报告题目： Endogenous Network Appraoch to Cancer Gensis and Progression

报 告 人：敖平 教授（上海交通大学)

报告时间：2015年4月22日（周三） 上午10:00

报告地点：嘉定园区行政楼316会议室

报告简介：

Abstract:

In this seminar I will present our progress to understand quantitatively the cancer genesis and progression beyond the current genomic centric view. The key ingredient is the existence of an autonomous endogenous molecular–cellular network for both normal and abnormal functions. This endogenous network forms a nonlinear stochastic dynamical system, with many stable attractors in its functional landscape. Normal or abnormal robust states can be decided by this network in a manner similar to the neural network. In this context cancer is hypothesized as one of its robust intrinsic states.

Such consideration implies that a nonlinear stochastic mathematical cancer model is constructible based on available experimental data and its quantitative prediction is directly testable. Within such model the genesis and progression of cancer may be viewed as stochastic transitions between different attractors. Thus it further suggests that progressions are not arbitrary. Other important issues on cancer, such as genetic vs epigenetics, double-edge effect, dormancy, are discussed in the light of present hypothesis. A different set of strategies for cancer prevention, cure, and care, is therefore suggested. How to construct the quantitative mathematical model from experimental data and the initial experimental validation will be discussed.
Key references:

1) Cancer as Robust Intrinsic State of Endogenous Molecular-Cellular Network Shaped by Evolution. P. Ao, D. Galas, L. Hood, X.-M. Zhu, Medical Hypotheses (2008) 78: 678–684. 
http://dx.doi.org/10.1016/j.mehy.2007.03.043 
Our first systematical presentaiton on endogenous network theory. This paper has no math, only arithemics: a simple counting shows that a 20 node network may be able to generate 32 attractors.

2) Global view of bionetwork dynamics: adaptive landscape. P. Ao. Journal of Genetics and Genomics　(2009) 36: 63-73.
Augument for the importance and usefulness of adaptive landscape in biology, including cancer.

3) Towards Predictive Stochastic Dynamical Modeling of Cancer Genesis and Progression. P. Ao, D. Galas, L. Hood, L.Yin, X.M.Zhu.　Interdiscip Sci Comput Life Sci (2010) 2: 140–144.
Explain the standard ways to turn endogenous network into a set of stochastic different equations. Boolean dynamics is a special limit of "n" goes to infinite.

4) From Phage lambda to human cancer: endogenous molecular-cellular network hypothesis. Gaowei Wang, Xiaomei Zhu, Leroy Hood, Ping Ao. Quantitative Biology (2013) 1: 32-49.
Reasoning core network approach is necessary as well as useful, because a full network is not here yet.
5) Quantitative Implementation of Endogenous Molecular-Cellular Network Hypothesis in Hepatocellular carcinoma. G.W. Wang, X.M. Zhu, J.R. Gu, P. Ao. Interface Focus 4 (2014) 20130064.
http://rsfs.royalsocietypublishing.org/content/4/3/20130064.abstract
First published results on realization of and predicrtion from (core) endogenous network

6) Endogenous Molecular-Cellular Hierarchical Modeling of Prostate Carcinogenesis Uncovers Robust Structure. X.M. Zhu, R.S. Yuan, L. Hood, P Ao　 Progress in Biophysics and Molecular Biology, (2015) 117: 30-42.
http://www.sciencedirect.com/science/article/pii/S007961071500005X
Explore the consequence of endogenous network from a hierarchical perspective.

7) Endogenous Molecular Network Reveals Two Mechanisms of Heterogeneity within Gastric Cancer. S.T. Li, X.M. Zhu, B.Y. Liu, G.W. Wang, P. Ao. Oncotarget (accepted).
Explore one cancer heterogenuity from network dynamics view.