Bio: Keisha Cook is an Assistant Professor in the School of Mathematical and Statistical Sciences at Clemson University. She received a Bachelors Degree in applied mathematics from The University of Alabama in 2014 and a PhD in applied mathematics and computational biology from The University of Alabama in 2019. Afterwards she completed a postdoctoral research position at Tulane University and the Southeast Center for Mathematics and Biology. Her research is in applied and computational biology; e.g. stochastic processes and single particle tracking of biological, physical, and ecological processes. Her analyses are used by experimentalists to provide a deeper understanding of various processes and introduce innovative ways to categorize movement. In addition to research, she teaches courses in probability and stochastic processes.

Outside of teaching and research, she is an organizer, speakers, and lecturer for many conferences, workshops, and committees focusing on mentoring, highlighting, and providing opportunities for underrepresented minorities in mathematics. She strives to support and mentor underrepresented students, postdocs, and researchers throughout her career in academia. She has a keen interest in increasing the number of women and underrepresented students in STEM. She has participated in and served as a mentor, organizer, and lead for several programs supporting under-represented minority students in mathematics. (EDGE Program for Women: Enhancing Diversity in Graduate Education, AMIGAs: Applied mathematics skills improvement for graduate studies advancement program, AWM, and Workshop Celebrating Diversity for the Society for Industrial and Applied  Mathematics)

More information can be found here.

Topics Include:

Building mathematical models of intracellular processes
Biological systems are traditionally studied as isolated processes (e.g. regulatory pathways, motor protein dynamics, transport of organelles, etc.). Although more recent approaches have been developed to study whole cell dynamics, integrating knowledge across biological levels remains largely unexplored. In experimental processes, we assume that the state of the system is unknown until we sample it. Many scales are necessary to quantify the dynamics of different processes. These may include a magnitude of measurements, multiple detection intensities, or variation in the magnitude of observations. The interconnection between scales, where events happening at one scale are directly influencing events occurring at other scales, can be accomplished using mathematical tools for integration to connect and predict complex biological outcomes. I rely on single particle tracking techniques based on stochastic models and explore long-term dynamics of the systems.

Quantification of endosomal escape
One of the most important aspects of drug delivery in cancer therapy is endosomal escape. The endocytosis process is the main uptake mechanism to transport peptides, proteins, and other biological agents into the cell. When therapeutic drugs enter the cell, they are encapsulated in endosomes. During the intracellular delivery process, these drugs must escape the endosome to reach the cytosol and their desired cancer-infested target. We developed a mathematical model to quantify the endosomal escape of siRNA over a series of drug delivery experiments. With a combination of stochastic simulation models, dynamical systems, parameter estimation, and statistical validation methods, our novel quantification approach answers many questions about measuring endosomal escape. Our quantification model contributes to the advancement of a significantly important aspect in the drug delivery research community.

Using applied mathematics and statistics technology in the classroom
Hands-on, visualization activities in computational math courses inspire student learning of probability and statistics. Today’s world is full of readily available data and students are typically interested in how they can use course topics in future coursework, jobs, and/or in the real world. Outside of teaching proofs and written computations, students need to grasp the whole story of probability and statistics by understanding how useful it is. Without compromising the theoretical understanding of the subjects, instructors can implement coding activities in their course lectures that go hand in hand with the written lectures. Students can engage with the lectures while learning incredibly versatile skills, which promotes forward-thinking and innovation. In this talk, we explore ways to implement hands-on coding activities in class and in homework assignments as well as the benefits that this provides students.