Cellular Interactions Networking in Interactive Models of Diseases

Abstract

Integromics is necessitated as the complex diseases require to collate the integrated analysis expression, variation, and regulation of genes involved in trigger, prognosis, and establishment of factors creating the complete disease paradigm. The further involvement of the non-genetic and environmental exogenous factors are also designated to formulate the multitude of data for furthering the “integromic” approaches. Identification and validation of interaction networks and network biomarkers have become more critical and important in the development of disease models, which are functionally changed during disease development, progression, or treatment. We represent the requirement of the multi-node analyses that goes beyond the binary relationships to enterprise the structured interactions at the interface of genotype to phenotype correlations in disease biology. The prevalence and sporadic occurrence of endemic and pandemic infectious diseases, as well as the unmanageable burden of the non-communicable diseases, have emerged as the most burgeoning task of scientific investigations. Disease-specific interaction networks, network biomarkers, or Dynamic network biomarkers have great significance in the understanding of molecular pathogenesis, risk assessment, disease classification and monitoring, or evaluations of therapeutic responses and toxicities. The systems level studies have indicated biomolecule to cellular organization requires communication and cross-talk possibilities at the organism levels. Designing newer theranostic regimes, thus is required to focus on disease heterogeneity integrating the knowledge base of dynamic physical or functional interactions of network of networks. The chapter is targeted toward the identification, characterization, and a follow through of the experimental and computational tools for evincing the futuristic plan for modular endeavors in disease biology.KeywordsDisease networkInterectomicsNetwork Biomarkers