Meet Diamond
“High Throughput and Patient-Specific Blood Systems Biology”
Scott L. Diamond, Ph.D. (B.S., Cornell University; Ph.D., Rice University) is the founding director of the Penn Center for Molecular Discovery. He holds the Arthur E. Humphrey Chair of Chemical and Biomolecular Engineering. Dr. Diamond researches biotechnologies in several key areas: endothelial mechanobiology, blood clot dissolving therapies, blood coagulation, nonviral gene therapy, proteomics, and high throughput drug discovery. He has produced over 120 publications and patents and has served on advisory committees to NSF, NIH, AHA, and NASA, and has consulted extensively for industry and government. Diamond is the recipient of the NSF National Young Investigator Award, the NIH FIRST Award, the American Heart Association Established Investigator Award, the AIChE Allan P. Colburn Award, and the George Heilmeier Excellence in Research Award. Dr. Diamond is an elected Fellow of the Biomedical Engineering Society (BMES). Currently, Dr. Diamond is the Director of the Penn Biotechnology Masters Program, one of the largest in the country with more than 130 students. Dr. Diamond also serves as Associate Director of the Institute for Medicine and Engineering (IME).
Seminar Abstract
“High Throughput and Patient-Specific Blood Systems Biology”
We deployed distinct approaches of bottom-up and top-down analyses to gain insight into platelet signal transduction, platelet adhesion, and coagulation protease cascades under flow conditions. The bottom-up approach involved a computational platelet model assembled from 24 peer-reviewed platelet studies to yield 132 measured kinetic rate constants that accurately predicts resting and stimulated levels of cytosolic calcium, IP3, diacylglycerol, phosphatidic acid, phosphoinositol, PIP, and PIP2. Additionally, we used a top-down approach to study patient-specific platelet signaling to combinatorial exposure to agonists encountered during thrombosis. A high throughput assay measured intracellular calcium responses to pairwise combinations of 6 major platelet agonists. The calcium responses trained a neural network (NN) model to predict the entire 6-dimensional platelet response space. These approaches were then assembled in multiscale simulation of clotting under flow where: (1) the changing flow field was solved by Lattice Boltzmann, (2) platelet aggregation and deposition was solved by lattice kinetic Monte Carlo, (3) soluble species were solved by finite element models for convection-diffusion-reaction, and (4) platelet activation was solved by patient-specific trained neural networks. Patient-specific simulations predicted platelet deposition rates on collagen under flow in the presence of indomethacin (COX inhibitor) or iloprost (prostacylin analog), as measured in microfluidic devices. Patient-trained NN representation of an individual’s platelets are ideal for use in multiscale models to predict clinical risk, disease progression, pharmacological responses, and novel phenotypes.
