2020 Summer REU Program

Title: Models of Pattern Formation and Decision Making in Slime Mold
Advisors: Dr. Simon Garnier, Dr. Jason Graham

In a complex and dynamic world, how do you navigate your environment when you do not possess a brain, or even the beginnings of a nervous system? From bacteria and immune cells to fungi and plants, the large majority of living beings face this problem every day. Nevertheless our knowledge of decision-making mechanisms is mostly limited to those of neuronal animals, and in particular vertebrates. The goal of this project is for students to explore with University of Scranton Associate Professor Jason Graham and NJIT Associate Professor Simon Garnier the navigational abilities of a non-neuronal model organism: the slime mold Physarum polycephalum. Using models of morphogenesis, the students will study (1) how external and internal stimuli modify the morphology of this giant cell as it moves through its environment and (2) how this morphological changes result in the integration of noisy and contradictory information during decision-making by P. polycephalum. The students will also compare their results to experimental data collected by Garnier’s lab as part of an IOS NSF-funded research effort. The results of this work will help understand information processing in organisms without a brain, thereby advancing our comprehension of the emergence of cognitive processes in biological systems.

Title: Modeling and Simulation of Larval Fish Swimming
Advisors: Dr. Enkeleida Lushi, Dr. Kristen Severi

The student will construct a mathematical model for the swimming dynamics of larval zebrafish as seen in the experiments. The millimeter-long swimmers have complex behavior as they move in water, and it is influenced by the fluidic interactions with other larval fish, the presence of interfaces, as well as the introduction of light or food. These various interactions can result in intricate collective motion, especially in the presence of walls. The student will implement the equations into a computer simulation and compare the results to the experimental observations.

Title: Data Augmentation for Machine Learning
Advisors: Dr. Laura Kubatko, Dr. Marilyn Vazquez

Data augmentation is the task of increasing the amount of data available without collecting new data. This is important to applications where collecting data can be costly (e.g. labor intensive, time consuming, or requiring highly specialized machinery), but more data are needed to construct accurate machine learning models. Another very important application is in patient data privacy. Being able to produce a data set with similar characteristics to one collected from human subjects would enable analyses of the data with less risk of infringing upon patient privacy. In this project, we will explore various methods for data augmentation and will apply several methods to both real and simulated data. Student participating in this project will gain experience coding in Python, but no prior experience in this area is required.

Title: Detecting Shifts in the Evolutionary Process in SARS-CoV-2 Genomes
Advisor: Dr. Laura Kubatko, Mr. Sungsik (Kevin) Kong

Description: SARS-CoV-2, the virus that causes COVID-19, is a positive-sense single-stranded RNA virus, and is one of seven known coronaviruses to cause infection in humans. The RNA sequence of SARS-CoV-2 is just under 30,000 base pairs in length, and codes for four structural proteins, called N (nucleocapsid), S (spike), E (envelope), and M (membrane). Recent work has demonstrated that mutations in the sequence encoding the spike protein have resulted in increased affinity of the virus to bind with the angiotensin converting enzyme 2 (ACE2) receptor on human cells, thus allowing the infection to establish in the human host. In this project, we’ll investigate the use of a tool called SplitSup to identify regions of the SARS-CoV-2 genome that are undergoing shifts in the evolutionary process. SplitSup is a method that uses singular value decomposition of a data matrix that encodes observed frequencies of patterns in the RNA sequence data to quantify support for hypothesized evolutionary relationships among the sequences. Preliminary work has shown that SplitSup correctly identifies the spike protein as having recently undergone an evolutionary shift.  This project will examine the more than 10,000 viral sequences in the Gisaid database to identify other genomic regions undergoing rapid evolutionary change.

Title: Modeling Local and Global Epidemics
Advisors:  Dr. Greg Rempala, Dr. Eben Kenah

Description:  The outbreak of COVID-19 has created a tremendous need for predicting both the dynamics and the size of regional COVID-19 outbreaks. Equally important is the need to determine the potential effects of early interventions such as school closures and mandatory or self-imposed quarantines. To answer these questions, we will develop a general mathematical framework for analyzing the ongoing outbreak trends using data solely from partially observed new daily infection counts (also known as the epidemic curve). The tools developed as part of this project will both help predict the rate of growth of new infections and estimate the effect of social distancing and other preventative measures on flattening the epidemic curve. We will use a new dynamical survival analysis approach to predict the trajectory of the epidemic using as an example COVID-19 data for the mid-western region of the United States. Data from elsewhere in the world, like the city of Wuhan in China, will be used to calibrate the predictions.