TY - JOUR
T1 - Dissect the Dynamic Molecular Circuits of Cell Cycle Control through Network Evolution Model
AU - Peng, Yang
AU - Scott, Paul
AU - Tao, Ruikang
AU - Wang, Hua
AU - Wu, Yan
AU - Peng, Guang
N1 - Publisher Copyright:
© 2017 Yang Peng et al.
PY - 2017
Y1 - 2017
N2 - The molecular circuits of cell cycle control serve as a key hub to integrate from endogenous and environmental signals into a robust biological decision driving cell growth and division. Dysfunctional cell cycle control is highlighted in a wide spectrum of human cancers. More importantly the mainstay anticancer treatment such as radiation therapy and chemotherapy targets the hallmark of uncontrolled cell proliferation in cancer cells by causing DNA damage, cell cycle arrest, and cell death. Given the functional importance of cell cycle control, the regulatory mechanisms that drive the cell division have been extensively investigated in a huge number of studies by conventional single-gene approaches. However the complexity of cell cycle control renders a significant barrier to understand its function at a network level. In this study, we used mathematical modeling through modern graph theory and differential equation systems. We believe our network evolution model can help us understand the dynamic cell cycle control in tumor evolution and optimizing dosing schedules for radiation therapy and chemotherapy targeting cell cycle.
AB - The molecular circuits of cell cycle control serve as a key hub to integrate from endogenous and environmental signals into a robust biological decision driving cell growth and division. Dysfunctional cell cycle control is highlighted in a wide spectrum of human cancers. More importantly the mainstay anticancer treatment such as radiation therapy and chemotherapy targets the hallmark of uncontrolled cell proliferation in cancer cells by causing DNA damage, cell cycle arrest, and cell death. Given the functional importance of cell cycle control, the regulatory mechanisms that drive the cell division have been extensively investigated in a huge number of studies by conventional single-gene approaches. However the complexity of cell cycle control renders a significant barrier to understand its function at a network level. In this study, we used mathematical modeling through modern graph theory and differential equation systems. We believe our network evolution model can help us understand the dynamic cell cycle control in tumor evolution and optimizing dosing schedules for radiation therapy and chemotherapy targeting cell cycle.
UR - http://www.scopus.com/inward/record.url?scp=85018657318&partnerID=8YFLogxK
U2 - 10.1155/2017/2954351
DO - 10.1155/2017/2954351
M3 - Article
C2 - 28466007
AN - SCOPUS:85018657318
SN - 2314-6133
VL - 2017
JO - BioMed Research International
JF - BioMed Research International
M1 - 2954351
ER -