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. 1. Introduction Despite the fact that research on PIN [1] is quite mature at both methodological (systems biology) and applied (biomedical and clinical bioinformatics) levels, there are still some domains that remain partially unexplored, in particular from an integrative dynamic standpoint. The first attribute, that is, integrative, includes the consideration of complementary omic layers that provide information on causality, for instance (through gene coexpression, transcription factors, microRNAs, etc.). The second attribute, that is, dynamic, aims at investigating differential properties of networks, and it is based on the assessment of the effects of different conditions at which network properties are measured. The field of “differential network biology” has been already explored from a variety of differential conditions, such as expression during drug and stress response [2] or condition-responsive subnetwork identification [3]. Recently, Ideker and Krogan [4] reviewed the field, suggesting new interesting directions. Currently, some of the main limitations that are encountered can be summarized as follows.(i)The available
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