Physiology

Bridge Kleiber's 3/4-power metabolic scaling law (B ~ M^{3/4}) across animals spanning 27 orders of magnitude in body mass is derived from the fractal geometry of space-filling vascular networks: West, Brown & Enquist (1997) proved that the 4/3 exponent arises necessarily from the constraint that hierarchical branching networks minimise hydrodynamic resistance while filling volume fractally.

Fields: Physiology, Physics, Ecology, Mathematics

West, Brown & Enquist (1997) derived Kleiber's law from three assumptions: (1) the vascular network is a self-similar fractal with branching ratio n_b, (2) the terminal units (capillaries/leaf stomata...

Bridge Plant water transport via the cohesion-tension mechanism is governed by Hagen-Poiseuille pipe flow, operating under negative pressures approaching cavitation limits set by fluid physics, with stomatal optimization connecting fluid mechanics to carbon economics.

Fields: Plant Physiology, Fluid Mechanics, Ecophysiology, Climate Science, Biophysics

Water transport in plants is driven by the cohesion-tension mechanism (Dixon & Joly 1895): transpiration at leaf surfaces creates a negative pressure (tension) that pulls water columns up from roots t...

Bridge The efficient coding hypothesis (Barlow 1961) unifies sensory neuroscience and information theory: retinal whitening, V1 Gabor receptive fields, and auditory log-frequency tuning all follow from maximizing Shannon information transmission per unit metabolic cost.

Fields: Neuroscience, Cognitive Science, Information Theory, Sensory Physiology, Computational Neuroscience

Barlow (1961) proposed that the goal of sensory processing is to represent the environment using the minimum number of active neurons — equivalently, to maximize the Shannon mutual information I(stimu...

Bridge Long short-term memory dynamics connect sequence-learning memory gates with ICU physiology forecasting.

Fields: Computer Science, Critical Care, Physiology

Speculative analogy: LSTM gating provides a sequence-memory abstraction that can capture delayed physiological interactions in ICU time-series forecasting....

Bridge West-Brown-Enquist fractal network model ↔ metabolic scaling: Kleiber's law from geometry alone

Fields: Theoretical Biology, Statistical Physics, Network Theory, Physiology, Ecology

Kleiber (1932) observed that basal metabolic rate B scales with body mass M as B ~ M^{3/4} across 20 orders of magnitude of body mass (from bacteria to blue whales). This 3/4-power law defied explanat...

Bridge Optimal transport theory ↔ biological vascular and neural network architecture (Murray's law as Wasserstein flow)

Fields: Mathematics, Fluid Dynamics, Comparative Physiology, Developmental Biology, Neuroscience

Murray's law (1926) — that the cube of the parent vessel radius equals the sum of cubes of daughter radii at every branch point (r_0^3 = r_1^3 + r_2^3) — is the exact solution to a variational problem...

Bridge The renormalization group explains why biological allometric scaling laws are power laws with universal exponents — metabolic scaling, growth rates, and lifespan all emerge from the same fixed-point structure that governs critical phenomena in statistical physics.

Fields: Mathematical Physics, Theoretical Biology, Statistical Physics, Comparative Physiology

The renormalization group (RG) is the standard physics explanation for why power laws arise universally near critical points: when you "coarse-grain" a system (average out short-scale details), the lo...

Bridge The glymphatic system uses perivascular cerebrospinal fluid flow driven by arterial pulsatility and aquaporin-4 water channels to clear amyloid-β and tau from the brain — a fluid dynamics problem with direct Alzheimer's disease implications.

Fields: Neuroscience, Fluid Dynamics, Physiology, Neurology

The glymphatic system (Iliff et al. 2012) uses cerebrospinal fluid (CSF) flow along perivascular spaces (the Virchow-Robin spaces surrounding cerebral arteries) to clear metabolic waste products — inc...

Bridge Sensory neurons as Shannon information channels — efficient coding and neural channel capacity

Fields: Neuroscience, Information Theory, Sensory Physiology, Computational Neuroscience

The nervous system encodes stimuli as spike trains — discrete all-or-none action potentials — which can be analysed as Shannon communication channels. The channel capacity C = B log₂(1 + S/N) bounds t...

Bridge Lymphatic capillary drainage of interstitial fluid is governed by Starling's revised principle: the balance of oncotic and hydrostatic pressures across the capillary wall drives net filtration that lymphatics must absorb, with lymphatic pumping modeled as a pressure-flow relationship analogous to fluid mechanics in compliant vessel networks

Fields: Physiology, Fluid Mechanics

Interstitial fluid homeostasis obeys the revised Starling equation J_v/A = L_p[(P_c - P_i) - σ(π_c - π_i)] where L_p is hydraulic conductivity, P_c and P_i are capillary and interstitial hydrostatic p...