Fall 2019 Biomathematics Graduate Seminar
MAP 6939-03

Wednesdays, 3:35 - 4:50 pm
LOV 200

Note: Class meeting times may change when there are guest speakers. Please contact the instructor to confirm class times.

Instructor: Dr. Monica K. Hurdal

Email: mhurdal@math.fsu.edu
Webpage: www.math.fsu.edu/~mhurdal

Office Hours
Syllabus

This course is designed to be an introductory seminar for graduate students wishing to learn about the field of biomathematics, including different applications of mathematics in biology and medicine.

This class is graded S/U. Advanced graduate students will be expected to give one presentation in class. A maximum of 3 absences will be allowed in order to receive a passing grade.

Speakers:

Aug 28: Dr. Monica Hurdal: Oranizational Meeting

Sep 4: No Seminar due to Faculty Meeting

Sep 11: Dr. Monica Hurdal, FSU Math
Title: Mathematical Models of Human Brain Folding Pattern Formation & Characterization
In this presentation I will give an overview of my research involving cortical pattern formation and conformal flat maps of the human brain. I will discuss some of biological theories of cortical folding pattern formation and some of the mathematical models I am developing to help answer questions regarding cortical folding pattern development and cortical malformation diseases.

Sep 18: Dr. Nick Cogan, FSU Math
Title: Fluid/biofilm interactions: Models and experiments
Biofilms are aggregates of bacteria and secreted polymers that form on almost any surface including ship hulls, catheters, teeth and within the mucus lining the lungs. Understanding the interplay between mechanics, chemistry and biology is crucial for removing deleterious biofilms. This talk will describe a series of models aimed at including more realistic mechanics in the context of mechanical removal of a biofilm.

Sep 25: Angie Davenport, FSU Math
Title: A Software Tool for Determining Subthreshold Ionic Currents in HVC Neurons of the Zebra Finch
Authors: A Davenport, RL Hyson, F Johnson, R Bertram
The resting membrane potential of a neuron is determined primarily by subthreshold currents, including the leakage current (IL) and the hyperpolarization-activated current (Ih). These currents also dictate the response of the neuron to hyperpolarizing stimuli. In neurons of the zebra finch HVC, the brain nucleus that encodes the bird’s song, the h-current varies during development in response to song learning, and this intrinsic plasticity likely plays a role in the learning process. We have developed biophysical models to aid in the study of learning-related changes in Ih. The model contains several parameters related to Ih and IL conductance, including their maximal conductance and the activation properties of Ih. In this poster, we describe a software tool that determines these parameter values in an automated fashion, using current-clamp data from the target neuron. This tool performs a fast optimization that minimizes the difference between the model voltage trace and that of the current-clamped neuron under various conditions. Because of the rapidity of the calculation, it can be performed while the neuron remains patched, providing predictions that can be utilized on the same neuron with tools such as Dynamic Clamp. Though developed for HVC neurons, the software can be applied to any neuron type to quantify Ih and IL currents using whole-cell current-clamp data.

Oct 2: Attend Biomath Colloquim on Friday Oct 4 at 3:35 in LOV 101
Speaker on Oct 4: Dr. Santiago Schnell, U. Michigan Medical School

Oct 9: Dr. Richard Bertram, FSU Math
Title: Birds, Beta Cells, and Beats
In this seminar, I will describe three focus areas of my researcdh group. We study birds; in particular, the neural basis for song production in the male zebra finch. My group contributes to this study by developing single-cell models as well as network models. We also perform experiments with collaborators at FSU. We study beta cells; in particular, the mechanism for pulsatile activity in the insulin-secreting cells of pancreatic islets, and the mechanism for synchronization of their activity. My group contributes to this study by developing beta cell and liver cell models, and assisting in data analysis and experimental design. We study beats; in particular, the mechanism for irregular electrical impulse generation in cardiomyocytes, which provides the beats of the heart. My group contributes to this study by employing sophisticated fast-slow analysis techniques to understanding the dynamical basis of the irregular oscillations.

Oct 16: Josh Kimrey, FSU Math
Title: Arrhythmia in Cardiac Cells Due to Pesky French Ducks (canards).
Early afterdepolarizations (EADs) are pathological voltage fluctuations that can occur in cardiac cells. These fluctuations have been shown to be a major source of potentially fatal arrhythmias. In this talk, we analyze the mechanisms for the generation of these pathological fluctuations in a canonical model of cardiac cell electrical behavior (Luo-Rudy 1991). Using novel techniques from fast-slow analysis, we show that EADs are due to the presence of special solutions--called canards--which organize solution behavior in both phase and parameter space. Tracking the locations of solutions in phase space with respect to these canards allows us to explain a range of experimental results, including: (1) why the genesis of EADs depends on extracellular potassium concentrations and ion channel expression, as well as (2) why the regularity of EADs depends on heart rate.

Oct 23: Yeuran Oh, FSU Math
Title: A Computational Study of the Interactions between Pancreatic Islets and Liver Hepatocytes
Inappropriate and excessive food intake-related obesity can contribute to the increased risk of developing diabetes. Diabetes mellitus type 2 has been described as a novel global epidemic characterized by the inability of the body to effectively control blood glucose levels. The liver is a central regulator of glucose homeostasis and stores or manufactures glucose according to metabolic demands. Pancreatic beta-cells, located in the islets of Langerhans, secrete insulin in response to elevations in the blood glucose level and the insulin stimulates the liver to store glucose in the form of glycogen. Insulin secretion from pancreatic beta-cells is pulsatile, and the resulting oscillatory insulin level is important physiologically because the oscillations are impaired in patients with type 2 diabetes. We previously demonstrated that a population of islets can synchronize their oscillations through interactions with a simple liver cell model. Recently we have developed a new mathematical liver model of glucose uptake and synthesis in liver cells, including pathways of insulin signaling. With the new liver model, we will evaluate the conditions for which islet oscillations can be synchronized. These conditions will be tested using a microfluidic device by the Roper lab in the Florida State University Chemistry department.

Oct 30: Ahmet Kilinc, FSU Math
Title: A Mathematical Model of Cerebral Cortical Folding Development with Comsol Simulations
We have developed a new mathematical model of cerebral cortical folding development. The model is time-dependent, nonlinear, 2D, and therefore more biologically relevant than the previous models of cerebral cortical folding. Structural mechanics and nonlinear structural materials modules of Comsol software are used for simulations.

Nov 6: Dr. Angela Jarrett, UTexas at Austin
Title: Mathematical modeling and analyses applied to clinical and preclinical investigations of breast cancer to improve therapy regimens
One of the great challenges for cancer treatment is the inability to optimize therapy. Without a reasonable mathematical framework, our ability to select treatment regimens for the individual patient is fundamentally limited to trial and error. Presented here are examples of data-driven, integrated experimental-mathematical approaches to studying breast cancer's response to therapy for both pre-clinical and clinical investigations. The preclinical model, consisting of ODEs, connects various experiments for an in vivo mouse system to better understand the interactions of the immune response and targeted therapy for breast cancer. The clinical model is a 3D PDE system for predicting tumor response to neoadjuvant therapy using patient-specific data that lays the groundwork for optimizing chemotherapeutic dosing and scheduling. In both examples, the results of uncertainty and sensitivity analyses are discussed to show how they can be used to generate experimentally testable hypotheses, narrow the scope for experimental investigations, and evolve mathematical models. Additionally, multi-scale models are proposed that bridge the gap between in vitro and in vivo experiments to step towards clinical translation.

Nov 13: Dr. Martin Bauer, FSU Math

Nov 20: Dr. Harsh Jain, FSU Math

Nov 27: No Classes - Thanksgiving Break

Dec 4: Available, but seminars generally do not meet the last week of classes

Home Page


Copyright 2019 by Monica K. Hurdal. All rights reserved.