Systems Biology (Methods in Molecular Biology)

network [3, 85], such manipulation can be handled by standard

procedures which pose no additional conceptual issues

[27, 49]. Beyond the genomic information, the nonlinear dynami-

cal biochemical interactions among the endogenous agents gener-

ate normal tissue attractors and cancer attractors. Due to the

nonlinear biochemical interactions beyond genomic information,

the present hypothesis holds that there is no simple one-to-one

relationship between genotype and phenotype in complex diseases

such as cancer. Instead, genotype is in general related to phenotypes

by a very complex network of biochemical reactions [86, 87]. The

genesis and progression of cancer is assumed as the transition from

the intrinsic normal state to the intrinsic cancer state. A suitable

quantity to describe such a process is the adaptive landscape [11].

Moreover, without any a priori knowledge of genetic mutation

propensity in HCC, our results show that such network-level ana-

lyses are indeed a powerful approach to enable the prediction and a

better understanding of genetic mutations in HCC. This illustrates

the usefulness of network-level analyses as a means to predict and

understand genetic mutations in cancers.

3.2 Working
Endogenous Network
and Typical
Bioinformatics
Network

The aim to establish a liver endogenous network is to reveal the

core regulatory mechanisms of HCC genesis and progression at the

systemic level. Many other high-throughput-based frameworks,

such as ENCODE project [88], also have been proposed with

similar aims. Theoretically, the regulatory mechanism can be

deduced from high-throughput data if we have enough data

[89]. Nevertheless, in reality the present genome-wide gene expres-

sion and protein interactions information are far from achieving

this goal [18, 89]. Currently, analysis of high throughput is based

more on statistics which can deduce the network topology and

correlation between these molecular-cellular agents [88]. In the

endogenous network construction, we solve this issue by making

full use of the well-documented gene regulatory network and

signaling transduction pathway which reflect our accumulated

knowledge in molecular biology of liver.

We have realized the hypothesis by establishing working endoge-

nous network for liver. Many other high-throughput-based frame-

works also have been proposed to grasp the regulatory mechanism of

biological systems, such as transcriptomics, ENCODE project, etc.

[88]. Those frameworks play an important role in accelerating our

understanding of biological systems. Nevertheless, it is unlikely that

we can deduce the endogenous molecular-cellular network or other

higher-level descriptions of a tissue solely from genome wide infor-

mation about gene expression and physical interactions between

proteins [18, 90, 91]. The quantitative analysis of high throughput

is based on statistics, less on biological mechanism, they are often

used to deduce the network topology or correlation between these

molecular-cellular agents [88]. Comparedwiththenetwork obtained

Endogenous Molecular-Cellular Network Cancer Theory 237