摘要翻译：
种群中的选择性控制是指控制种群中的一个成员而使其他成员相对不受影响的能力。选择性控制的概念是以异质细胞群体中的细胞死亡或凋亡为例发展起来的。异种细胞中的凋亡信号是由拓扑结构相同但连接强度不同的基因网络组成的。选择控制依赖于网络集合中信令的统计量，我们分析了叠加、非线性和反馈对这些统计量的影响。平行路径促进正态分布，而串联路径促进偏斜分布，在最极端的情况下，偏斜分布变成对数正态分布。我们还表明反馈和非线性可以产生双峰信号统计量，离散和非线性也可以产生双峰信号统计量。提出了两种优化选择控制的方法。第一种是穷举搜索方法，第二种是基于线性规划的方法。尽管控制信号网络中的单个基因产生的选择性很小，但控制少数基因通常产生更高水平的选择性。研究了易受选择控制的基因组合的统计量，并用于识别一般的控制策略。我们发现，在弱种群的情况下，选择性通过作用于最不敏感的节点来提高，而鲁棒种群的选择性控制通过扰动更敏感的节点来优化。异质细胞系的高通量实验可以以类似的方式设计，进一步有可能将选择性优化过程纳入闭环控制系统。
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英文标题：
《Selective control of the apoptosis signaling network in heterogeneous
  cell populations》
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作者：
Diego Calzolari, Giovanni Paternostro, Patrick L. Harrington Jr.,
  Carlo Piermarocchi, and Phillip M. Duxbury
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最新提交年份：
2007
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分类信息：

一级分类：Quantitative Biology        数量生物学
二级分类：Quantitative Methods        定量方法
分类描述：All experimental, numerical, statistical and mathematical contributions of value to biology
对生物学价值的所有实验、数值、统计和数学贡献
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一级分类：Physics        物理学
二级分类：Statistical Mechanics        统计力学
分类描述：Phase transitions, thermodynamics, field theory, non-equilibrium phenomena, renormalization group and scaling, integrable models, turbulence
相变，热力学，场论，非平衡现象，重整化群和标度，可积模型，湍流
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英文摘要：
  Selective control in a population is the ability to control a member of the population while leaving the other members relatively unaffected. The concept of selective control is developed using cell death or apoptosis in heterogeneous cell populations as an example. Apoptosis signaling in heterogeneous cells is described by an ensemble of gene networks with identical topology but different link strengths. Selective control depends on the statistics of signaling in the ensemble of networks and we analyse the effects of superposition, non-linearity and feedback on these statistics. Parallel pathways promote normal statistics while series pathways promote skew distributions which in the most extreme cases become log-normal. We also show that feedback and non-linearity can produce bimodal signaling statistics, as can discreteness and non-linearity. Two methods for optimizing selective control are presented. The first is an exhaustive search method and the second is a linear programming based approach. Though control of a single gene in the signaling network yields little selectivity, control of a few genes typically yields higher levels of selectivity. The statistics of gene combinations susceptible to selective control is studied and is used to identify general control strategies. We found that selectivity is promoted by acting on the least sensitive nodes in the case of weak populations, while selective control of robust populations is optimized through perturbations of more sensitive nodes. High throughput experiments with heterogeneous cell lines could be designed in an analogous manner, with the further possibility of incorporating the selectivity optimization process into a closed-loop control system.
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PDF链接：
https://arxiv.org/pdf/705.4634