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Quant Biol    2013, Vol. 1 Issue (1) : 32-49    https://doi.org/10.1007/s40484-013-0007-1
REVIEW
From Phage lambda to human cancer: endogenous molecular-cellular network hypothesis
Gaowei Wang1,2, Xiaomei Zhu3, Leroy Hood4, Ping Ao1,2,5()
1. Ministry of Education Key Laboratory of Systems Biomedicine, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China; 2. State Key Laboratory for Oncogenes and Related Genes, Shanghai Cancer Institute, Shanghai Jiao Tong University School of Medicine, Shanghai 200032, China; 3. GenMath, Corp., Seattle, WA 98105, USA; 4. Institute for Systems Biology, Seattle, WA 98103, USA; 5. Department of Physics, Shanghai Jiao Tong University, Shanghai 200240, China
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Abstract

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.
Corresponding Author(s): Ao Ping,Email:aoping@sjtu.edu.cn   
Issue Date: 05 March 2013
 Cite this article:   
Gaowei Wang,Xiaomei Zhu,Leroy Hood, et al. From Phage lambda to human cancer: endogenous molecular-cellular network hypothesis[J]. Quant Biol, 2013, 1(1): 32-49.
 URL:  
https://academic.hep.com.cn/qb/EN/10.1007/s40484-013-0007-1
https://academic.hep.com.cn/qb/EN/Y2013/V1/I1/32
Fig.1  Life cycle, whole genome, regulatory network of Phage lambda.
(A) After infection, there are two possible fates of the Phage lambda, one is lysis, Phage lambda replicating and releasing the . The other is lysogeny, the phage DNA integrated into the host cell chromosome. Phage lambda can switch from lysogeny to lysis under the influence of temperature, nutrients, and mutated genes.
(B) Whole genome of Phage lambda containing a total of about 40 genes, signaling transduction pathways and gene regulatory network of Phage lambda.
(C) configrations of right operator O at lysogeny state and lysis state.
(D) Endogenous molecular-cellular network of Phage lambda, the core network.
Fig.1  Life cycle, whole genome, regulatory network of Phage lambda.
(A) After infection, there are two possible fates of the Phage lambda, one is lysis, Phage lambda replicating and releasing the . The other is lysogeny, the phage DNA integrated into the host cell chromosome. Phage lambda can switch from lysogeny to lysis under the influence of temperature, nutrients, and mutated genes.
(B) Whole genome of Phage lambda containing a total of about 40 genes, signaling transduction pathways and gene regulatory network of Phage lambda.
(C) configrations of right operator O at lysogeny state and lysis state.
(D) Endogenous molecular-cellular network of Phage lambda, the core network.
StatePRMOR1OR2OR3PRi(s)j(s)k(s)
1000
2R2100
3R2100
4R2100
5R2R2200
6R2R2200
7R2R2200
8R2R2R2300
9C2010
10C2010
11C2010
12C2C2020
13C2C2020
14C2C2020
15C2C2C2030
16C2R2110
17C2R2110
18C2C2R2120
19C2R2110
20R2C2110
21C2R2C2120
22R2C2110
23R2C2110
24R2C2C2120
25C2R2R2210
26R2R2C2210
27R2C2R2210
28RNAp001
29R2RNAp101
30C2RNAp011
31RNApR2101
32RNApR2R2201
33RNApR2C2111
34RNAp001
35RNApR2101
36RNApC2011
37RNApC2011
38RNApC2R2111
39RNApC2C2021
40RNApRNAp002
Tab.1  The 40 configurations corresponding to right operator.
Fig.2  Illustration of adaptive landscape of Phage lambda genetic switch.
(A) The dynamic state of the network is represented by a particle whose position is given by instantaneous protein numbers. The potential function maps a landscape in the protein number space. For Phage lambda genetic switch, there are two potential minima corresponding to two epigenetic states. The area around each of the minima forms the attractive basin. The state of the network always tends to relax to one of the minima. The fluctuation may bring the network from one minimum to another.
(B) Before switching the Phage grows in lysogenic state. The potential barrier separating the lysogenic state and the lytic state is high. When is activated, this barrier is lowered. The lifetime of lysogenic state reduces drastically and the Phage switches to lytic state.
(C) When stochastic effect is included, the switching happens when the lysogenic potential minimum become too shallow to confine fluctuation.
Fig.2  Illustration of adaptive landscape of Phage lambda genetic switch.
(A) The dynamic state of the network is represented by a particle whose position is given by instantaneous protein numbers. The potential function maps a landscape in the protein number space. For Phage lambda genetic switch, there are two potential minima corresponding to two epigenetic states. The area around each of the minima forms the attractive basin. The state of the network always tends to relax to one of the minima. The fluctuation may bring the network from one minimum to another.
(B) Before switching the Phage grows in lysogenic state. The potential barrier separating the lysogenic state and the lytic state is high. When is activated, this barrier is lowered. The lifetime of lysogenic state reduces drastically and the Phage switches to lytic state.
(C) When stochastic effect is included, the switching happens when the lysogenic potential minimum become too shallow to confine fluctuation.
Phage genotypeRelative CI level in lysogenRelative Cro level in lysisSwitching frequency to lytic state recA- per minuteSwitching frequency to lytic state recA+ per minute
Theoretical(Experimental)TheoreticalTheoretical(Experimental)Theoretical
λ+λOR121λOR323λOR3′23′100% (100%)20% (25%–30%)70% (60%–75%)50% (50%–60%)100%100%70%130%1×10–93×10–97×10–91×10–91×10–53×10–51×10–52×10–5
Tab.2  Comparison between the calculation and the experiment data (adapted from Ref. []).
Fig.3  Minimum set of modules of HCC and typical situations of the adaptive landscape.
(A) The minimum modules include cell proliferation, cell death, inflammation, metabolism, cell adhesion, angiogenesis, liver specific functions, in molecular-cellular level, these modules and cross-talks between modules may be simplified and specified by regulatory proteins, modules cross talk to each other to form the endogenous molecular-cellular network.
(B) Endogenous molecular-cellular network of HCC (incomplete).
(C) Three typical situations of the functional landscape, the vertical scale illustrates the relative stability of robust states, healthy, tumor and others, in the multiple dimensional state space. (a) The healthy state is a globally stable under normal conditions; (b) due to genetic and epidemiologic influence on the endogenous network, tumor or cancer states may become more stable than healthy state. Such metastable healthy state may still have a long life time for the whole organism being viable; (c) a very ‘damaged’ endogenous network may not be able to produce a locally stable healthy state.
Fig.3  Minimum set of modules of HCC and typical situations of the adaptive landscape.
(A) The minimum modules include cell proliferation, cell death, inflammation, metabolism, cell adhesion, angiogenesis, liver specific functions, in molecular-cellular level, these modules and cross-talks between modules may be simplified and specified by regulatory proteins, modules cross talk to each other to form the endogenous molecular-cellular network.
(B) Endogenous molecular-cellular network of HCC (incomplete).
(C) Three typical situations of the functional landscape, the vertical scale illustrates the relative stability of robust states, healthy, tumor and others, in the multiple dimensional state space. (a) The healthy state is a globally stable under normal conditions; (b) due to genetic and epidemiologic influence on the endogenous network, tumor or cancer states may become more stable than healthy state. Such metastable healthy state may still have a long life time for the whole organism being viable; (c) a very ‘damaged’ endogenous network may not be able to produce a locally stable healthy state.
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