The physiological LVH state can typically be maintained for months or years without significant compromise of cardiac function. In contrast, pathological LVH occurring in response to chronic cardiac overload, imposed this research by diseases such as hypertension, is characterized by a progression to con tractile dysfunction and heart failure and an increased long term mortality. Other Inhibitors,Modulators,Libraries differences between phy siological and pathological LVH include the occurrence of significant fibrosis and capillary rarefaction in the latter condition. Due to the stark clinical contrast between physiological and pathological LV remodeling, it is of importance to delineate the precise molecular mechanisms that drive these divergent responses to stress.

Some progress has been made in elucidating mechan isms of physiological Inhibitors,Modulators,Libraries hypertrophy through a number of genomic analyses and several reports implicate activation of the phosphoinositide 3 kinase Akt pathway as an important component. More recent studies offer the possibility to examine gene expression patterns in this phenotype more consistently and broadly. However, restrictions still exist, primarily due to an innate heteroge neity of signaling cascades and limitations of conventional statistical methods to address higher order relationships between genes. Visualization and analysis of biological data as networks is a powerful explorative alternative with the capacity to accurately assess complex relationships and eliminate noise inherent to microarray experiments.

Although such methods have already been successful in defining miRNA signature in obesity and diabetes, Batimastat dis covering novel cancer associated genes, and predicting the involvement of genes in core biological processes, their use in cardiovascular biology has been limited. Recent availability of comprehensive mouse cardiac hypertrophy microarray datasets, deposited in resources such as ArrayExpress and Gene Inhibitors,Modulators,Libraries Expres sion Omnibus, makes it possible to investigate global molecular mechanisms of this phenotype. The inference of gene relevance networks by co expression analysis is based on the hypothesis that genes encoding proteins participating in the same pathway or biologi cal process may often be co regulated under a large number of experimental conditions. An important advantage of network analysis algorithms is their abil ity to exploit local structure between biologically related nodes, thus eliminating most of the inherent noise. Additionally, confidence in network inference through co expression analysis may be increased by an integrative Inhibitors,Modulators,Libraries approach that utilizes multiple datasets across a variety of experimental kinase inhibitor Tofacitinib conditions and micro array platforms.