Courtney Astore


Courtney Astore





Courtney Astore is an aspiring student leader, mentor, entrepreneur, and inventor. She is enrolled in undergraduate degree programs at the University of Central Florida for Biotechnology and Biomedical Science. She is also the co-founder of Enasci-x, a STEM technology start-up company currently working on their first venture Genes4Vaccines, with a mission to improve the early stages of vaccine development through computational predictions. She has been involved in science research for a decade and has been recognized at the regional, state and international level for her innovative research projects. Courtney has interned at prestigious research institutions such as Roskamp Institute & Sanford Burnham. She is passionate about developing novel technology, tools and solutions to aid in the global health challenges of tomorrow. Not only is she passionate about STEM and promotion of such education, but is also a tireless spokesperson and advocate for those with neurological and learning disabilities. She currently resides in Orlando, Florida as she continues to expand her working network around the world with fellow professionals and educators that share her vision-- one person can truly make a profound difference and positive impact on the world.

Faculty Mentor

James Hickman, Ph.D.; Ivan Garibay, Ph.D.; Aaron Smith, Ph.D.

Undergraduate Major

Biotechnology and Biomedical Sciences

Future Plans

M.S. in Bioinformatics or Computer Science & Ph.D. in Biomedical Engineering


Title: Computational microfluidic modeling for design and optimization of multi-organ body-on-a-chip systems

Institutions: Hybrid Systems Lab, Nanoscience Technology Center, University of Central Florida,

Mentor: Dr. James Hickman

Abstract: The pharmaceutical industry spends billions of dollars researching and developing new drug compounds annually; however, late in the development process, it is often discovered many of these potential drugs have unintended and undesirable side effects. In vitro body-on-a-chip systems using human organ constructs aim to mimic aspects of human responses to pharmaceuticals for in vitro investigations of drug candidates for safety and efficacy. In these body-on-a-chip systems, fluidic connections among the organs enables the study of organ-organ interactions. Fluid flow through the microfluidic channels is a major component of the absorption, distribution, metabolism and excretion (ADME pharmacokinetics) of a compound within the system. Computational fluid dynamic (CFD) modeling is a powerful tool for predicting the fluid flow and species transport within the systems to predict the fluidic environment the tissues experience. Shear stresses can be critical for physiological function or be detrimental to cell health, depending on cell type and range of shear stress. Body-on-a-chip systems with two different organ constructs (liver, cardiomyocytes) were designed and modeled using commercially available computer aided design (CAD) and CFD software (CFD-ACE+) for recirculating medium flow using an oscillatory flow profile. CFD modeling drove changes to the geometries of the microfluidic pathways and chambers to optimize the shear stresses and flow rates among the organ chambers. Incorporating computational modeling into the design and evaluation processes enables physiological microfluidic body-on-a-chip systems with fewer physical iterations and a faster development timeline and ability to integrate into pharmacodynamic-pharmacokinetic (PDPK) prediction models.

Title: Genes4Vaccines: A computational model that utilizes comparative genetics to identify DNA & protein sequences for novel vaccines

University of Central Florida & University of Florida

Mentor: Aaron Smith, Ph.D.

With the lack of efficient treatment for many devastating infections, the emergence of multidrug resistant bacteria, and the great promise for innovative vaccine design and research with genomics, vaccine research and development is experiencing a renaissance of interest from the global scientific community. An emerging field known as ‘reverse vaccinology’ uses a combination of whole-genome sequencing, in silico processing, and recombinant DNA technology to develop new vaccines. Only 1 for every 5,000 to 10,000 compounds screened is approved by the Food and Drug Administration. As a result it takes a long period of time to create a vaccine that will be completely approved, 10 to 12 years. During the early stages of development the risk of failure is at its highest. This is because much of early stage development is based off trial and error of different components of a vaccine. To eliminate this dated guess-and-check methodology, an algorithm, Genes4Vaccines ,will aid in predicting the specific DNA and protein sequences for antigens and/or virmugens of bacteria and viruses. Genes4Vaccines can be utilized in developing novel vaccines, as well as predicting their efficacy. By collecting mass data on biological classification properties of current vaccines, such as molecule role and protein length, from publicly available databases and developing a statistical model, it is anticipated that Genes4Vaccines will be able to decrease the time and monetary investment in the early stages of vaccine development.

An agent-based model to study the effects of epidemiological factors on three common Influenza strains’ virulence

Institution: University of Central Florida, Department of Industrial Engineering and Management Systems

Principle Investigator: Ivan Garibay, Ph.D.

Abstract: Influenza is a virus that has caused harm at a devastating level worldwide. The biotechnology industry spends billions of dollars researching and developing new vaccines for Influenza every year due to the abrupt antigenic shifts and the subtle antigenic drifts. Currently, there are a variety of Influenza vaccines that are prescribed based on one’s age and health. The goal of this project is to identify the influence of discrete factors, such as vaccination rates, and temperature on the overall rate of different Influenza strains’ infection in the South Atlantic region for a given year. This was accomplished by developing an agent-based model to simulate the progression of the virus from the 2010 through 2016 seasons. Although similar models have been completed before on Influenza, the objective of this project was to analyze the differences and similarities of three common strains of Influenza based upon their contagiousness. The three common strains analyzed throughout this study were H1N1, H3, and H3N2. The biological differences between these three strains are found in the variations in the binding molecules, hemagglutinin and neuraminidase.
To study such variations, an agent-based model was created using the NetLogo software to simulate the spread of the different Influenza strains amongst individuals in the South Atlantic region. For the purpose of this project, 3 agent-based models, one for each strain, of the same functionality were developed. The agent based model developed accounts for the behavior and effectiveness of the actual annual Influenza vaccine. That is, it accounts for one to create an immune response to the most common Influenza strains predicted for the upcoming season and that one only maintains protection for various strains if the specific sequence is accounted for in the vaccine cocktail. Alternatively, individuals can also create immunity to a specific strain by getting infected with the specific strain and creating immunity for it. In the model, the infected individuals are marked as a red agent, the healthy individuals are marked as a white agent, and the individuals who have recovered from a specific strain of Influenza are marked as a blue agent. The results obtained from the proposed model demonstrated the behavioral similarities and differences of the three Influenza strains amongst the South Atlantic region population. Such understanding will help doctors and researchers better understand the effectiveness of the Influenza vaccine as well as the virulence of the different subtypes of Influenza. Therefore, this simulation could help medical professionals visualize, analyze, and understand if there is a need to reposition the Influenza vaccine in the future.


Medicine and Health Sciences

Courtney Astore