Estimating network changes from lifespan measurements using a parsimonious gene network model of cellular aging
Estimating network changes from lifespan measurements using a parsimonious gene network model of cellular aging
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DOI：
10.1186/s12859-019-3177-7
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发表时间：
2019-11-20
影响因子：
3
通讯作者：
Qin, Hong
中科院分区：
文献类型：
--
作者：
Qin, Hong
背景：细胞老化最好的研究是在芽殖酵母酿酒酵母。作为多效性的一个例子，酵母的寿命受到数百个相互关联的基因的影响。然而，目前还没有定量的方法来推断系统水平的变化，基因网络在cellular ageing.Results：我们提出了一个简约的数学模型的基础上随机基因相互作用网络的细胞衰老。这个网络模型仅由非老化成分组成：基因相互作用的强度随着恒定的死亡率而下降。在模型中，当一个必要节点失去与其他节点的所有相互作用时，细胞就会死亡，相当于删除了一个必要基因。基因相互作用的随机性是用二项分布来模拟的。我们发现，在衰老的早期阶段，死亡率随时间的指数增长可以从这个基因网络模型中出现。我们开发了一种最大似然方法，从实验寿命中估计三个影响寿命的网络参数：t（0），网络系统的初始虚拟年龄; n，每个基本节点的平均寿命影响相互作用;和R，初始死亡率。我们将此模型应用于已知对复制寿命有影响的酵母突变体。我们发现，SIR 2，FOB 1，和HXK 2的删除显着改变了初始虚拟年龄，但不是每个重要节点的平均寿命影响的相互作用，这表明这些突变主要影响基因相互作用的可靠性，但不是基因networks.We的整体配置应用此模型来研究酵母天然分离株的复制寿命。我们估计，在这些分离株中，每个基本节点的寿命影响相互作用的平均数为7.0（6.1-8），平均估计的初始虚拟年龄为45.4（30.6-74）次细胞分裂。我们还发现，t（0）可以潜在地介导所观察到的Strehler-Mildvan的相关性在酵母天然isolates.Conclusions：我们的理论模型提供了一个简约的解释实验寿命数据从基因网络的角度。我们希望，我们的工作将激发更多的兴趣，开发网络模型来研究衰老作为一个多效性特征。
Background: Cellular aging is best studied in the budding yeast Saccharomyces cerevisiae. As an example of a pleiotropic trait, yeast lifespan is influenced by hundreds of interconnected genes. However, no quantitative methods are currently available to infer system-level changes in gene networks during cellular aging.Results: We propose a parsimonious mathematical model of cellular aging based on stochastic gene interaction networks. This network model is made of only non-aging components: the strength of gene interactions declines with a constant mortality rate. Death of a cell occurs in the model when an essential node loses all of its interactions with other nodes, and is equivalent to the deletion of an essential gene. Stochasticity of gene interactions is modeled using a binomial distribution. We show that the exponential increase of mortality rate over time can emerge from this gene network model during the early stages of aging.We developed a maximal likelihood approach to estimate three lifespan-influencing network parameters from experimental lifespans: t(0), the initial virtual age of the network system; n, the average lifespan-influencing interactions per essential node; and R, the initial mortality rate. We applied this model to yeast mutants with known effects on replicative lifespans. We found that deletion of SIR2, FOB1, and HXK2 considerably altered the initial virtual age but not the average lifespan-influencing interactions per essential node, suggesting that these mutations mainly influence the reliability of gene interactions but not the overall configurations of gene networks.We applied this model to investigate replicative lifespans of yeast natural isolates. We estimated that the average number of lifespan-influencing interactions per essential node is 7.0 (6.1-8) and the average estimated initial virtual age is 45.4 (30.6-74) cell divisions in these isolates. We also found that t(0) could potentially mediate the observed Strehler-Mildvan correlation in yeast natural isolates.Conclusions: Our theoretical model provides a parsimonious interpretation of experimental lifespan data from the perspective of gene networks. We hope that our work will stimulate more interest in developing network models to study aging as a pleiotropic trait.