Quantitative Biology

, Volume 1, Issue 1, pp 32–49 | Cite as

From Phage lambda to human cancer: endogenous molecular-cellular network hypothesis

  • Gaowei Wang
  • Xiaomei Zhu
  • Leroy Hood
  • Ping AoEmail author


Experimental evidences and theoretical analyses have amply suggested that in cancer genesis and progression genetic information is very important but not the whole. Nevertheless, “cancer as a disease of the genome” is still currently the dominant doctrine. With such a background and based on the fundamental properties of biological systems, a new endogenous molecular-cellular network theory for cancer was recently proposed by us. Similar proposals were also made by others. The new theory attempts to incorporate both genetic and environmental effects into one single framework, with the possibility to give a quantitative and dynamical description. It is asserted that the complex regulatory machinery behind biological processes may be modeled by a nonlinear stochastic dynamical system similar to a noise perturbed Morse-Smale system. Both qualitative and quantitative descriptions may be obtained. The dynamical variables are specified by a set of endogenous molecular-cellular agents and the structure of the dynamical system by the interactions among those biological agents. Here we review this theory from a pedagogical angle which emphasizes the role of modularization, hierarchy and autonomous regulation. We discuss how the core set of assumptions is exemplified in detail in one of the simple, important and well studied model organisms, Phage lambda. With this concrete and quantitative example in hand, we show that the application of the hypothesized theory in human cancer, such as hepatocellular carcinoma (HCC), is plausible, and that it may provide a set of new insights on understanding cancer genesis and progression, and on strategies for cancer prevention, cure, and care.


Phage Lambda Intrinsic State Adaptive Landscape Regulatory Machinery Genetic Switch 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Higher Education Press and Springer-Verlag GmbH 2013

Authors and Affiliations

  • Gaowei Wang
    • 1
    • 2
  • Xiaomei Zhu
    • 3
  • Leroy Hood
    • 4
  • Ping Ao
    • 1
    • 2
    • 5
    Email author
  1. 1.Ministry of Education Key Laboratory of Systems Biomedicine, Shanghai Center for Systems BiomedicineShanghai Jiao Tong UniversityShanghaiChina
  2. 2.State Key Laboratory for Oncogenes and Related Genes, Shanghai Cancer InstituteShanghai Jiao Tong University School of MedicineShanghaiChina
  3. 3.GenMath, Corp.SeattleUSA
  4. 4.Institute for Systems BiologySeattleUSA
  5. 5.Department of PhysicsShanghai Jiao Tong UniversityShanghaiChina

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