Bioinformatics has for a long time been a discipline based on the comparison of gene and protein sequences, with the aim of discovering evolutionary relationships (and hence comparable function). There is now a growing interest in a more systems-based approach, where the focus is on how molecules and pathways interrelate and on the identification of the 'minimal building blocks', i.e. molecules that exchange a common substrate or participate in the formation of large supramolecular complexes. Systems biology elevates the study of biological systems from the single entity level (genes, proteins) to higher hierarchies, such as entire genomic regions, groups of co-expressed genes, functional modules, and networks of interactions. In this context, the availability of high-throughput gene expression data, coupled with bioinformatics tools for their analysis, represents a scientific breakthrough. A plethora of computational methods have been developed in the last several years to analyze microarray data, proving the effectiveness of expression signatures, in many fields. However, the massive accumulation of high-quality structural and functional annotations of genomes imposes the development of computational frameworks able, not only to analyze gene expression profiles per se, but to merge any genomic information. The integration of different types of genomic data (gene sequences, transcriptional levels, functional characteristics) is a fundamental step in the identification of networks of molecular interaction which will allow turning genomic researches into accurate biological hypotheses. Standard methodologies for the analysis of gene expression profiles, which aim at identifying molecular mechanisms from the statistical analysis of the sole microarray signal, are clearly limited. Bioinformatics and computational biology need to overcome this limitation and develop approaches for the integration of high-throughput transcriptional data with gene structural and functional characteristics and for the identification of regulatory networks. These computational methods should help deciphering how the structural elements of genomes impact the molecular mechanisms of functional utilization. Network analysis has emerged as a powerful approach to understand complex phenomena and organization in social, technological and biological systems. In particular, it is increasingly recognized the role played by the topology of cellular networks, the intricate web of interactions among genes, proteins and other molecules regulating cell activity, in unveiling the function and the evolution of living organisms. In this view, the regulation of gene expression at genome level may be studied by dissecting different relationships among genes, deducible by integrated analysis of different data like co-expression in different biological contests, genomic co-localization, co-regulation of transcription (by means of promoter analyses) and post-transcriptional co-regulation (principally by miRNA-mediated regulation). The basic idea underlining this approach is that co-expression is essential to sustain the normal function of cells and tissues. On the other hand, genes can be co-expressed because they are co-regulated. Genes may be co-regulated because they share similar promoters (e.g. enhancers acting locally on a limited chromosomal region), because they are co-localized (i.e. under the effect of local epigenetic control, due to specific chromatin modifications) or because their mRNA are target of overlapping miRNA regulators. Moreover, part of co-expressed genes may be functionally related and thus to be under the control of similar gene circuits. Thus, the integrated study of co-expression, co-regulation, co-localization and functional similarity should help understanding basic and general rules governing genomic expression and identifying interaction mechanisms, circuit characteristics and specific switches of expression in the considered biological model.
The goal of our research is establishing the methodological bases and developing the computational infrastructures to investigate, through a systems biology approach, the networks of molecular interactions characterizing complex biological systems. The methodological aim is to develop algorithms to study complex biological processes, considering structural organization of chromosomal regions, classes of genes homogeneous for expression characteristics, functional modules, and regulatory networks. The applicative aim regards the study of genetic and epigenetic mechanisms of regulation of genome expression in model systems, e.g. myeloid differentiation and skeletal muscle.