Using Nonlinear Stochastic Evolutionary Game Strategy to Model an Evolutionary Biological Network of Organ Carcinogenesis under a Natural Selection Scheme

Abstract

Molecular biologists have long recognized carcinogenesis as an evolutionary process that involves natural selection. Cancer is driven by the somatic evolution of cell lineages. In this study, the evolution of somatic cancer cell lineages during carcinogenesis was modeled as an equilibrium point (ie, phenotype of attractor) shifting, the process of a nonlinear stochastic evolutionary biological network. This process is subject to intrinsic random fluctuations because of somatic genetic and epigenetic variations, as well as extrinsic disturbances because of carcinogens and stressors. In order to maintain the normal function (ie, phenotype) of an evolutionary biological network subjected to random intrinsic fluctuations and extrinsic disturbances, a network robustness scheme that incorporates natural selection needs to be developed. This can be accomplished by selecting certain genetic and epigenetic variations to modify the network structure to attenuate intrinsic fluctuations efficiently and to resist extrinsic disturbances in order to maintain the phenotype of the evolutionary biological network at an equilibrium point (attractor). However, during carcinogenesis, the remaining (or neutral) genetic and epigenetic variations accumulate, and the extrinsic disturbances become too large to maintain the normal phenotype at the desired equilibrium point for the nonlinear evolutionary biological network. Thus, the network is shifted to a cancer phenotype at a new equilibrium point that begins a new evolutionary process. In this study, the natural selection scheme of an evolutionary biological network of carcinogenesis was derived from a robust negative feedback scheme based on the nonlinear stochastic Nash game strategy. The evolvability and phenotypic robustness criteria of the evolutionary cancer network were also estimated by solving a Hamilton–Jacobi inequality – constrained optimization problem. The simulation revealed that the phenotypic shift of the lung cancer-associated cell network takes 54.5 years from a normal state to stage I cancer, 1.5 years from stage I to stage II cancer, and 2.5 years from stage II to stage III cancer, with a reasonable match for the statistical result of the average age of lung cancer. These results suggest that a robust negative feedback scheme, based on a stochastic evolutionary game strategy, plays a critical role in an evolutionary biological network of carcinogenesis under a natural selection scheme.

Investigating core genetic-and-epigenetic cell cycle networks for stemness and carcinogenic mechanisms, and cancer drug design using big database mining and genome-wide next-generation sequencing data

“…4 ) could trigger DNA repair and cell apoptosis signals to counteract the accumulated mutations and methylations. However, cervical cancer cells do not undergo apoptosis, 60 instead leading to chromosome instability and the accumulation of mutations and methylations that lead to subsequent phenotypic changes, 61,62 which finally induce carcinogenesis. Interestingly, we report that the expression of HMMR changes from G2 phase in ESCs to M phase in HeLa cells.…”

“…Therefore, to accumulate genetic mutations and abnormal epigenetic regulations, cervical cancer robustly enhances cell survival in the unsTable S and G2 phases by genetic mutations and methylations. 61 …”

“…This is also supported by the model simulation of cancer. 61 With alterations in histone modifications, methylated genes, such as STAMBPL1, TROAP, CDCA2, PIF1, CKAP2L , and NUF2 , in G2 phase could execute numerous functions to promote carcinogenesis. At the same time, miR-192 regulates metastasis in carcinogenesis and mitosis.…”

A genome wide dosage suppressor network reveals genomic robustness

“…These pathways of suppression appear to decouple physical interaction modules from the modules of functional activities, and the flexible interaction edges rearrange the functional modules to buffer the effects of genetic and environmental perturbations (4,27). …”

“…Genetic robustness leads to physiological robustness through optimization of fitness by competition within an environment (4). Genomic robustness is genetic robustness when applied to the whole set of genes of an organism.…”