Kittisak Taoma, John J Tyson, Teeraphan Laomettachit, Pavel Kraikivski
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引用次数: 0
摘要
萌发酵母的细胞周期受复杂的蛋白质调控网络控制,其失调可导致致命错误或异常细胞分裂周期。在这项研究中,我们用布尔框架为这一网络建模,进行随机模拟。我们的模型足够详细,能够解释 40 个突变酵母菌株(占我们模拟的实验特征菌株的 83%)的表型,还能模拟内再复制菌株(不进行有丝分裂而进行多轮 DNA 合成)和表现出 "Cdc14 内循环"(分裂相与无丝分裂相之间的周期性转换)的菌株。由于我们的模型成功地复制了所观察到的野生型酵母细胞和许多突变株的特性,它为细胞周期控制的更全面的随机布尔模型提供了一个合理的、经过验证的起点。这样的模型可以让我们更好地理解芽殖酵母的细胞周期异常,并最终理解哺乳动物细胞的细胞周期异常。
Stochastic Boolean model of normal and aberrant cell cycles in budding yeast.
The cell cycle of budding yeast is governed by an intricate protein regulatory network whose dysregulation can lead to lethal mistakes or aberrant cell division cycles. In this work, we model this network in a Boolean framework for stochastic simulations. Our model is sufficiently detailed to account for the phenotypes of 40 mutant yeast strains (83% of the experimentally characterized strains that we simulated) and also to simulate an endoreplicating strain (multiple rounds of DNA synthesis without mitosis) and a strain that exhibits 'Cdc14 endocycles' (periodic transitions between metaphase and anaphase). Because our model successfully replicates the observed properties of both wild-type yeast cells and many mutant strains, it provides a reasonable, validated starting point for more comprehensive stochastic-Boolean models of cell cycle controls. Such models may provide a better understanding of cell cycle anomalies in budding yeast and ultimately in mammalian cells.
期刊介绍:
npj Systems Biology and Applications is an online Open Access journal dedicated to publishing the premier research that takes a systems-oriented approach. The journal aims to provide a forum for the presentation of articles that help define this nascent field, as well as those that apply the advances to wider fields. We encourage studies that integrate, or aid the integration of, data, analyses and insight from molecules to organisms and broader systems. Important areas of interest include not only fundamental biological systems and drug discovery, but also applications to health, medical practice and implementation, big data, biotechnology, food science, human behaviour, broader biological systems and industrial applications of systems biology.
We encourage all approaches, including network biology, application of control theory to biological systems, computational modelling and analysis, comprehensive and/or high-content measurements, theoretical, analytical and computational studies of system-level properties of biological systems and computational/software/data platforms enabling such studies.