摘要翻译:
我们将重整化群理论应用于动力系统,并给出了基本生物模体的最简单例子。这包括将复杂网络解释为对简单网络的扰动。这是建立我们的原始框架来推断生物网络性质的第一步,也是检验其对实际复杂系统有效性的基础工作。
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英文标题:
《Renormalization Group Transformation for Hamiltonian Dynamical Systems
in Biological Networks》
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作者:
Masamichi Sato
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最新提交年份:
2016
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分类信息:
一级分类:Quantitative Biology 数量生物学
二级分类:Other Quantitative Biology 其他定量生物学
分类描述:Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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一级分类:Physics 物理学
二级分类:Disordered Systems and Neural Networks 无序系统与神经网络
分类描述:Glasses and spin glasses; properties of random, aperiodic and quasiperiodic systems; transport in disordered media; localization; phenomena mediated by defects and disorder; neural networks
眼镜和旋转眼镜;随机、非周期和准周期系统的性质;无序介质中的传输;本地化;由缺陷和无序介导的现象;神经网络
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一级分类:Mathematics 数学
二级分类:Dynamical Systems 动力系统
分类描述:Dynamics of differential equations and flows, mechanics, classical few-body problems, iterations, complex dynamics, delayed differential equations
微分方程和流动的动力学,力学,经典的少体问题,迭代,复杂动力学,延迟微分方程
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英文摘要:
We apply the renormalization group theory to the dynamical systems with the simplest example of basic biological motifs. This includes the interpretation of complex networks as the perturbation to simple network. This is the first step to build our original framework to infer the properties of biological networks, and the basis work to see its effectiveness to actual complex systems.
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PDF链接:
https://arxiv.org/pdf/1609.02981