Graphical user interfaces as chemical engineering educational tools in university and informal learning environments

I will discuss the development and use of graphical user interfaces (GUIs) as cyber-assisted educational tools for instructing and engaging undergraduate chemical engineering students, training graduate students for computational research in science and engineering, and introducing lay audiences to chemical engineering concepts in informal learning environments outside of the classroom. MATLAB and Python both provide excellent user support for rapid development of professional-quality GUIs by engineering educators, academic researchers, and science and engineering undergraduate and graduate students. These GUIs can be distributed and run easily by novice users without any prior programming experience. I will provide examples of customized GUIs from my research lab and courses that demonstrate their use in the undergraduate curriculum, in an interdisciplinary upper division/graduate elective called Applied Numerical Computing for Scientists and Engineers, and in several informal learning environments for science, technology, engineering, and mathematics (STEM) outreach. The informal learning environments where my team has utilized these GUIs include a pre-college program for incoming engineering freshmen, a summer camp for children of university alumni and the campers’ grandparents, and a hands-on science fair featuring interactive demonstration booths for middle school and high school girls and their teachers.Chemical Engineerin

Social Buffering of Pesticides in Bumblebees: Agent-Based Modeling of the Effects of Colony Size and Neonicotinoid Exposure on Behavior Within Nests

Neonicotinoids are a globally prevalent class of pesticides that can negatively affect bees and the pollination services they provide. While there is evidence suggesting that colony size may play an important role in mitigating neonicotinoid exposure in bees, mechanisms underlying these effects are not well understood. Here, a recently developed agent-based computational model is used to investigate how the effects of sub-lethal neonicotinoid exposure on intranest behavior of bumblebees (Bombus impatiens) are modulated by colony size. Simulations from the model, parameterized using empirical data on bumblebee workers exposed to imidacloprid (a common neonicotinoid pesticide), suggest that colony size has significant effects on neonicotinoid-sensitivity within bumblebee nests. Specifically, differences are reduced between treated and untreated workers in larger colonies for several key aspects of behavior within nests. Our results suggest that changes in both number of workers and nest architecture may contribute to making larger colonies less sensitive to pesticide exposure

Derivation of an Analytical Solution to a Reaction-Diffusion Model for Autocatalytic Degradation and Erosion in Polymer Microspheres

A mathematical reaction-diffusion model is defined to describe the gradual decomposition of polymer microspheres composed of poly(D,L-lactic-co-glycolic acid) (PLGA) that are used for pharmaceutical drug delivery over extended periods of time. The partial differential equation (PDE) model treats simultaneous first-order generation due to chemical reaction and diffusion of reaction products in spherical geometry to capture the microsphere-size-dependent effects of autocatalysis on PLGA erosion that occurs when the microspheres are exposed to aqueous media such as biological fluids. The model is solved analytically for the concentration of the autocatalytic carboxylic acid end groups of the polymer chains that comprise the microspheres as a function of radial position and time. The analytical solution for the reaction and transport of the autocatalytic chemical species is useful for predicting the conditions under which drug release from PLGA microspheres transitions from diffusion-controlled to erosion-controlled release, for understanding the dynamic coupling between the PLGA degradation and erosion mechanisms, and for designing drug release particles. The model is the first to provide an analytical prediction for the dynamics and spatial heterogeneities of PLGA degradation and erosion within a spherical particle. The analytical solution is applicable to other spherical systems with simultaneous diffusive transport and first-order generation by reaction

Derivation of an Analytical Solution to a Reaction-Diffusion Model for Autocatalytic Degradation and Erosion in Polymer Microspheres

A mathematical reaction-diffusion model is defined to describe the gradual decomposition of polymer microspheres composed of poly(D,L-lactic-co-glycolic acid) (PLGA) that are used for pharmaceutical drug delivery over extended periods of time. The partial differential equation (PDE) model treats simultaneous first-order generation due to chemical reaction and diffusion of reaction products in spherical geometry to capture the microsphere-size-dependent effects of autocatalysis on PLGA erosion that occurs when the microspheres are exposed to aqueous media such as biological fluids. The model is solved analytically for the concentration of the autocatalytic carboxylic acid end groups of the polymer chains that comprise the microspheres as a function of radial position and time. The analytical solution for the reaction and transport of the autocatalytic chemical species is useful for predicting the conditions under which drug release from PLGA microspheres transitions from diffusion-controlled to erosion-controlled release, for understanding the dynamic coupling between the PLGA degradation and erosion mechanisms, and for designing drug release particles. The model is the first to provide an analytical prediction for the dynamics and spatial heterogeneities of PLGA degradation and erosion within a spherical particle. The analytical solution is applicable to other spherical systems with simultaneous diffusive transport and first-order generation by reaction.National Institutes of Health (U.S.) (NIBIB 5RO1EB005181)National Science Foundation (U.S.) (Grant 0426328)United States. Dept. of Energy (Computational Science Graduate Fellowship Contract DE-FG02-97ER25308

Bifurcation study of blood flow control in the kidney.

Renal blood flow is maintained within a narrow window by a set of intrinsic autoregulatory mechanisms. Here, a mathematical model of renal hemodynamics control in the rat kidney is used to understand the interactions between two major renal autoregulatory mechanisms: the myogenic response and tubuloglomerular feedback. A bifurcation analysis of the model equations is performed to assess the effects of the delay and sensitivity of the feedback system and the time constants governing the response of vessel diameter and smooth muscle tone. The results of the bifurcation analysis are verified using numerical simulations of the full nonlinear model. Both the analytical and numerical results predict the generation of limit cycle oscillations under certain physiologically relevant conditions, as observed in vivo

Multiscale Modeling of Tissues, Treatments, and Toxicology

Presented on October 9, 2019 from 3:00 p.m.- 4:00 p.m. in the Molecular Science and Engineering Building (MoSE), Room G011, Georgia Tech.Dr. Ashlee N. Ford Versypt holds three degrees in chemical engineering: a B.S. from the University of Oklahoma and an M.S. and a Ph.D. from the University of Illinois at Urbana-Champaign. During graduate school, Dr. Ford Versypt was awarded the Department of Energy Computational Science Graduate Fellowship (DOE CSGF) and the National Science Foundation Graduate Research Fellowship. In 2013, Dr. Ford Versypt was recognized as the Frederick A. Howes Scholar in Computational Science, which is awarded annually to a recent alumnus of the DOE CSGF for outstanding leadership, character, and technical achievement. In 2012-2014, Dr. Ford Versypt was a postdoctoral research associate with Richard Braatz in the Department of Chemical Engineering at the Massachusetts Institute of Technology. Currently, Dr. Ford Versypt is an assistant professor in the School of Chemical Engineering at Oklahoma State University (OSU). She is a member of the Harold Hamm Diabetes Center and the Stephenson Cancer Center at the University of Oklahoma Health Sciences Center, the Interdisciplinary Toxicology Program at OSU, and the Oklahoma Center for Respiratory Infectious Diseases. She is the Chair-Elect for the American Society for Engineering Education Chemical Engineering Division. Dr. Ford Versypt is active in engaging the public in science through leading more than 60 outreach events for K-12, collegiate, and lay audiences. She has received a number of awards for her research and teaching including the NSF CAREER Award in 2019, AIChE 35 Under 35 for 2017, and the OSU College of Engineering, Architecture and Technology Excellent Teacher Award in 2017. She has mentored 7 graduate students and 31 undergraduate students at OSU since 2014. Her research is currently funded by the National Science Foundation, National Institutes of Health, and the Oklahoma Center for the Advancement of Science and Technology.Runtime: 58:23 minutesThe Systems Biomedicine and Pharmaceutics research lab at Oklahoma State University led by Dr. Ford Versypt focuses on developing and utilizing multiscale systems engineering approaches including mathematical and computational modeling to determine and understand the mechanisms governing physiological effects of various chemicals, e.g., pharmaceutical drugs, toxins, metabolites, and hormones, on human and animal tissues. We specialize in modeling the transport processes and chemical interactions related to both natural and engineered biomedical and pharmaceutical systems. We also develop and refine the computational software elements to support multiscale modeling of such systems. We draw from an interdisciplinary skillset in chemical engineering, pharmaceutics, physiology, applied mathematics, and computational science. In this seminar, vignettes of recently published work from the lab in four different lines of research will be highlighted including (1) the immune system interplay with tuberculosis granulomas, (2) metastatic cancer spread, (3) bumblebee behaviors in response to chronic exposure to pesticides, and (4) glucose-stimulated damage to kidney cells in diabetes and preventative pharmaceutical treatments. The latter area has recently been funded by an NSF CAREER award and exemplifies the integration of teaching, research, and outreach

Mathematical Modeling of Metastatic Cancer Migration through a Remodeling Extracellular Matrix

The spreading of cancer cells, also known as metastasis, is a lethal hallmark in cancer progression and the primary cause of cancer death. Recent cancer research has suggested that the remodeling of collagen fibers in the extracellular matrix (ECM) of the tumor microenvironment facilitates the migration of cancer cells during metastasis. ECM remodeling refers to the following two procedures: the ECM degradation caused by enzyme matrix metalloproteinases and the ECM alignment due to the cross-linking enzyme lysyl oxidase (LOX). Such modifications of ECM collagen fibers result in changes of ECM physical and biomechanical properties that affect cancer cell migration through the ECM. However, the mechanism of such cancer migration through a remodeling ECM remains not well understood. A mathematical model is proposed in this work to better describe and understand cancer migration by means of ECM remodeling. Effects of LOX are considered to enable transport of enzymes and migration of cells through a dynamic, reactive tumor microenvironment that is modulated during cell migration. For validation cases, the results obtained show comparable trends to previously established models. In novel test cases, the model predicts the impact on ECM remodeling and the overall migration of cancer cells due to the inclusion of LOX, which has not yet been included in previous cancer invasion models

A Glucose-Dependent Pharmacokinetic/ Pharmacodynamic Model of ACE Inhibition in Kidney Cells

Diabetic kidney disease (DKD) is a major cause of renal failure. Podocytes are terminally differentiated renal epithelial cells that are key targets of damage due to DKD. Podocytes express a glucose-stimulated local renin-angiotensin system (RAS) that produces angiotensin II (ANG II). Local RAS differs from systemic RAS, which has been studied widely. Hyperglycemia increases the production of ANG II by podocyte cells, leading to podocyte injury. Angiotensin-converting enzyme (ACE) is involved in the production of ANG II, and ACE inhibitors are drugs used to suppress elevated ANG II concentration. As systemic RAS differs from the local RAS in podocytes, ACE inhibitor drugs should act differently in local versus systemic contexts. Experimental and computational studies have considered the pharmacokinetics (PK) and pharmacodynamics (PD) of ACE inhibition of the systemic RAS. Here, a PK/PD model for ACE inhibition is developed for the local RAS in podocytes. The model takes constant or dynamic subject-specific glucose concentration input to predict the ANG II concentration and the corresponding effects of drug doses locally and systemically. The model is developed for normal and impaired renal function in combination with different glucose conditions, thus enabling the study of various pathophysiological conditions. Parameter uncertainty is also analyzed. Such a model can improve the study of the effects of drugs at the cellular level and can aid in development of therapeutic approaches to slow the progression of DKD

Mathematical Modeling of Tuberculosis Granuloma Activation

Tuberculosis (TB) is one of the most common infectious diseases worldwide. It is estimated that one-third of the world’s population is infected with TB. Most have the latent stage of the disease that can later transition to active TB disease. TB is spread by aerosol droplets containing Mycobacterium tuberculosis (Mtb). Mtb bacteria enter through the respiratory system and are attacked by the immune system in the lungs. The bacteria are clustered and contained by macrophages into cellular aggregates called granulomas. These granulomas can hold the bacteria dormant for long periods of time in latent TB. The bacteria can be perturbed from latency to active TB disease in a process called granuloma activation when the granulomas are compromised by other immune response events in a host, such as HIV, cancer, or aging. Dysregulation of matrix metalloproteinase 1 (MMP-1) has been recently implicated in granuloma activation through experimental studies, but the mechanism is not well understood. Animal and human studies currently cannot probe the dynamics of activation, so a computational model is developed to fill this gap. This dynamic mathematical model focuses specifically on the latent to active transition after the initial immune response has successfully formed a granuloma. Bacterial leakage from latent granulomas is successfully simulated in response to the MMP-1 dynamics under several scenarios for granuloma activation