uncertain data, that is, noisy information (false positives) for which partial recovery is possible through data integration. This mix is usually measured through both transient and persistent PIN dynamics, together with the related degree of uncertainty
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