Gene interaction networks and epigenetic control in health and disease transition

Abstract

The massive data generation derived from the study of complex diseases by high throughput techniques, such as DNA microarrays and new generation sequencing, has prompted the development of systematic approaches for the investigation of biological phenomena. These new approaches seek data integration, as well as the characterization, modeling and prediction of emergent properties in biological systems. Moreover, the analysis of transcriptional changes through gene coexpression networks (GCNs) has been utilized to capture the molecular mechanisms associated with complex diseases. Furthermore, the topological properties of GCNs have been associated with the functional organization of the genome. This approach has been used to identify different transition states associated with health and disease, where changes in the topological properties of GCNs, such as entropy, modularity, node centrality and network robustness are directly associated with the pathophysiological processes of specific diseases. On the other hand, epigenetic mechanisms - such as histone modifications and DNA methylation and non-coding RNAs - are the transforming forces behind GCNs alterations in response to environmental factors and in the health-disease transition. Therefore, the interplay between the genome, epigenetic mechanisms and environment represents the foundation for the study of dynamic changes that govern the development and progression of complex diseases.