SMB2023 FollowMonday during the "CT01" time block. Room assignment: Suzanne M. Scharer Room (##3146) in The Ohio Union.
University of Michigan
"Mathematical Modeling of Circadian Rhythms Across Populations with Consumer-Grade Wearable Data"
With the rise of wearable technology in recent years, large-scale collections of physiological and behavioral data have become readily available for analysis. Mathematical models can effectively process these increasingly accessible signals, such as heart rate and activity, into information about the circadian rhythm of an individual. One subset of these models predicts circadian phase (often via a limit-cycle oscillator framework) based on the light and/or activity pattern of an individual, while another recently developed method extracts heart rate phase and other information by accounting for circadian variation, the impact of activity, and effects due to other physiological processes. In this talk, we adapt these modeling approaches to examine circadian features across populations, including students, cancer patients, and individuals with COVID-19. We discuss large-scale differences across populations and implications of the models for reducing fatigue and tracking disease progression, using a combination of synthetic and real data in order to test algorithm performance. We develop a real-time anomaly detection framework for identifying abnormal changes such as fever in oscillatory physiological signals. Finally, we explore parameter differences across populations and the effects of parameter perturbations on the models.
Additional authors: Daniel B Forger, University of Michigan; Sung Won Choi, University of Michigan; Muneesh Tewari, University of Michigan; Yitong Huang, Northwestern; Dae Wook Kim, University of Michigan; Kevin Hannay, Arcascope; Olivia Walch, Arcascope
Edward J Hancock
The University of Sydney
"Modelling the Synchronization of the Membrane and Calcium Clocks in Lymphatic Muscle Cells"
Lymphoedema is a common dysfunction of the lymphatic system that results in fluid accumulating between cells. Fluid return through the lymphatic system is primarily provided by contractions of muscle cells in the walls of lymphatic vessels, driven by oscillations in the cell’s membrane potential (M-clock) and calcium ion concentration (C-clock). However, there is an incomplete understanding of the mechanisms involved in these oscillations, restricting the development of pharmacological treatments for dysfunction. Previously, we proposed a minimal model where self-driven oscillations in M-clock drove passive oscillations in the C-clock. Here, we extend this model to the case where the M-clock and the C-clock oscillators are both self-driven and coupled together. The synchronized behaviour enables the model to match experimental M-clock waveforms for both wild-type and knock-out data, resolving issues with previous models. We also use phase-plane analysis to explain the dual-clock coupling. The model has the potential to help determine mechanisms and find targets for pharmacological treatment of lymphoedema.
Additional authors: C.D. Bertram C. Macaskill
Georgia State University
"Mathematical modeling of cell growth in a tissue engineering scaffold pore"
A tissue engineering scaffold is composed of pores lined with cells that allow nutrient-rich culture medium to pass through, thereby encouraging the proliferation of cells. Growth of the tissue is affected by a number of factors, including the flow rate and concentration of nutrients in the feed, the elasticity of the scaffold, and the properties of the cells. Many studies have examined these factors separately, but in this project, we aim to examine all of them together. In this work, we (i) develop a mathematical model that describes the dynamics and concentration of nutrients, scaffold elasticity, and cell proliferation; (ii) solve the model and simulate the cell proliferation process; (iii) develop a reverse algorithm that determines the initial scaffold pore configuration based on the desired geometry of the final tissue.
Additional authors: Dr. Pejman Sanaei, Georgia State University
"Classify dynamic responses to food through time-series glucose data"
Monitoring of health status is complex, and the common approach focuses on one-time measurement of clinical markers during a visit with a healthcare professional. This is not ideal as the physiological states are proven to change dynamically in response to daily activities (e.g., eating and exercise). For type 2 diabetes and prediabetes patients, continuous glucose monitoring (CGM), as well as monitoring of physical activities and sleep provide important information for individualized intervention suggestions. Here, we incorporated the novel and noisy real-life CGM measurements to evaluate the personal responses to 7 different types of carbohydrates and 3 different nutrients (fat, protein, and fiber) that tend to mitigate glucose spikes individually in 43 participants. We built a computational workflow to clean the data, removed errors from manual records and time zone shifts in traveling, and provided quality control for future data. We then viewed the data through smoothing, dimensionality reduction, and clustering. We discovered consistent separating patterns from common carbohydrates, with rice and beans being the two extremes. We discovered interesting individual responses to the additional mitigators, which indicate the importance of personalized intervention in diabetes.
Additional authors: Ben W. Ehlert1; Dalia Perelman1,2; Ahmed A. Metwally1,3; Alessandra Celli1; Caroline Bejikian1; Heyjun Park1; Tracey McLaughlin2,1; Michael P. Snyder1 1 Department of Genetics, Stanford University, Stanford, CA 94305, USA 2 Department of Medicine, Stanford University, Stanford, CA 94305, USA 3 Google, Mountain View, CA 94043, USA