Quantitative implementation of the endogenous molecular–cellular network hypothesis in hepatocellular carcinoma

Abstract

One contribution of 10 to a Theme Issue 'Computational cell biology: from the past to the future'. A quantitative hypothesis for cancer genesis and progression-the endogenous molecular-cellular network hypothesis, intended to include both genetic and epigenetic causes of cancer-has been proposed recently. Using this hypothesis, here we address the molecular basis for maintaining normal liver and hepatocellular carcinoma (HCC), and the potential strategy to cure or relieve HCC. First, we elaborate the basic assumptions of the hypothesis and establish a core working network of HCC according to the hypothesis. Second, we quantify the working network by a nonlinear dynamical system. We show that the working network reproduces the main known features of normal liver and HCC at both the modular and molecular levels. Lastly, the validated working network reveals that (i) specific positive feedback loops are responsible for the maintenance of normal liver and HCC; (ii) inhibiting proliferation and inflammation-related positive feedback loops and simultaneously inducing a liver-specific positive feedback loop is predicated as a potential strategy to cure or relieve HCC; and (iii) the genesis and regression of HCC are asymmetric. In light of the characteristic properties of the nonlinear dynamical system, we demonstrate that positive feedback loops must exist as a simple and general molecular basis for the maintenance of heritable phenotypes, such as normal liver and HCC, and regulating the positive feedback loops directly or indirectly provides potential strategies to cure or relieve HCC.

Endogenous network states predict gain or loss of functions for genetic mutations in hepatocellular carcinoma

“…When comparing with clinical observation and experimental data, we found that two robust stable states from the model reproduced the main known features of the normal hepatocyte and cancerous hepatocyte at the modular and molecular level (electronic supplementary material, tables S13 and S14, more detailed information in our previous work [31]). As the core endogenous network was constructed using knowledge obtained independently and the stable states of the complex nonlinear dynamical systems were not obvious before analysis, it is striking that a single network can reproduce key features of both normal and cancerous hepatocytes, among other predictions.…”

“…In this work, based on the core endogenous molecularcellular network for hepatocytes [18,19,31], we found that the normal hepatocyte and cancerous hepatocyte can be represented by two robust stable states of the core network. Relationship between genetic mutations and biological function can be analysed in this context.…”

Are stem cells at the heart of all cancers?

“…S4 represents stem cell and progenitor cell types, S1 cancer cells (APL), and S12 a normal cell type (neutrophil) 细胞(S4); 有非正常细胞类, 如早熟白血球细胞(S1 今受到越来越多的实验证据支持 [25] . 景观拓扑图还 能直观读出致病和治愈的路径通常不是同一的 [16] ,…”

Accurate Chemical Master Equation Solution Using Multi-Finite Buffers

“…The stochastic properties of the network can then be inferred through analysis of a large number of simulation trajectories. However, as the SSA follows high-probability reaction paths, it is therefore inefficient for sampling biologically critical rare events that often occur in stiff multi-scale reaction networks, in which slow and fast reactions are well-separated in time scale [28][29][30][31][32][33]. In addition, assessment of its convergence of simulation trajectories is also difficult.…”