Mathematical Linear Programming to Model MicroRNAs-Mediated Gene Regulation Using Gurobi Optimizer
Por:
Yogesh Muley, Vijaykumar
Publicada:
1 ene 2021
Resumen:
Genes are transcribed into various RNA molecules, and a portion of them
called messenger RNA (mRNA) is then translated into proteins in the
process known as gene expression. Gene expression is a high-energy
demanding process, and aberrant expression changes often manifest into
pathophysiology. Therefore, gene expression is tightly regulated by
several factors at different levels. MicroRNAs (miRNAs) are one of the
powerful post-transcriptional regulators involved in key biological
processes and diseases. They inhibit the translation of their mRNA
targets or degrade them in a sequence-specific manner, and hence control
the rate of protein synthesis. In recent years, in response to
experimental limitations, several computational methods have been
proposed to predict miRNA target genes based on sequence complementarity
and structural features. However, these predictions yield a large number
of false positives. Integration of gene and miRNA expression data
drastically alleviates this problem. Here, I describe a mathematical
linear modeling approach to identify miRNA targets at the genome scale
using gene and miRNA expression data. Mathematical modeling is faster
and more scalable to genome-level compared to conventional statistical
modeling approaches.
Filiaciones:
Yogesh Muley, Vijaykumar:
Muley, VY (Corresponding Author), Univ Nacl Autonoma Mexico, Inst Neurobiol, Queretaro, Mexico
Univ Nacl Autonoma Mexico, Inst Neurobiol, Queretaro, Mexico
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