#microarray studies

The living cells are composed of bio-membranes which construct lipid bilayers composed mainly of phospholipids with proteins and cholesterol embedded in them. The internal organelles of the cell are composed of intracellular membranes and their unique structure modulates the permeation of molecules, like water, ions, and oxygen. Bio-membranes are considered as complex systems, and their state of matter is the liquid crystalline state corresponds to the fluid mosaic model of Singer & Nicolson [1]. Such state of matter undergoes a huge number of metastable phases that are named as ‘lipid rafts’ that are considered to act as information hubs.

These ‘lipid rafts’ are thermodynamic driven bioinformation hubs essential for the cell functions and for the survival of the organism [2]. The convergence of various scientific disciplines, including bioinformatics, cheminformatics, medical informatics, and nanoinformatics, has given rise to novel approaches in understanding and harnessing the potential of artificial cell membranes as bioinformation hubs. This paper delves into the intricate interplay between bio-membranes, lipid rafts, and thermodynamic-driven bioinformation, elucidating their pivotal role in establishing therapeutic networks.

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Genes are considered as segments of DNA that provide instructions for the production of specific proteins or RNA molecules. Genes can be related to one another in a variety of ways. These relationships can play important roles in the regulation of cellular processes and functions, as well as in the development and evolution of organisms. Gene relation refers to the relationship between two or more genes in terms of their function, location, or sequence similarity. In studying the expression and relationship of genes, researchers can get a sense of how gene-information is used to know cell functions and how changes in gene expression or regulation can lead to disease. As an example, co-expression analysis can help to determine gene-groups that are functionally related and may be involved in specific biological processes or diseases. Also, network analysis can be used to identify regulatory relationships between genes as well as to build gene regulation networks that control specific cellular processes. See also Allocco et al. [1], Elise et al. [2], Ruan et al. [3] & Song et al. [4], By interacting with each other, genes form a complicated network. The relationship between groups of genes with different functions can be represented as gene networks. Since the accessibility of the large gene expression data, several methods have been developed to analyze the gene-to-gene relationship. Copula approach is being used to study the relationship between the expression profiles of pairs of genes, since a copula is a multivariate model that can be used for explaining gene-gene dependence.

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