Towards predictive stochastic dynamical modeling of cancer genesis and progression.

Interdisciplinary Sciences, Computational Life Sciences
P AoX M Zhu

Abstract

Based on an innovative endogenous network hypothesis on cancer genesis and progression we have been working towards a quantitative cancer theory along the systems biology perspective. Here we give a brief report on our progress and illustrate that combing ideas from evolutionary and molecular biology, mathematics, engineering, and physics, such quantitative approach is feasible.

References

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Citations

Jun 7, 2011·Seminars in Cancer Biology·Sui Huang
Feb 26, 2016·Journal of the Royal Society, Interface·Gaowei WangPing Ao
Jul 31, 2014·IET Systems Biology·Jan K SchluesenerPing Ao
Feb 7, 2015·Progress in Biophysics and Molecular Biology·Xiaomei ZhuPing Ao
Sep 19, 2016·Interdisciplinary Sciences, Computational Life Sciences·Lingtao SuGuixia Liu
Jun 25, 2017·Science China. Life Sciences·Hang SuXiaomei Zhu
Feb 18, 2017·Reports on Progress in Physics·Ruoshi YuanPing Ao
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Oct 21, 2016·Multiscale Modeling & Simulation : a SIAM Interdisciplinary Journal·Youfang CaoJie Liang
Nov 19, 2019·Frontiers in Genetics·Christine J YeHenry H Heng
Nov 16, 2020·Seminars in Cancer Biology·Julie Heng, Henry H Heng

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