Blood is pumped from the heart to the capillaries through a highly branched and interconnected network known as the cardiovascular system, with vessel sizes that range from microns to centimeters and blood flow speeds that differ by a factor of 1000. Similar types of vascular networks are essential for the flow of resources in nearly all multicellular organisms, including plants (xylem networks), insects (tracheal networks), and mammals. Understanding which evolutionary principles and environmental factors drive the structure of vascular networks, and what constrains the flow through them, could help lead to a better understanding of organismic structure and function with implications for systems as diverse as forests, food webs, and even tumors. This research project will supply the data needed to directly test existing models and to develop new, more realistic models. Outputs will be new software for extracting vascular data from images (e.g., Magnetic Resonance Imaging (MRI), Computed Tomography (CT)), a large database for vascular measurements in plants and animals, identification of branching patterns common to specific taxa or tissues, and new theory. Measurements will include vessel radii, lengths, branching ratios, and branching angles. Existing models are contradicted by some preliminary results, including asymmetric branching (two daughter vessels of different sizes and different flow) and deviations from self-similarity (similar branching patterns recurring across scales). New models will be constructed to incorporate these findings and used to predict connections between the geometry of and flow through vascular networks. A key prediction of models will be scaling exponents that describe how the number of capillaries changes with network volume. In an attempt to enable quick translation of branching geometry into predictions for flow rate, techniques will be adapted to classify vascular networks according to a suite of these characteristic scaling exponents.
Closely integrated with these research objectives are three educational goals: 1) teach students how to use image recognition software to extract data from images across biological fields, including museum collections and labs, 2) teach students how to translate empirical results into equations and test specific, mechanistic hypotheses, and 3) motivate data sharing with the public and scientific community via websites for large, comprehensive databases that will accelerate scientific research and community outreach. High school, undergraduate, and graduate students, as well as postdoctoral researchers will all be trained during this research. Notably, summer research experiences for three high school students will be provided for each year of the project. A critical aspect of this training is in teaching students how to combine theory and empirical data and how to disseminate research findings through publications, websites, curricular materials, and talks at professional meetings as well as outreach. The PI has experience educating and training students at all levels and will actively recruit students from under-represented groups to engage in informatics research. Publications, databases, code, and curricular materials will all be made available through websites. Together, this work will provide an example of how computer vision techniques can be used to extract data from biological images around the world.
