The Dynamic Origin of Kleiber's Law
TLDR
This paper redefines Kleiber's Law, showing it arises from pulsatile wave physics rather than static network geometry.
Key contributions
- Kleiber's Law ($3/4$ metabolic scaling) stems from pulsatile wave physics, not static network geometry.
- Derives a generalized metabolic exponent $β= dα/(2d+α)$ linking local transport to global scaling.
- Explains the shift to steeper scaling ($β\approx 0.9$) in small organisms via a wave-to-viscous transition.
- Validates framework across diverse biological systems, from vertebrate vasculature to plant xylem.
Why it matters
This paper fundamentally reinterprets Kleiber's Law, shifting its origin from static network geometry to dynamic wave physics. It offers a robust framework explaining empirical shifts in metabolic scaling and unifies diverse biological transport networks.
Original Abstract
The ubiquitous $3/4$ metabolic scaling exponent, known as Kleiber's law, has long been attributed to the minimization of viscous dissipation within fractal transport networks. In this paper, we invert this standard narrative, demonstrating that Kleiber's law is fundamentally a signature of pulsatile wave physics rather than steady-state geometry. By coupling local branching optimization to global allometry, we derive the exact generalized metabolic exponent $β= dα/(2d+α)$, which strictly maps local transport microphysics to global organismal scaling. We show that dynamic wave-impedance matching in the proximal vasculature uniquely enforces $β= 3/4$ in three dimensions. This bound is dynamically protected: no static optimization of a viscous network can reproduce it. Consequently, we analytically predict the critical body mass for the wave-to-viscous transition, successfully explaining the empirical shift to steeper allometric scaling ($β\approx 0.9$) in small mammals and invertebrates with no free parameters. Furthermore, we demonstrate that the classical West--Brown--Enquist (WBE) derivation is structurally divergent under its own geometric assumptions, failing at the required proximal-dominance limit. Our framework is validated across nine biological systems spanning five phyla -- including vertebrate vasculature, insect tracheae, plant xylem, and sponge canals -- accurately predicting empirical branching exponents from independent biophysical measurements. Ultimately, we establish a general allometric equation of state that organizes diverse biological networks into discrete universality classes, generating falsifiable predictions across clades from shrews to flatworms.
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