Protein networks tomography Targeting cancer and associated morbidities

摘要

Networks represent powerful inference tools for the analysis of complex biological systems. Inference is especially relevant when associations between network nodes are established by focusing on modularity. The problem of identifying first, and validating then, modules in networks has received substantial attention, and many approaches have been proposed. An important goal is functional validation of the identified modules, based on existing database resources. The quality and performance of algorithms can be assessed by evaluating the matching rate between retrieved and well annotated modules, in addition to newly established associations. Due to the variety of algorithms, the concept of module resolution spectrum has become central to this specific research field. In general, coarse-resolution modules reflect global network regulation patterns operating at the gene level or at the protein pathway scale. Fine-resolution modules localize dense regions, uncovering details of the variety of the constitutive connectivity patterns. The resolution limit problem is affected by uncertainty factors such as experimental accuracy and detection power of inference methods, and impacts the quality and accuracy of functional annotation. Our proposed approach works at the systems level; it aims to dissect networks and look at modularity in breadth-first search followed by in-depth analysis. In particular, “slicing” the protein interactome under exam yields a sort of tomography scan implemented by eigendecomposition of network affinity matrices. Such affinity matrices can be designed ad hoc, characterized by topological attributes, and analyzed with spectral methods. Consequently, a selected interactome data set allows the exploration of disease protein maps modularity through selected eigenmodes that are informative of both direct (protein-centric) and indirect (protein-neighbor centric) connectivity patterns of cancer targets and associated morbidities. The network tomography approach is thus recommended to infer about disease-induced multiscale modularity.