title: Testing Foundations of Biological Scaling Theory Using Automated Measurements of Vascular Networks.
abstract: Scientists have long sought to understand how vascular networks supply blood and oxygen to cells throughout the body. Recent work focuses on principles that constrain how vessel size changes through branching generations from the aorta to capillaries and uses scaling exponents to quantify these changes. Prominent scaling theories predict that combinations of these exponents explain how metabolic, growth, and other biological rates vary with body size. Nevertheless, direct measurements of individual vessel segments have been limited because existing techniques for measuring vasculature are invasive, time consuming, and technically difficult. We developed software that extracts the length, radius, and connectivity of in vivo vessels from contrast-enhanced 3D Magnetic Resonance Angiography. Using data from 20 human subjects, we calculated scaling exponents by four methods-two derived from local properties of branching junctions and two from whole-network properties. Although these methods are often used interchangeably in the literature, we do not find general agreement between these methods, particularly for vessel lengths. Measurements for length of vessels also diverge from theoretical values, but those for radius show stronger agreement. Our results demonstrate that vascular network models cannot ignore certain complexities of real vascular systems and indicate the need to discover new principles regarding vessel lengths.
authors: Department of Biomathematics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America.
authors: Mitchell G
authors: MG
authors: Newberry
authors: Department of Radiological Sciences, Biomedical Physics, and Bioengineering, University of California, Los Angeles, Los Angeles, California, United States of America.
authors: Daniel B
authors: DB
authors: Ennis
authors: Department of Biomathematics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America; Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, California, United States of America; Santa Fe Institute, Santa Fe, New Mexico, United States of America.
authors: Van M
authors: VM
authors: Savage
mesh_terms: D000328:Adult; D000465:Algorithms; D002340:Carotid Artery Diseases / Q000473:pathology; D019295:Computational Biology / Q000379:methods*; D006801:Humans; D018810:Magnetic Resonance Angiography / Q000379:methods*; D008955:Models, Cardiovascular; D011446:Prospective Studies; D012044:Regression Analysis