Whole-cell modeling of E.coli
Collaborators: Prof. Markus Covert and lab
Check out the model on our GitHub repo: github.com/CovertLab/wcEcoli
Unlocking the Feasibility of Rare Disease Drug Development
Authors: Jin Hyun Ju*, PhD, Riley Juenemann*, BS, Stevan Methven Jeknic, PhD, Issa Benna PhD, Morgan Paull, MS, Sun-Gou Ji, PhD, Neil Kumar, PhD
*Authors contributed equally to this work.
Read the whitepaper here!
Using quantitative systems modeling, we identify key factors that influence
the economic feasibility of drug development for rare diseases. The
interactive web tool that allows users to explore how changes in these factors
impact the net present value (NPV) of a hypothetical rare disease drug development
program. Our findings highlight strategies to improve the economic viability of
rare disease drug development, ultimately aiming to enhance patient access to novel therapies.
A first-pass statistical dashboard for categorizing diverse particle movement patterns
Collaborators: Prof. Scott A McKinley, Prof. Christine K Payne
Try out the dashboard here!
Read my undergraduate honors thesis here!
At the intersection of nanoscience and biology lies the question of precisely how particles move within cells. Recent developments in imaging, including quantum dots and semiconductor nanoparticles, have made it possible for researchers to peek beneath the surface of living cells, tracking individual particles over significant timescales. In contrast to in vitro particle tracking experiments, wherein there are great controls on particle and environmental homogeneity, live-cell tracking features tremendous diversity in particle movement.
In this research area, the use of mathematics has allowed for a better description of movement categorizations and quantitative methods to differentiate between them. We have developed a first-pass statistical dashboard to categorize disparate types of particle trajectories. Based on six statistical measures, a support vector machine was utilized to distinguish between free diffusion, anchored diffusion, directed transport, tracker error, subdiffusion, and skating diffusion. This automated categorization process proved to be successful on data simulated using stochastic differential equations and provided interesting results on the live-cell data.
I expanded upon this work in both my Mathematics Honors Thesis and Computer Science Capstone, advised by Prof. Scott A McKinley, Prof. Michelle Lacey, and Prof. Jihun Hamm.
Topological data analysis approaches to uncovering the timing of ring structure onset in filamentous networks
Collaborators: Prof. Maria-Veronica Ciocanel, Prof. Adriana T Dawes, Prof. Scott A McKinley
Bulletin of Mathematical Biology, 83(10), (2021). DOI PDF
arXiv
We apply topological data analysis techniques to detecting the formation of persistent ring channels in the interaction between protein filaments (actin) and motor proteins (myosins). We apply these methods to agent-based mechanical and chemical simulations and introduce visualization tools for analyzing the organization of filamentous networks.