BACKGROUND: Type 2 diabetes mellitus (T2D) is a common age-related disease, and is a major health concern, particularly in developed countries where the population is aging, including Europe. The multi-scale immune system simulator for the onset of type 2 diabetes (MISSION-T2D) is a European Union-funded project that aims to develop and validate an integrated, multilevel, and patient-specific model, incorporating genetic, metabolic, and nutritional data for the simulation and prediction of metabolic and inflammatory processes in the onset and progression of T2D. The project will ultimately provide a tool for diagnosis and clinical decision making that can estimate the risk of developing T2D and predict its progression in response to possible therapies. Recent data showed that T2D and its complications, specifically in the heart, kidney, retina, and feet, should be considered a systemic disease that is sustained by a pervasive, metabolically-driven state of inflammation. Accordingly, there is an urgent need (1) to understand the complex mechanisms underpinning the onset of this disease, and (2) to identify early patient-specific diagnostic parameters and related inflammatory indicators. OBJECTIVE: We aim to accomplish this mission by setting up a multi-scale model to study the systemic interactions of the biological mechanisms involved in response to a variety of nutritional and metabolic stimuli and stressors. METHODS: Specifically, we will be studying the biological mechanisms of immunological/inflammatory processes, energy intake/expenditure ratio, and cell cycle rate. The overall architecture of the model will exploit an already established immune system simulator as well as several discrete and continuous mathematical methods for modeling of the processes critically involved in the onset and progression of T2D. We aim to validate the predictions of our models using actual biological and clinical data. RESULTS: This study was initiated in March 2013 and is expected to be completed by February 2016. CONCLUSIONS: MISSION-T2D aims to pave the way for translating validated multilevel immune-metabolic models into the clinical setting of T2D. This approach will eventually generate predictive biomarkers for this disease from the integration of clinical data with metabolic, nutritional, immune/inflammatory, genetic, and gut microbiota profiles. Eventually, it should prove possible to translate these into cost-effective and mobile-based diagnostic tools.

Inflammation is part of a complex physiological response to harmful stimuli and pathogenic stress. The five components of the Nuclear Factor ?B (NF-?B) family are prominent mediators of inflammation, acting as key transcriptional regulators of hundreds of genes. Several signaling pathways activated by diverse stimuli converge on NF-?B activation, resulting in a regulatory system characterized by high complexity. It is increasingly recognized that the number of components that impinges upon phenotypic outcomes of signal transduction pathways may be higher than those taken into consideration from canonical pathway representations. Scope of the present analysis is to provide a wider, systemic picture of the NF-?B signaling system. Data from different sources such as literature, functional enrichment web resources, protein-protein interaction and pathway databases have been gathered, curated, integrated and analyzed in order to reconstruct a single, comprehensive picture of the proteins that interact with, and participate to the NF-?B activation system. Such a reconstruction shows that the NF-?B interactome is substantially different in quantity and quality of components with respect to canonical representations. The analysis highlights that several neglected but topologically central proteins may play a role in the activation of NF-?B mediated responses. Moreover the interactome structure fits with the characteristics of a bow tie architecture. This interactome is intended as an open network resource available for further development, refinement and analysis.

Omics data and computational approaches are today providing a key to disentangle the complex architecture of living systems. The integration and analysis of data of different nature allows to extract meaningful representations of signaling pathways and protein interactions networks, helpful in achieving an increased understanding of such intricate biochemical processes. We here describe a general workflow and relative hurdles in integrating online Omics data and analyzing reconstructed representations by using the available computational platforms.

Background: In a systems biology perspective, protein-protein interactions (PPI) are encoded in machine-readable formats to avoid issues encountered in their retrieval for the reconstruction of comprehensive interaction maps and biological pathways. However, the information stored in electronic formats currently used doesn't allow a valid automatic reconstruction of biological pathways.Results: We propose a logical model of PPI that takes into account the "state" of proteins before and after the interaction. This information is necessary for proper reconstruction of the pathway.Conclusions: The adoption of the proposed model, which can be easily integrated into existing machine-readable formats used to store the PPI data, would facilitate the automatic or semi-automated reconstruction of biological pathways.Reviewers: This article was reviewed by Dr. Wen-Yu Chung (nominated by Kateryna Makova), Dr. Carl Herrmann (nominated by Dr. Purificación López-García) and Dr. Arcady Mushegian. © 2010 Termanini et al; licensee BioMed Central Ltd.

Recently, the network paradigm, an application of graph theory to biology, has proven to be a powerful approach to gaining insights into biological complexity, and has catalyzed the advancement of systems biology. In this perspective and focusing on the immune system, we propose here a more comprehensive view to go beyond the concept of network. We start from the concept of degeneracy, one of the most prominent characteristic of biological complexity, defined as the ability of structurally different elements to perform the same function, and we show that degeneracy is highly intertwined with another recently-proposed organizational principle, i.e. 'bow tie architecture'. The simultaneous consideration of concepts such as degeneracy, bow tie architecture and network results in a powerful new interpretative tool that takes into account the constructive role of noise (stochastic fluctuations) and is able to grasp the major characteristics of biological complexity, i.e. the capacity to turn an apparently chaotic and highly dynamic set of signals into functional information. © 2010 Tieri et al; licensee BioMed Central Ltd.

Human aging and longevity are complex and multi-factorial traits that result from a combination of environmental, genetic, epigenetic and stochastic factors, each contributing to the overall phenotype. The multi-factorial process of aging acts at different levels of complexity, from molecule to cell, from organ to organ systems and finally to organism, giving rise to the dynamic "aging mosaic". At present, an increasing amount of experimental data on genetics, genomics, proteomics and other -omics are available thanks to new highthroughput technologies but a comprehensive model for the study of human aging and longevity is still lacking. Systems biology represents a strategy to integrate and quantify the existing knowledge from different sources into predictive models, to be later tested and then implemented with new experimental data for validation and refinement in a recursive process. The ultimate goal is to compact the new acquired knowledge into a single picture, ideally able to characterize the phenotype at systemic/organism level. In this review we will briefly discuss the aging phenotype in a systems biology perspective, showing four specific examples at different levels of complexity, from a systemic process (inflammation) to a cascade-process pathways (coagulation) and from cellular organelle (proteasome) to single gene-network (PON-1), which could also represent targets for anti-aging strategies. © 2010 Bentham Science Publishers Ltd.

Background: The aging phenotype in humans is very heterogeneous and can be described as a complex mosaic resulting from the interaction of a variety of environmental, stochastic and genetic-epigenetic variables. Therefore, each old person must be considered as a singleton, and consequently the definition of 'aging phenotype' is very difficult. Objective: We discuss the phenotype of centenarians, the best example of successful aging, as well as other models exploited to study human aging and longevity, such as families enriched in long-living subjects, twins and cohorts of unrelated subjects. Methods: A critical review of literature available until March 2008. Conclusions: No single model can be considered the gold standard for the study of aging and longevity, instead the combination of results obtained from different models must be considered in order to better understand these complex phenomena. We propose that a systems biology concept such as that of 'bow-tie' architecture useful for managing information flow, could help in this demanding task. © 2008 Informa UK Ltd.

Cell death is as important as cell proliferation for cell turn-over, and susceptibility to cell death is affected by a number of parameters that change with time. A time-dependent derangement of such a crucial process, or even the simple cell loss mediated by cell death impinges upon aging and longevity. In this review we will discuss how cell death phenomena are modulated during aging and what is their possible role in the aging process. We will focus on apoptosis and autophagy, which affect mostly proliferating and post-mitotic cells, respectively, and on mitochondrial degradation in long living cells. Since the "decisional process" that leads the cell to death is very complex, we will also discuss the possibility to address this topic with a systems biology approach. © 2008 Bentham Science Publishers Ltd.

For host survival, the immune system (IS) is required to deliver high-level, specific and continuous performance, dealing with a very complex universe of stimuli and functions, as well as physical and resource constraints. From this perspective, the immune system needs an effective strategy to assure the requested operational functions, to survive and to evolve. The concept of degeneracy discussed in this chapter, is the ability of some immune receptors to bind many types of ligands and it would appear to be a fundamental characteristic for immune system functioning as well as a formidable weapon in the architecture of complex biological structures and systems. In this chapter, we will discuss how degeneracy acts as a strategy to optimize the necessary trade-off between the inescapable promiscuity of receptors and ligands, with the necessity to produce a specific response, and how the degeneracy principle acts to set up a memory of each immunological event, thus contributing to the fitness of the organism, and how degeneracy can be considered among the underlying causes for the evolution and robustness of the IS.

The search for longevity genes has greatly developed in recent years basing on the idea that a consistent part of longevity is determined by genetics. The ultimate goal of this research is to identify possible genetic determinants of human aging and longevity, but studies on humans are limited by a series of critical restrictions. For this reason, most of the studies in this field have been, and still are, performed on animal models, basing on the assumption that fundamental biological mechanisms are highly conserved throughout evolution and that, accordingly, extrapolation from model systems to humans is quite reasonable. Indeed, many comparative data obtained on single genes or gene families fit with this assumption. However, it is also clear that, despite such a basic conservative scenario, major changes also occurred in evolution, particularly regarding biological regulatory processes and integration between and among pathways. This consideration raises the fundamental question of the transferability of the results obtained from model systems to humans. In this review, we discuss the differences between animal models and men regarding the genetics of aging and longevity, and the possible reasons that can explain such discrepancies, with a particular emphasis on the phenomena of conservation and evolvability of biological systems. Finally we will suggest a possible strategy to identify putative longevity genes basing on their position inside conserved metabolic structures.

In this article we summarise present knowledge on the role of pro-inflammatory cytokines on chronic inflammation leading to organismal aging, a phenomenon we proposed to call "inflamm-aging". In particular, we review genetic data regarding polymorphisms of genes encoding for cytokines and proteins involved in natural immunity (such as Toll-like Receptors and Heat Shock Proteins) obtained from large population studies including young, old and very old people in good health status or affected by age-related diseases such as Alzheimer's Disease and Type II Diabetes. On the whole, despite some controversial results, the available data are in favour of the hypothesis that pro-inflammatory cytokines play an important role in aging and longevity. Further, we present a possible hypothesis to reconcile energetic dysfunction, including mitochondria, and inflamm-aging. New perspectives for future studies, including phylogenetic studies in animal models and in silico studies on mathematical and bioinformatic models inspired by the systems biology approach, are also proposed. © 2006 Bentham Science Publishers Ltd.

We propose a network model in which the communication between its elements (cells, neurons and lymphocytes) can be established in various ways. The system evolution is driven by a set of equations that encodes various degrees of competition between elements. Each element has an "internal plasticity threshold" that, by setting the number of inputs and outputs, determines different network global topologies.

Motivation: Immune cells coordinate their efforts for the correct and efficient functioning of the immune system (IS). Each cell type plays a distinct role and communicates with other cell types through mediators such as cytokines, chemokines and hormones, among others, that are crucial for the functioning of the IS and its fine tuning. Nevertheless, a quantitative analysis of the topological properties of an immunological network involving this complex interchange of mediators among immune cells is still lacking. Results: Here we present a method for quantifying the relevance of different mediators in the immune network, which exploits a definition of centrality based on the concept of efficient communication. The analysis, applied to the human IS, indicates that its mediators differ significantly in their network relevance. We found that cytokines involved in innate immunity and inflammation and some hormones rank highest in the network, revealing that the most prominent mediators of the IS are molecules involved in these ancestral types of defence mechanisms which are highly integrated with the adaptive immune response, and at the interplay among the nervous, the endocrine and the immune systems. © The Author 2004. Published by Oxford University Press. All rights reserved.

In this paper we examine the impact of graph theory and more particularly the scale-free topology on Immune Network models. In the case of a simple but not trivial model we analyze network performances as long term selectivity properties, its computational capabilities as memory capacity, and relation with Neural Networks. A more advanced Immune Network model is conceptualized and it is developed a scaffold for further mathematical investigation.