Investigation about the mechanisms involved in the onset of type 2 diabetes in absence of familiarity is the focus of a research project which has led to the development of a computational model that recapitulates the aetiology of the disease. The model simulates the metabolic and immunological alterations related to type-2 diabetes associated to several clinical, physiological and behavioural characteristics of representative virtual patients. In this study, the results of 46170 simulations corresponding to the same number of virtual subjects, experiencing different lifestyle conditions, are analysed for the construction of a statis- tical model able to recapitulate the simulated dynamics. The resulting machine learning model adequately predicts the synthetic data and can therefore be used as a computationally- cheaper version of the detailed mathematical model, ready to be implemented on mobile devices to allow self assessment by informed and aware individuals.

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.