PRINCIPLES OF MATHEMATICAL MODELING OF microRNA-MEDIATED SIGNALING NETWORKS IN HUMAN DISEASES
DOI:
https://doi.org/10.11603/mie.1996-1960.2020.3.11610Abstract
Background. Taking into account the growth of scientific knowledge and the discovery of new miRNAs and their gene targets, approaches to computational modeling of new functional associations between differentiated expression of miRNAs and diseases, as well as phenotypic patterns of miRNAs expression in gene regulatory networks. The goal of the conceptualization was the results of comparatively recent studies in systems biology, which use various kinetic modeling methods to help identify possibilities regarding the regulatory function and therapeutic potential of miRNAs in human diseases.
Materials and methods. Results. Some of the key computational aspects of mathematical modeling are also considered, which include: regulation of miRNAs-mediated network motifs in the regulation of gene expression; models of miRNAs biogenesis and miRNA target interactions; inclusion of such models in complex pathways of disease development, systemic understanding of their pathophysiological context. It is concluded that the efficiency and practicality of using small miRNA-associated network motifs, simplified to several components, to study the prognostic characteristics of the simulated network dynamics in diseases and physiological conditions. It is emphasized that most experimental studies focus on direct interactions of target miRNAs. Thus, the role of miRNAs in systems is revealed and, therefore, a systematic understanding of gene-mediated miRNA gene repression is provided.
Conclusions. However, in addition to the usual interactions of target miRNAs, recent experiments have shown that primary miRNAs or miRNA precursors formed during miRNA biogenesis can also compete with mature miRNAs for binding sites on target miRNAs. It is also important to move from the temporal dynamics of gene regulation by miRNAs, to the analysis and modeling of miRNA spatial information in cells as different subcellular locations.
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