Sizing up allometric scaling theory.
Sizing up allometric scaling theory.
复制标题
大小提高异形缩放理论。
DOI：
10.1371/journal.pcbi.1000171
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发表时间：
2008-09-12
影响因子：
4.3
通讯作者：
Fontana, Walter
中科院分区：
文献类型：
--
作者：
Savage, Van M.;Deeds, Eric J.;Fontana, Walter
代谢率、心率、寿命和许多其他生理特性以系统和相互关联的方式随体重而变化。目前的经验数据表明，这些比例关系采取幂律的形式，指数是四分之一的简单倍数。10年前，韦斯特、布朗和恩奎斯特（West，Brown，and Enquist，WBE）对这一观察结果提出了一个令人信服的解释。他们的框架阐明了代谢率和体重之间的联系，重点是资源分配网络的动力学和结构，在哺乳动物的情况下，心血管系统。在这个框架内，WBE模型基于八个假设，从这些假设中推导出众所周知的观测标度指数3/4。在本文中，我们澄清，这一结果仅适用于无限网络大小（身体质量）的限制和模型预测的实际指数取决于正在研究的生物体的大小。由于未能澄清和探索这种近似的性质，导致了关于WBE模型的相互矛盾的辩论。我们计算的3/4指数的有限大小的修正的解析表达式，导致在一个频谱的标度指数作为绝对网络大小的函数。当考虑到在哺乳动物中观察到的八个数量级的大小范围内的这些校正时，WBE模型预测的标度指数为0.81，似乎与数据不符。然后，我们继续研究的敏感性标度指数的变化，在几个假设的基础WBE模型，总是在有限大小的修正。在这里，我们从模型中得出的趋势似乎与经验数据中可检测到的趋势不一致。我们的工作说明了WBE框架在异速生长标度推理中的实用性，同时表明当前的规范模型可能需要修改，以使其预测完全符合可用的数据集。生物体产生能量维持生命的速率随体重的3/4次方而增加。10年前，韦斯特、布朗和恩奎斯特提出，这种经验关系源于资源分配网络（如心血管系统）的结构和动态。使用捕获物理和生物约束的假设，他们定义了一个预测3/4标度指数的血管网络模型。在我们的论文中，我们澄清，这个模型产生的3/4指数，只有在无限大的生物体的限制。我们的计算表明，在有限尺寸版本的模型代谢率和体重不相关的纯幂律，我们表明是与现有数据一致。我们还表明，这导致模型产生的标度指数显着大于观察到的3/4。我们研究了网络结构的某些假设的变化如何影响标度指数，从而确定可用数据与有限大小模型预测之间的差异。这表明，模型、数据或两者都需要重新评估。挑战在于精确定位限制驱动代谢缩放的网络形状的生理和进化因素。
Metabolic rate, heart rate, lifespan, and many other physiological properties vary with body mass in systematic and interrelated ways. Present empirical data suggest that these scaling relationships take the form of power laws with exponents that are simple multiples of one quarter. A compelling explanation of this observation was put forward a decade ago by West, Brown, and Enquist (WBE). Their framework elucidates the link between metabolic rate and body mass by focusing on the dynamics and structure of resource distribution networks—the cardiovascular system in the case of mammals. Within this framework the WBE model is based on eight assumptions from which it derives the well-known observed scaling exponent of 3/4. In this paper we clarify that this result only holds in the limit of infinite network size (body mass) and that the actual exponent predicted by the model depends on the sizes of the organisms being studied. Failure to clarify and to explore the nature of this approximation has led to debates about the WBE model that were at cross purposes. We compute analytical expressions for the finite-size corrections to the 3/4 exponent, resulting in a spectrum of scaling exponents as a function of absolute network size. When accounting for these corrections over a size range spanning the eight orders of magnitude observed in mammals, the WBE model predicts a scaling exponent of 0.81, seemingly at odds with data. We then proceed to study the sensitivity of the scaling exponent with respect to variations in several assumptions that underlie the WBE model, always in the context of finite-size corrections. Here too, the trends we derive from the model seem at odds with trends detectable in empirical data. Our work illustrates the utility of the WBE framework in reasoning about allometric scaling, while at the same time suggesting that the current canonical model may need amendments to bring its predictions fully in line with available datasets. The rate at which an organism produces energy to live increases with body mass to the 3/4 power. Ten years ago West, Brown, and Enquist posited that this empirical relationship arises from the structure and dynamics of resource distribution networks such as the cardiovascular system. Using assumptions that capture physical and biological constraints, they defined a vascular network model that predicts a 3/4 scaling exponent. In our paper we clarify that this model generates the 3/4 exponent only in the limit of infinitely large organisms. Our calculations indicate that in the finite-size version of the model metabolic rate and body mass are not related by a pure power law, which we show is consistent with available data. We also show that this causes the model to produce scaling exponents significantly larger than the observed 3/4. We investigate how changes in certain assumptions about network structure affect the scaling exponent, leading us to identify discrepancies between available data and the predictions of the finite-size model. This suggests that the model, the data, or both, need reassessment. The challenge lies in pinpointing the physiological and evolutionary factors that constrain the shape of networks driving metabolic scaling.
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影响因子：
4
作者：
Becker, P;Gribben, RJ
通讯作者：
Gribben, RJ
影响因子：
2.7
作者：
BLUEWEISS, L;FOX, H;SAMS, S
通讯作者：
SAMS, S
影响因子：
2
作者：
Dodds, PS;Rothman, DH;Weitz, JS
通讯作者：
Weitz, JS
DOI：
10.1073/pnas.162216899
发表时间：
2002-08-06
影响因子：
11.1
作者：
Banavar, JR;Damuth, J;Rinaldo, A
通讯作者：
Rinaldo, A
影响因子：
5.2
作者：
Etienne, Rampal S.;Apol, M. Emile F.;Olff, Han
通讯作者：
Olff, Han