Dynamical analysis of cellular ageing by modeling of gene regulatory network based attractor landscape

Ageing is a natural phenomenon that is inherently complex and remains a mystery. Conceptual model of cellular ageing landscape was proposed for computational studies of ageing. However, there is a lack of quantitative model of cellular ageing landscape. This study aims to investigate the mechanism of cellular ageing in a theoretical model using the framework of Waddington’s epigenetic landscape. We construct an ageing gene regulatory network (GRN) consisting of the core cell cycle regulatory genes (including p53). A model parameter (activation rate) is used as a measure of the accumulation of DNA damage. Using the bifurcation diagrams to estimate the parameter values that lead to multi-stability, we obtained a conceptual model for capturing three distinct stable steady states (or attractors) corresponding to homeostasis, cell cycle arrest, and senescence or apoptosis. In addition, we applied a Monte Carlo computational method to quantify the potential landscape, which displays: I) one homeostasis attractor for low accumulation of DNA damage; II) two attractors for cell cycle arrest and senescence (or apoptosis) in response to high accumulation of DNA damage. Using the Waddington’s epigenetic landscape framework, the process of ageing can be characterized by state transitions from landscape I to II. By in silico perturbations, we identified the potential landscape of a perturbed network (inactivation of p53), and thereby demonstrated the emergence of a cancer attractor. The simulated dynamics of the perturbed network displays a landscape with four basins of attraction: homeostasis, cell cycle arrest, senescence (or apoptosis) and cancer. Our analysis also showed that for the same perturbed network with low DNA damage, the landscape displays only the homeostasis attractor. The mechanistic model offers theoretical insights that can facilitate discovery of potential strategies for network medicine of ageing-related diseases such as cancer.

衰老是一种与生俱来复杂且至今仍未完全被揭开的自然现象。此前已有研究者针对衰老的计算研究提出了细胞衰老景观的概念模型，但目前仍缺乏定量的细胞衰老景观模型。本研究借助沃丁顿表观遗传景观（Waddington’s epigenetic landscape）框架，通过理论模型探究细胞衰老的调控机制。我们构建了一套由核心细胞周期调控基因（包含p53）组成的衰老基因调控网络（gene regulatory network, GRN），并将模型参数（激活速率）作为DNA损伤积累程度的量化指标。通过分叉图（bifurcation diagrams）估算可引发多稳态的参数取值范围，我们得到了一套可捕捉三种截然不同的稳定稳态（或称吸引子（attractor））的概念模型，这三种稳态分别对应内稳态（homeostasis）、细胞周期阻滞（cell cycle arrest）以及衰老（senescence）或凋亡（apoptosis）状态。此外，我们采用蒙特卡洛（Monte Carlo）计算方法对势能景观进行量化分析，结果显示：I) 当DNA损伤积累程度较低时，景观仅存在一个内稳态吸引子；II) 当DNA损伤积累程度较高时，景观则出现两种吸引子，分别对应细胞周期阻滞与衰老（或凋亡）状态。基于沃丁顿表观遗传景观框架，衰老过程可被表征为从景观I到景观II的状态转变过程。通过计算机模拟扰动（in silico perturbations）实验，我们分析了p53失活的扰动网络的势能景观，证实了癌症吸引子的出现；该扰动网络的模拟动力学结果显示，其势能景观包含四个吸引域：内稳态、细胞周期阻滞、衰老（或凋亡）以及癌症。本研究的分析还表明，对于该p53失活的扰动网络，当DNA损伤积累程度较低时，其势能景观仅存在内稳态吸引子。该机制模型为相关研究提供了理论视角，有望助力开发衰老相关疾病（如癌症）的网络医学潜在干预策略。