在电池中计算的热力学效率 生活范围

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摘要翻译：
生物有机体在生长、繁殖和进化时必须执行计算。此外，自从Landauer界被提出以来，人们已经知道所有的计算都有一些热力学代价--同样的计算可以用更大或更小的热力学代价来实现，这取决于它是如何实现的。因此，关于生命进化的一个重要问题是评估生物体进行计算的热力学效率。这个问题是有趣的，无论是从生命已经接近最大效率的计算的角度来看（大概是在自然选择的压力下），还是从我们可能希望工程生物计算机可能达到的效率的实际角度来看，特别是与当前的计算系统相比。在这里，我们表明翻译的计算效率，定义为每个氨基酸操作消耗的自由能，比最好的超级计算机高出几个数量级，只比兰道尔界差大约一个数量级。然而，这种效率很大程度上取决于所讨论的细胞的大小和结构。特别地，我们表明，氨基酸操作的{it有用}效率，定义为每个氨基酸聚合的体积能量，随着细菌大小的增加而降低，并收敛到核糖体的聚合成本。当我们经历了向单细胞和多细胞真核生物的重大进化转变时，细胞中最大细菌的成本并没有改变。然而，随着细菌细胞大小的增加，单位质量的总计算速度是非单调的，而且在不同的生物结构中也会发生变化，包括从单细胞到多细胞的真核生物。
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英文标题：
《The thermodynamic efficiency of computations made in cells across the
  range of life》
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作者：
Christopher P. Kempes, David Wolpert, Zachary Cohen, Juan
  P\'erez-Mercader
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最新提交年份：
2017
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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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英文摘要：
  Biological organisms must perform computation as they grow, reproduce, and evolve. Moreover, ever since Landauer's bound was proposed it has been known that all computation has some thermodynamic cost -- and that the same computation can be achieved with greater or smaller thermodynamic cost depending on how it is implemented. Accordingly an important issue concerning the evolution of life is assessing the thermodynamic efficiency of the computations performed by organisms. This issue is interesting both from the perspective of how close life has come to maximally efficient computation (presumably under the pressure of natural selection), and from the practical perspective of what efficiencies we might hope that engineered biological computers might achieve, especially in comparison with current computational systems. Here we show that the computational efficiency of translation, defined as free energy expended per amino acid operation, outperforms the best supercomputers by several orders of magnitude, and is only about an order of magnitude worse than the Landauer bound. However this efficiency depends strongly on the size and architecture of the cell in question. In particular, we show that the {\it useful} efficiency of an amino acid operation, defined as the bulk energy per amino acid polymerization, decreases for increasing bacterial size and converges to the polymerization cost of the ribosome. This cost of the largest bacteria does not change in cells as we progress through the major evolutionary shifts to both single and multicellular eukaryotes. However, the rates of total computation per unit mass are nonmonotonic in bacteria with increasing cell size, and also change across different biological architectures including the shift from unicellular to multicellular eukaryotes.
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PDF链接：
https://arxiv.org/pdf/1706.05043

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关键词：热力学 Quantitative computations Architecture Evolutionary 单细胞 amino 计算 效率 computation