An increasing body of data is progressively indicating that the comprehension of the epigenetic landscape, actively integrated with the genetic elements, is crucial to delineate the molecular basis of the inter-individual complexity of ageing process.

The study of complex hereditary diseases is a very challenging area of research. The expanding set of in silico approaches offers a flourishing ground for the acceleration of meaningful findings in this area by exploitation of rich and diverse sources of omic data. These approaches are cheap, flexible, extensible, often complementary and can continuously integrate new information and tests to improve the selection of genes responsible for hereditary diseases.

Background: Computational biology contributes to a variety of areas related to life sciences and, due to the growing impact of translational medicine - the scientific approach to medicine in tight relation with basic science, it is becoming an important player in clinical-related areas. In this study, we use computation methods in order to improve our understanding of the complex interactions that occur between molecules related to Rheumatoid Arthritis (RA).

Background: Advances in biotechnology offer a fast growing variety of high-throughput data for screening molecular activities of genomic, transcriptional, post-transcriptional and translational observations. However, to date, most computational and algorithmic efforts have been directed at mining data from each of these molecular levels (genomic, transcriptional, etc.) separately. In view of the rapid advances in technology (new generation sequencing, high-throughput proteomics) it is important to address the problem of analyzing these data as a whole, i.e.

Advances in experimental biology, coupled with advances in computational power, bring new challenges to the interdisciplinary field of computational biology. One such broad challenge lies in the reverse engineering of gene networks, and goes from determining the structure of static networks, to reconstructing the dynamics of interactions from time series data. Here, we focus our attention on the latter area, and in particular, on parameterizing a dynamic network of oriented interactions between genes.

Motivation: The reliable and reproducible identification of gene interaction networks represents one of the grand challenges of both modern molecular biology and computational sciences. Approaches based on careful collection of literature data and network topological analysis, applied to unicellular organisms, have proven to offer results applicable to medical therapies. However, when little a priori knowledge is available, other approaches, not relying so strongly on previous literature, must be used.

In recent years, the development of high throughput devices for the massive parallel analyses of genomic data has lead to the generation of large amount of new biological evidences and has triggered the proliferation of data mining algorithms for the extraction of meaningful information. Microarrays for gene expression analyses are part of this revolution and provide important insight in molecular biology often in the form of coherent sets of genes representing previously uncharacterized processes.

Mathematical models based on non-linear integral and integro-differential equations are gaining increasing attention in mathematical epidemiology due to their ability to incorporate the past infection dynamic into its current development. This property is particularly suitable to represent the evolution of diseases where the dependence of infectivity on the time since becoming infected plays a crucial role.