Branching principles of animal and plant networks identified by combining extensive data, machine learning and modelling

Mapping the blood vasculature in an intact human kidney using hierarchical phase-contrast tomography

The architecture of the kidney vasculature is essential for its function. Although structural profiling of the intact rodent kidney vasculature has been performed, it is challenging to map vascular architecture of larger human organs. We hypothesised that hierarchical phase-contrast tomography (HiP-CT) would enable quantitative analysis of the entire human kidney vasculature. Combining label-free HiP-CT imaging of an intact kidney from a 63-year-old male with topology network analysis, we quantitated vasculature architecture in the human kidney down to the scale of arterioles. Although human and rat kidney vascular topologies are comparable, vascular radius decreases at a significantly faster rate in humans as vessels branch from artery towards the cortex. At branching points of large vessels, radii are theoretically optimised to minimise flow resistance, an observation not found for smaller arterioles. Structural differences in the vasculature were found in different spatial zones of the kidney reflecting their unique functional roles. Overall, this represents the first time the entire arterial vasculature of a human kidney has been mapped providing essential inputs for computational models of kidney vascular flow and synthetic vascular architectures, with implications for understanding how the structure of individual blood vessels collectively scales to facilitate organ function.

Neuronal branching is increasingly asymmetric near synapses, potentially enabling plasticity while minimizing energy dissipation and conduction time

Neurons' primary function is to encode and transmit information in the brain and body. The branching architecture of axons and dendrites must compute, respond and make decisions while obeying the rules of the substrate in which they are enmeshed. Thus, it is important to delineate and understand the principles that govern these branching patterns. Here, we present evidence that asymmetric branching is a key factor in understanding the functional properties of neurons. First, we derive novel predictions for asymmetric scaling exponents that encapsulate branching architecture associated with crucial principles such as conduction time, power minimization and material costs. We compare our predictions with extensive data extracted from images to associate specific principles with specific biophysical functions and cell types. Notably, we find that asymmetric branching models lead to predictions and empirical findings that correspond to different weightings of the importance of maximum, minimum or total path lengths from the soma to the synapses. These different path lengths quantitatively and qualitatively affect energy, time and materials. Moreover, we generally observe that higher degrees of asymmetric branching-potentially arising from extrinsic environmental cues and synaptic plasticity in response to activity-occur closer to the tips than the soma (cell body).

Neuronal Branching is Increasingly Asymmetric Near Synapses, Potentially Enabling Plasticity While Minimizing Energy Dissipation and Conduction Time

Neurons' primary function is to encode and transmit information in the brain and body. The branching architecture of axons and dendrites must compute, respond, and make decisions while obeying the rules of the substrate in which they are enmeshed. Thus, it is important to delineate and understand the principles that govern these branching patterns. Here, we present evidence that asymmetric branching is a key factor in understanding the functional properties of neurons. First, we derive novel predictions for asymmetric scaling exponents that encapsulate branching architecture associated with crucial principles such as conduction time, power minimization, and material costs. We compare our predictions with extensive data extracted from images to associate specific principles with specific biophysical functions and cell types. Notably, we find that asymmetric branching models lead to predictions and empirical findings that correspond to different weightings of the importance of maximum, minimum, or total path lengths from the soma to the synapses. These different path lengths quantitatively and qualitatively affect energy, time, and materials. Moreover, we generally observe that higher degrees of asymmetric branching- potentially arising from extrinsic environmental cues and synaptic plasticity in response to activity- occur closer to the tips than the soma (cell body).

Spatial and Texture Analysis of Root System distribution with Earth mover's Distance (STARSEED)

PURPOSE

Root system architectures are complex and challenging to characterize effectively for agronomic and ecological discovery.

METHODS

We propose a new method, Spatial and Texture Analysis of Root SystEm distribution with Earth mover's Distance (STARSEED), for comparing root system distributions that incorporates spatial information through a novel application of the Earth Mover's Distance (EMD).

RESULTS

We illustrate that the approach captures the response of sesame root systems for different genotypes and soil moisture levels. STARSEED provides quantitative and visual insights into changes that occur in root architectures across experimental treatments.

CONCLUSION

STARSEED can be generalized to other plants and provides insight into root system architecture development and response to varying growth conditions not captured by existing root architecture metrics and models. The code and data for our experiments are publicly available: https://github.com/GatorSense/STARSEED .

Variable metabolic scaling breaks the law: from 'Newtonian' to 'Darwinian' approaches

Life's size and tempo are intimately linked. The rate of metabolism varies with body mass in remarkably regular ways that can often be described by a simple power function, where the scaling exponent (b, slope in a log-linear plot) is typically less than 1. Traditional theory based on physical constraints has assumed that b is 2/3 or 3/4, following natural law, but hundreds of studies have documented extensive, systematic variation in b. This overwhelming, law-breaking, empirical evidence is causing a paradigm shift in metabolic scaling theory and methodology from ‘Newtonian’ to ‘Darwinian’ approaches. A new wave of studies focuses on the adaptable regulation and evolution of metabolic scaling, as influenced by diverse intrinsic and extrinsic factors, according to multiple context-dependent mechanisms, and within boundary limits set by physical constraints.

Cancer as a Model System for Testing Metabolic Scaling Theory

Biological allometries, such as the scaling of metabolism to mass, are hypothesized to result from natural selection to maximize how vascular networks fill space yet minimize internal transport distances and resistance to blood flow. Metabolic scaling theory argues two guiding principles—conservation of fluid flow and space-filling fractal distributions—describe a diversity of biological networks and predict how the geometry of these networks influences organismal metabolism. Yet, mostly absent from past efforts are studies that directly, and independently, measure metabolic rate from respiration and vascular architecture for the same organ, organism, or tissue. Lack of these measures may lead to inconsistent results and conclusions about metabolism, growth, and allometric scaling. We present simultaneous and consistent measurements of metabolic scaling exponents from clinical images of lung cancer, serving as a first-of-its-kind test of metabolic scaling theory, and identifying potential quantitative imaging biomarkers indicative of tumor growth. We analyze data for 535 clinical PET-CT scans of patients with non-small cell lung carcinoma to establish the presence of metabolic scaling between tumor metabolism and tumor volume. Furthermore, we use computer vision and mathematical modeling to examine predictions of metabolic scaling based on the branching geometry of the tumor-supplying blood vessel networks in a subset of 56 patients diagnosed with stage II-IV lung cancer. Examination of the scaling of maximum standard uptake value with metabolic tumor volume, and metabolic tumor volume with gross tumor volume, yield metabolic scaling exponents of 0.64 (0.20) and 0.70 (0.17), respectively. We compare these to the value of 0.85 (0.06) derived from the geometric scaling of the tumor-supplying vasculature. These results: (1) inform energetic models of growth and development for tumor forecasting; (2) identify imaging biomarkers in vascular geometry related to blood volume and flow; and (3) highlight unique opportunities to develop and test the metabolic scaling theory of ecology in tumors transitioning from avascular to vascular geometries.