Multiscale fragPIN Modularity

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

Constrained Network Modularity

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

Assessment of brain cancer atlas maps with multimodal imaging features.

BACKGROUND: Glioblastoma Multiforme (GBM) is a fast-growing and highly aggressive brain tumor that invades the nearby brain tissue and presents secondary nodular lesions across the whole brain but generally does not spread to distant organs. Without treatment, GBM can result in death in about 6 months. The challenges are known to depend on multiple factors: brain localization, resistance to conventional therapy, disrupted tumor blood supply inhibiting effective drug delivery, complications from peritumoral edema, intracranial hypertension, seizures, and neurotoxicity. MAIN TEXT: Imaging techniques are routinely used to obtain accurate detections of lesions that localize brain tumors. Especially magnetic resonance imaging (MRI) delivers multimodal images both before and after the administration of contrast, which results in displaying enhancement and describing physiological features as hemodynamic processes. This review considers one possible extension of the use of radiomics in GBM studies, one that recalibrates the analysis of targeted segmentations to the whole organ scale. After identifying critical areas of research, the focus is on illustrating the potential utility of an integrated approach with multimodal imaging, radiomic data processing and brain atlases as the main components. The templates associated with the outcome of straightforward analyses represent promising inference tools able to spatio-temporally inform on the GBM evolution while being generalizable also to other cancers. CONCLUSIONS: The focus on novel inference strategies applicable to complex cancer systems and based on building radiomic models from multimodal imaging data can be well supported by machine learning and other computational tools potentially able to translate suitably processed information into more accurate patient stratifications and evaluations of treatment efficacy

Complexity of the marine ecosystem in view of the human health factors: role of network science

Anthropogenic and natural factors impacting health and well-being in coastal waters, regional seas, and the global ocean have long been recognized by the marine scientists, however not as much by the medical and public health community. Although establishing causal effects that directly or indirectly affect human health-related conditions is problematic and depends on the complex marine ecosystem, significant influences are present at both local and global levels, i.e., specific to coastal areas but also associated with sea activities referred to the ‘ocean health’ status. This offers a good rationale for an assessment of the human-marine environment interaction, evolution and complexity landscape. The health ecosystem as a whole (humans and environment, especially marine in our interests) is a complex bio-entity whose dynamics are largely unknown due to the presence of biodiversity and heterogeneity. In parallel, this complexity translates into various new processes that the stakeholders face to establish possible interventions and preserve the sustainability. A major checkpoint in our discussion refers to how to leverage the consolidated and indeed pervasive role of digital information across multiple fields and disciplines, supported by developments in artificial intelligence, machine learning and network science. This is an urgency, as the scientific marine community and the public health policy makers are struggling to gather big data from multiple sources and/or devices that help reveal the marine environmental status. Improvements in the ability of analyzing efficiently and effectively data are needed, and we suggest to profitably look at knowledge transfer strategies. In particular, considering and valuing how the scientific biomedical community has made use of network inference approaches to better understand complex biosystems in both structural and functional terms, we believe that the existing knowledge base can be further generalized to deal with the marine environmental ecosystem context

Pathway landscapes and epigenetic regulation in breast cancer and melanoma cell lines

Background Epigenetic variation is a main regulation mechanism of gene expression in various cancer histotypes, and due to its reversibility, the potential impact in therapy can be very relevant. Methods Based on a selected pair, breast cancer (BC) and melanoma, we conducted inference analysis in parallel on a few cell lines (MCF-7 for BC and A375 for melanoma). Starting from differential expression after treatment with a demethylating agent, the 5-Aza-2\u27-deoxycytidine (DAC), we provided pathway enrichment analysis and gene regulatory maps with cross-linked microRNAs and transcription factors. Results Several oncogenic signaling pathways altered upon DAC treatment were detected with significant enrichment. We represented the association between these cancers by depicting the landscape of common and specific variation affecting them