The impact of transcriptional and translational time delays on the cancer network regulated by MicroRNA‐17‐92

In this paper, we have formulated and analyzed a mathematical model with multiple time delays of the genetic regulatory network of the microRNA‐17‐92 (miR‐17‐92) cluster. We have focused on the effect of the associated transcriptional and translational time delays of both positive and negative feedback loops on the miR‐17‐92 regulatory network. To gain better insight, we have explored the role of individual time delay in controlling the transition of the G1/S cell cycle process. We pay particular attention to the concentration of E2F and Myc, which trigger the protein ON and OFF switches. To analyze the situation mathematically, we employ delay‐dependent stability analysis and bifurcation analysis using delay differential equations and a biological study of the time‐delayed random transitions between the ON and OFF states for protein synthesis. Based on our study, we can conclude that the translational time delay can halt the healthy transition of cells in the G1/S cell cycle phase as the small increase in can destroy bistability of the system. Simulations are carried out to numerically show the solution trajectories under the combined effect of delays. Finally, we summarize the simulation results and the impact of delays on the dual behavior of miR‐17‐92 as it can act as an oncogene or as a tumor suppressor gene.

Authors

Institutions

Publication Details

Journal
Mathematical Methods in the Applied Sciences
Published
2024-09-16
DOI
https://doi.org/10.1002/mma.10472
Primary Topic
MicroRNA in disease regulation
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The impact of transcriptional and translational time delays on the cancer network regulated by MicroRNA‐17‐92

Louise Olsen‐Kettle, Tonghua Zhang, Akshay Sharma
Mathematical Methods in the Applied Sciences
MicroRNA in disease regulation
article

The impact of transcriptional and translational time delays on the cancer network regulated by MicroRNA‐17‐92

Louise Olsen‐Kettle, Tonghua Zhang, Akshay Sharma
article en

Abstract

In this paper, we have formulated and analyzed a mathematical model with multiple time delays of the genetic regulatory network of the microRNA‐17‐92 (miR‐17‐92) cluster. We have focused on the effect of the associated transcriptional and translational time delays of both positive and negative feedback loops on the miR‐17‐92 regulatory network. To gain better insight, we have explored the role of individual time delay in controlling the transition of the G1/S cell cycle process. We pay particular attention to the concentration of E2F and Myc, which trigger the protein ON and OFF switches. To analyze the situation mathematically, we employ delay‐dependent stability analysis and bifurcation analysis using delay differential equations and a biological study of the time‐delayed random transitions between the ON and OFF states for protein synthesis. Based on our study, we can conclude that the translational time delay can halt the healthy transition of cells in the G1/S cell cycle phase as the small increase in can destroy bistability of the system. Simulations are carried out to numerically show the solution trajectories under the combined effect of delays. Finally, we summarize the simulation results and the impact of delays on the dual behavior of miR‐17‐92 as it can act as an oncogene or as a tumor suppressor gene.

Mathematical Methods in the Applied SciencesVol. 48(3)
Swinburne University of Technology (AU)
Openalex Percentile: Top 75%
MicroRNA in disease regulation
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.