Recent advances in omics profiling technologies yield ever larger amounts of molecular data. Yet, the elucidation of the molecular basis of human diseases remains an unsolved challenge. The analysis of multi-scale omics data requires integrative bioinformatic tools capable of multi-modal computing and multi-scale modeling. Unsupervised learning approaches are frequently employed to identify biomolecules and pathways involved in specific diseases. However, classical clustering is hardly suitable to analyse, e.g., gene expression data conjointly with experimental conditions and molecular pathway information. Since we are interested in gene sets displaying a consistent behaviour across different conditions, both genes and samples have to be clustered simultaneously employing models respecting the heterogeneity of such multi-scale data. To this end, we aim for extending bi-clustering approaches by including information encoded in biological networks. Methods BiCluE (Sun et al. 2013) has been the first software package tackling the weighted bi-cluster editing problem. It pro- vides an exact algorithm based on fixed-parameter tractability (FPT). The bi-cluster editing problem is formulated as a bi-partite graph connecting features and samples. We then transform this graph into a disjunct set of bi-cliques while minimizing the editing costs (e.g., number of edges to be added/removed). Even though BiCluE yields potent solutions in many scenarios such as novel genotype-phenotype associations in GWAS data, it does not consider intrinsic feature relationships, e.g., interactions between proteins or regulatory interactions between genes. Therefore, we propose an extension of the BiCluE algorithm by mapping molecular interaction networks onto the bi-partite graph such that we impose constraints that force bi-cliques to respect intrinsic feature relationships. This reduces the computational com- plexity from O(4k) to O(2k), with k being the cluster editing costs due to a drastic reduction of the search space. Ad- ditionally, this model straight-forwardly allows incorporation of multi-scale data depending on the integrated network. Results and conclusions We demonstrate the validity and efficiency of our extension to BiCluE on simulated data. In general, such network- constrained bi-clustering approaches do not only allow for more stable feature selection, they also lead to more coherent functional enrichment, improving interpretability with respect to systems biology and systems medicine while being straight-forwardly applicable to multi-scale omics data.

Biological interaction databases can be exploited by pathway-level enrichment methods for downstream analyses in biological and biomedical settings. Classical enrichment methods rely on predefined lists of pathways, biasing the search towards known pathways and risking to overlook unknown, yet important functional modules. To overcome this limitation, so-called de novo network enrichment approaches extract novel pathways from large, molecular interaction networks given molecular profiles of patients, e.g. gene expression, promoter methylation, etc. Network enrichment of molecular profiling data is challenging due to noise and incompleteness of both the data them- selves and the networks. KeyPathwayMiner (KPM) jointly considers multi-scale molecular profiles to extract subnet- works enriched for de-regulated genes, e.g. differentially expressed genes. KPM is available as a feature-rich, user-friend- ly Cytoscape app, standalone software, or web service for de novo network enrichment (http://www.keypathwayminer. compbio.sdu.dk/). Clinical cancer research often focuses on patient survival times. Thus, we developed a new strategy to identify sub- networks most significantly associated with differences in survival. Our approach is based on the Network of Muta- tions Associated with Survival (NoMAS) algorithm that extracts subnetworks enriched in mutations. NoMAS exploits colour-coding to identify candidate subnetworks that are then evaluated with a log-rank test. We adapted NoMAS for multi-scale omics data by introducing a k-means clustering step to split patients into two groups using the candidate subnetworks molecular profile. Next, we apply a log-rank test to assess the significance of the difference in survival times between the two groups. Our overall goal is to find subnetworks significantly associated with survival time, thus creating multi-scale models that connect molecular changes, e.g. on the level of gene expression, to changes in the time-scale of patient survival. The identified subnetworks can be expected to represent important disease mechanisms, making them interesting candidates for further investigation. We thus expect that extending KPM to survival data will make de novo network enrichment considerably more attractive as a systems medicine approach.

Macrophages derived from monocyte precursors undergo specific polarization processes being influenced by the local tissue environment: classically-activated (M1) macrophages, showing a pro-inflammatory activity affecting effector cells in Th1 cellular immune responses; and alternatively-activated (M2) macrophages, with anti-inflammatory functions, involved in immunosuppression and tissue repair. At least three distinctive subsets of M2 macrophages, i.e. M2a, M2b and M2c, are characterized in the literature based on their eliciting molecular signals. The triggering and polarization of macrophages is attained through numerous, interweaved signaling pathways. To depict the logical relations among the genes involved in macrophage polarization, we utilized a computational modeling methodology, viz. Boolean modeling of gene regulation. We combined experimental data/knowledge from the literature to build a logical gene regulation network model driving macrophage polarization to M1, M2a, M2b and M2c phenotypes. Exploiting the GINsim software we studied the network dynamics under different settings and perturbations to comprehend how they affect cell polarization. Simulations of the network model, enacting the most significant biological conditions, showed consistency with the experimentally observed behaviour of in vivo macrophages. The model could properly replicate the polarization toward the four main phenotypes as well as to numerous hybrid phenotypes, known to be experimentally associated to physiological and pathological conditions. We speculate that shifts among different phenotypes in our model mimic the hypothetical continuum of macrophage polarization, with M1 and M2 being the poles of a continuous succession of states. Our simulations also suggest that anti-inflammatory macrophages are more resilient to shift to the pro-inflammatory phenotype.

The change in interest from identifying individual molecules to several components in biological samples as well as how they interact is addressed by systems biology. Mathematical, statistical, and computational methods have emerged to deal with the biological complexity exposed in past years by the massive production of high-throughput data through "omics" technologies. This chapter discusses powerful mathematical networks and modeling to identify key components related to rheumatoid arthritis and how to predict the response of different individuals to infections. As the consequence of a better understanding of biological processes, this chapter also presents the creation of new specific devices to diagnosis, treat, and prevent diseases. These nonnatural systems, produced by the insertion of genetic devices into a cell or cell-free systems, or even by editing a genome, are part of the synthetic biology field. The use of synthetic molecules or systems is discussed as attempts to diagnosis and treat cancer, Lyme disease, Ebola, and human immunodeficiency virus, among others. The chapter describes a diversity of ways in which systems and synthetic biology may help understand and control diseases.

Infection by Leishmania protozoan parasites can cause a variety of disease outcomes in humans and other mammals, from single self-healing cutaneous lesions to a visceral dissemination of the parasite. The correlation between chronic lesions and ecto-nucleotidase enzymes activity on the surface of the parasite is addressed here using damage caused in epithelial cells by nitric oxide. In order to explore the role of purinergic metabolism in lesion formation and the outcome of the infection, we implemented a cellular automata/lattice gas model involving major immune characters (Th1 and Th2 cells, IFN-gamma, IL-4, IL-12, adenosine-Ado-, NO) and parasite players for the dynamic analysis of the disease progress. The model were analyzed using partial ranking correlation coefficient (PRCC) to indicate the components that most influence the disease progression. Results show that low Ado inhibition rate over Th-cells is shared by L. major and L. braziliensis, while in L. amazonensis infection the Ado inhibition rate over Th-cells reaches 30%. IL-4 inhibition rate over Th-cell priming to Th1 independent of IL-12 are exclusive of L. major. The lesion size and progression showed agreement with published biological data and the model was able to simulate cutaneous leishmaniasis outcomes. The sensitivity analysis suggested that Ado inhibition rate over Th-cells followed by Leishmania survival probability were the most important characteristics of the process, with PRCC of 0.89 and 0.77 respectively. The simulations also showed a non-linear relationship between Ado inhibition rate over Th-cells and lesion size measured as number of dead epithelial cells. In conclusion, this model can be a useful tool for the quantitative understanding of the immune response in leishmaniasis.

Systems medicine holds many promises, but has so far provided only a limited number of proofs of principle. To address this road block, possible barriers and challenges of translating systems medicine into clinical practice need to be identified and addressed. The members of the European Cooperation in Science and Technology (COST) Action CA15120 Open Multiscale Systems Medicine (OpenMultiMed) wish to engage the scientific community of systems medicine and multiscale modelling, data science and computing, to provide their feedback in a structured manner. This will result in follow-up white papers and open access resources to accelerate the clinical translation of systems medicine.

Systems and synthetic biology for health

Immunosenescence is thought to result from cellular aging and to reflect exposure to environmental stressors and antigens, including cytomegalovirus (CMV). However, not all of the features of immunosenescence are consistent with this view, and this has led to the emergence of the sister theory of "inflammaging". The recently discovered diffuse tissue distribution of resident memory T cells (TRM) which don't recirculate, calls these theories into question. These cells account for most T cells residing in barrier epithelia which sit in and travel through the extracellular matrix (ECM). With almost all studies to date carried out on peripheral blood, the age-related changes of the ECM and their consequences for T cell mobility, which is crucial for the function of these cells, have been largely ignored. We propose an update of the theoretical framework of immunosenescence, based on a novel hypothesis: the increasing stiffness and cross-linking of the senescent ECM lead to a progressive immunodeficiency due to an age-related decrease in T cell mobility and eventually the death of these cells. A key element of this mechanism is the mechanical stress to which the cell cytoplasm and nucleus are subjected during passage through the ECM. This hypothesis is based on an "evo-devo" perspective bringing together some major characteristics of aging, to create a single interpretive framework for immunosenescence.

Background: Macrophages cover a major role in the immune system, being the most plastic cell yielding several key immune functions. Methods: Here we derived a minimalistic gene regulatory network model for the differentiation of macrophages into the two phenotypes M1 (pro-) and M2 (anti-inflammatory).

Cosa sono le reti? Come funzionano? Lo racconta Paolo Tieri, fisico e ricercatore del CNR che da anni si dedica alla biologia dei sistemi e all'immunologia. Ogni mattina, quando andiamo a scuola, all'università, al lavoro, usiamo la rete stradale o quella dei trasporti urbani. Arrivati a destinazione ci troviamo ad interagire e a collaborare con amici e colleghi della nostra rete amicale. Ogni tanto controlliamo su Facebook cosa fanno gli amici del nostro social network preferito. E poi facciamo la spesa e prenotiamo le vacanze su Internet, usando la "rete delle reti" tecnologica mondiale. Insomma le reti sono ovunque e noi ci siamo immersi.

Recurrent focal neuropathy with liability to pressure palsies is a relatively frequent autosomal-dominant demyelinating neuropathy linked to peripheral myelin protein 22 (PMP22) gene deletions. The combination of PMP22 gene mutations with other genetic variants is known to cause a more severe phenotype than expected. We present the case of a patient with severe orthostatic hypotension since 12 years of age, who inherited a PMP22 gene deletion from his father. Genetic double trouble was suspected because of selective sympathetic autonomic disturbances. Through exome-sequencing analysis, we identified two novel mutations in the dopamine beta hydroxylase gene. Moreover, with interactome analysis, we excluded a further influence on the origin of the disease by variants in other genes. This case increases the number of unique patients presenting with dopamine-?-hydroxylase deficiency and of cases with genetically proven double trouble. Finding the right, complete diagnosis is crucial to obtain adequate medical care and appropriate genetic counseling.

Stable, predictive biomarkers and interpretable disease signatures are seen as a signi cant step towards personalized medicine. In this per- spective, integration of multi-omic data com- ing from genomics, transcriptomics, glycomics, proteomics, metabolomics is a powerful strat- egy to reconstruct and analyse complex mul- ti-dimensional interactions, enabling deeper mechanistic and medical insight. At the same time, there is a rising concern that much of such different omic data -although often publicly and freely available- lie in data- bases and repositories underutilised or not used at all. Issues coming from lack of stand- ardisation and shared biological identities are also well-known. From these considerations, a novel, pressing request arises from the life sciences to design methodologies and approaches that allow for these data to be interpreted as a whole, i.e. as inter- twined molecular signatures containing genes, proteins, mRNAs and miRNAs, able to capture inter-layers connections and complexity. Papers discuss data integration approaches and methods of several types and extents, their application in understanding the pathogenesis of speci c diseases or in identifying candidate biomarkers to exploit the full bene t of multi-omic datasets and their intrinsic information content. Topics of interest include, but are not limited to: o Methods for the integration of layered data, including, but not limited to, genomics, transcrip- tomics, glycomics, proteomics, metabolomics; o Application of multi-omic data integration approaches for diagnostic biomarker discovery in any eld of the life sciences; o Innovative approaches for the analysis and the visualization of multi-omic datasets; o Methods and applications for systematic measurements from single/undivided samples (com- prising genomic, transcriptomic, proteomic, metabolomic measurements, among others); o Multi-scale approaches for integrated dynamic modelling and simulation; o Implementation of applications, computational resources and repositories devoted to data integration including, but not limited to, data warehousing, database federation, semantic integration, service-oriented and/or wiki integration; o Issues related to the de nition and implementation of standards, shared identities and seman- tics, with particular focus on the integration problem.

One of the greatest challenges in biomedicine is to get a unified view of observations made from the molecular up to the organism scale. Towards this goal, multiscale models have been highly instrumental in contexts such as the cardiovascular field, angiogenesis, neurosciences and tumour biology. More recently, such models are becoming an increasingly important resource to address immunological questions as well. Systematic mining of the literature in multiscale modelling led us to identify three main fields of immunological applications: host-virus interactions, inflammatory diseases and their treatment and development of multiscale simulation platforms for immunological research and for educational purposes. Here, we review the current developments in these directions, which illustrate that multiscale models can consistently integrate immunological data generated at several scales, and can be used to describe and optimize therapeutic treatments of complex immune diseases.

TRANSLATION OF THE ORIGINAL DOCUMENT "NETWORK LITERACY" AVAILABLE AT https://sites.google.com/a/binghamton.edu/netscied/teaching-learning/network-concepts Mentre il nostro mondo diventa sempre più connesso attraverso l'uso di reti, o network, che rendono le comunicazioni e la diffusione di informazioni pressoché istantanee, il livello di comprensione di come queste reti funzionino avrà un ruolo importante nel determinare quanto la società trarrà beneficio da questa connettività accresciuta. In breve, una società connessa richiede un'alfabetizzazione sul concetto di rete, ossia una conoscenza di base di cosa siano le reti, come possano essere utilizzate come strumento per la scoperta e per i processi decisionali, nonché sulle problematiche e i potenziali vantaggi resi accessibili a tutti coloro che vivono nel mondo interconnesso di oggi.

The conceptualization of immunological self is amongst the most important theories of modern biology, representing a sort of theoretical guideline for experimental immunologists, in order to understand how host constituents are ignored by the immune system (IS). A consistent advancement in this field has been represented by the danger/damage theory and its subsequent refinements, which at present represents the most comprehensive conceptualization of immunological self. Here, we present the new hypothesis of "liquid self," which integrates and extends the danger/damage theory. The main novelty of the liquid self hypothesis lies in the full integration of the immune response mechanisms into the host body's ecosystems, i.e., in adding the temporal, as well as the geographical/evolutionary and environmental, dimensions, which we suggested to call "immunological biography." Our hypothesis takes into account the important biological changes occurring with time (age) in the IS (including immunosenescence and inflammaging), as well as changes in the organismal context related to nutrition, lifestyle, and geography (populations). We argue that such temporal and geographical dimensions impinge upon, and continuously reshape, the antigenicity of physical entities (molecules, cells, bacteria, viruses), making them switching between "self" and "non-self" states in a dynamical, "liquid" fashion. Particular attention is devoted to oral tolerance and gut microbiota, as well as to a new potential source of unexpected self epitopes produced by proteasome splicing. Finally, our framework allows the set up of a variety of testable predictions, the most straightforward suggesting that the immune responses to defined molecules representing potentials antigens will be quantitatively and qualitatively quite different according to the immuno-biographical background of the host. © 2014 Grignolio, Mishto, Faria, Garagnani, Franceschi and Tieri.

The complex differentiation process of the CD4+ T helper lymphocytes shapes the form and the range of the immune response to different antigenic challenges. Few mathematical and computational models have addressed this key phenomenon. We here present a multiscale approach in which two different levels of description, i.e. a gene regulatory network model and an agent-based simulator for cell population dynamics, are integrated into a single immune system model. We illustrate how such model integration allows bridging a gap between gene level information and cell level population, and how the model is able to describe a coherent immunological behaviour when challenged with different stimuli.

Objective: To provide a frame to estimate the systemic impact (side/adverse events) of (novel) therapeutic targets by taking into consideration drugs potential on the numerous districts involved in rheumatoid arthritis (RA) from the inflammatory and immune response to the gut-intestinal (GI) microbiome. Methods: We curated the collection of molecules from high-throughput screens of diverse (multi-omic) biochemical origin, experimentally associated to RA. Starting from such collection we generated RA-related protein-protein interaction (PPI) networks (interactomes) based on experimental PPI data. Pharmacological treatment simulation, topological and functional analyses were further run to gain insight into the proteins most affected by therapy and by multi-omic modeling. Results: Simulation on the administration of MTX results in the activation of expected (apoptosis) and adverse (nitrogenous metabolism alteration) effects. Growth factor receptor-bound protein 2 (GRB2) and Interleukin-1 Receptor Associated Kinase-4 (IRAK4, already an RA target) emerge as relevant nodes. The former controls the activation of inflammatory, proliferative and degenerative pathways in host and pathogens. The latter controls immune alterations and blocks innate response to pathogens. Conclusions: This multi-omic map properly recollects in a single analytical picture known, yet complex, information like the adverse/side effects of MTX, and provides a reliable platform for in silico hypothesis testing or recommendation on novel therapies. These results can support the development of RA translational research in the design of validation experiments and clinical trials, as such we identify GRB2 as a robust potential new target for RA for its ability to control both synovial degeneracy and dysbiosis, and, conversely, warn on the usage of IRAK4-inhibitors recently promoted, as this involves potential adverse effects in the form of impaired innate response to pathogens.

BACKGROUND: issues and limitations related to accessibility, understandability and ease of use of signalling pathway databases may hamper or divert research workflow, leading, in the worst case, to the generation of confusing reference frameworks and misinterpretation of experimental results. In an attempt to retrieve signalling pathway data related to a specific set of test genes, we queried and analysed the results from six of the major curated signalling pathway databases: Reactome, PathwayCommons, KEGG, InnateDB, PID, and Wikipathways. FINDINGS: although we expected differences - often a desirable feature for the integration of each individual query, we observed variations of exceptional magnitude, with disproportionate quality and quantity of the results. Some of the more remarkable differences can be explained by the diverse conceptual designs and purposes of the databases, the types of data stored and the structure of the query, as well as by missing or erroneous descriptions of the search procedure. To go beyond the mere enumeration of these problems, we identified a number of operational features, in particular inner and cross coherence, which, once quantified, offer objective criteria to choose the best source of information. CONCLUSIONS: in silico biology heavily relies on the information stored in databases. To ensure that computational biology mirrors biological reality and offers focused hypotheses to be experimentally validated, coherence of data codification is crucial and yet highly underestimated. We make practical recommendations for the end-user to cope with the current state of the databases as well as for the maintainers of those databases to contribute to the goal of the full enactment of the open data paradigm.

Genetic association studies of age-related, chronic human diseases often suffer from a lack of power to detect modest effects. Here we propose an alternative approach of including healthy centenarians as a more homogeneous and extreme control group. As a proof of principle we focused on type 2 diabetes (T2D) and assessed /genotypic associations of 31 SNPs associated with T2D, diabetes complications and metabolic diseases and SNPs of genes relevant for telomere stability and age-related diseases. We hypothesized that the frequencies of risk variants are inversely correlated with decreasing health and longevity. We performed association analyses comparing diabetic patients and non-diabetic controls followed by association analyses with extreme phenotypic groups (T2D patients with complications and centenarians). Results drew attention to rs7903146 (TCF7L2 gene) that showed a constant increase in the frequencies of risk genotype (TT) from centenarians to diabetic patients who developed macro-complications and the strongest genotypic association was detected when diabetic patients were compared to centenarians (p_value = 9.066*10(-)(7)). We conclude that robust and biologically relevant associations can be obtained when extreme phenotypes, even with a small sample size, are compared.