Archives: Spring 2021, Fall 2021

Links : UCR Interdisciplinary Center for Quantitative Modeling in Biology

UCR Math Department seminar

PDE and Applied Math Seminar

Please contact the organizer (qixuanw@ucr.edu) if you are interested in attending this seminar.

Winter 2022 Schedule

Jan. 4th Organization meeting

Jan. 11th

Jan. 18th Dr. Shawn Ryan, Cleveland State University

title: Mathematics Provides Insights Into Self-Organization in Biology

Jan. 25th Dr. Hong Qian, University of Washington

title: The Gibbs Theory of Statistical Thermodynamics Revisited with Possible Applications to Biological Data

Feb. 1st Dr. Julie Simons, California State University Maritime Academy

title: Flagellar Motion in 3D

Feb. 8th Dr. Jay Newby, University of Alberta

title: Dynamic self organization and microscale fluid properties of nucleoplasm

Feb. 15th Virtual coffee time with oncologists from the City of Hope

Feb. 22nd, Dr. Wenrui Hao, Pennsylvania State University

title: Data-driven modeling on Alzheimer's disease

Mar. 1st, Dr. Folashade B. Agusto, University of Kansas

title: Ticks and Fire: Exploring the effects of prescribed fire on tick-borne disease Abstract.

Mar. 8 Dr. Wandi Ding, Middle Tennessee State University

title: Optimal control applied to mosquito-borne diseases: Malaria and WNV

Future Talk Titles & Abstracts:

Speaker: Dr. Wandi Ding

Date: Mar. 8th, 2pm (PST)

Title: Optimal control applied to mosquito-borne diseases: Malaria and WNV

Abstract: We present some optimal control work on mosquito-borne diseases: Malaria and West Nile Virus. First, a malaria transmission model with SEIR (susceptible-exposed-infected-recovered) classes for the human population, SEI (susceptible-exposed-infected) classes for the wild mosquitoes and an additional class for sterile mosquitoes is formulated. We derive the basic reproduction number of the infection. We formulate an optimal control problem in which the goal is to minimize both the infected human populations and the cost to implement two control strategies: the release of sterile mosquitoes and the usage of insecticide-treated nets to reduce the malaria transmission. Adjoint equations are derived, and the characterization of the optimal controls are established. Finally, we quantify the effectiveness of the two interventions aimed at limiting the spread of Malaria. A combination of both strategies leads to a more rapid elimination of the wild mosquito population that can suppress Malaria transmission.

Secondly, we consider a West-Nile Virus transmission model that describes the interaction between bird and mosquito populations (eggs, larvae, adults) and the dynamics for larvicide and adulticide, with impulse controls. We derive the basic reproduction number of the infection. We reformulate the impulse control problems as nonlinear optimization problems to derive adjoint equations and establish optimality conditions. We formulate three optimal control problems which seek to balance the cost of insecticide applications (both the timing and application level) with (1) the benefit of reducing the number of mosquitoes, (2) the benefit of reducing the disease burden, or (3) the benefit of preserving the healthy bird population. Numerical simulations are provided to illustrate the results of both models.

Bio: Dr. Ding is a professor in the Department of Mathematical Sciences at Middle Tennessee State University. She serves as faculty for the Interdisciplinary Ph.D. in Computational and Data Science Program, as well as the Honors College. She is an active learner and user of SIMIODE, which is a Community of Practice dedicated to using modeling to teach differential equations, and she serves as Board of Contributing Advisors for SIMIODE (2017 – current). She serves as co-president for Association for Women in Science (AWIS) Tennessee Chapter (2021-current). Dr. Ding's research interests include mathematical biology, computational biology, optimal control, mathematical modeling, ordinary and partial differential equations, difference equations, agent/individual-based modeling, and hybrid systems with applications to population dynamics, disease modeling and control, natural resource management, and systems biology. She is also interested in deep learning. Dr. Ding's research focuses on understanding the spatial and temporal patterns that arise in dynamic biological systems and, when possible, finding the best way to control the system. She served as the editor for Society for Mathematical Biology (SMB) Digest, 2013 – 2019, and has been the editor and guest editor for multiple journals.

Past Talk Titles & Abstracts:

Speaker: Dr. Folashade B. Agusto

Date: Mar. 1st, 2pm (PST)

Title: Ticks and Fire: Exploring the effects of prescribed fire on tick-borne disease Abstract.

Abstract: Tick-borne illnesses are trending upward and are an increasing source of risks to people’s health in the United States. Thus, it is imperative to find a practical way of managing tick populations. Prescribed burns are a common form of land management, it can be cost efficient if properly managed and can be applied across large amounts of land. In this seminar, I will present a spatial stage-structured tick-host model with impulsive differential equations to investigate the effect of prescribed fire intensity, and the duration between burns on tick population and disease prevalence. Results indicate that fire intensity has a larger impact in reducing tick population than frequency between burns. Furthermore, burning at high intensity is preferable to burning at low intensity whenever possible. Exploring the use of prescribed burns in preventing the establishment of ticks into new areas shows that fewer burns are ineffective at preventing their establishment because ticks can recover relatively quickly following a burn. While frequent, long-term prescribed burns slow the propagation of ticks, their eventual establishment is inevitable, and the additional use of other tick population management strategies is necessary to prevent their establishment.

Bio: I got my PhD. in Mathematics from the University of Ilorin in Nigeria.

I am a trained applied mathematician based in the department of Ecology and Evolutionary Biology at the University of Kansas. My work focuses on designing novel models to gain insight on the emergence and re-emergence of infectious diseases of public health importance and how to mitigate the risks they pose to human health.

I have designed and analyzed novel models for diseases like Ebola, avian influenza, bovine tuberculosis, Johnes disease, toxplasmagondii, Chikungunya, and malaria. My current works are on modeling tick-borne disease across the Great Plains and understanding the role of human behavior on the transmission of COVID-19.

I am also involved in capacity building across West Africa by organizing summer schools in mathematical epidemiology and ecology. I have organized summer schools in Benin, Senegal, and Nigeria, and currently seeking funds for a school in Ghana for 2022 Summer.

Speaker: Dr. Wenrui Hao

Date: Feb. 22nd, 2pm (PST)

Title: Data-driven modeling on Alzheimer's disease.

Abstract: Alzheimer's disease (AD) affects more than 5 million people in the US. Recently, personalized treatment of AD provides a new way to manage AD patients' treatment plans. Such treatment requires a new approach to analyze the growing electronic AD brain data. In this talk, we will introduce a mathematical modeling approach to describe the progression of AD clinical biomarkers and also incorporate patient data for personalized prediction and optimal treatment. More specifically, an AD personalized prediction is provided via validating the mathematical model on a multi-institutional dataset of AD biomarkers. Personalized therapeutic simulation studies for AD patients are performed via adding optimal controls to this model.

Bio: Dr. Wenrui Hao got his Ph.D. from the University of Notre Dame in 2013 and was a postdoc researcher at Mathematical Biosciences Institute from 2013 to 2016. Since 2016, he becomes an assistant professor in the Department of Mathematics at Penn State. Dr. Hao's research focuses on computational modeling in biomedical diseases and large-scale nonlinear scientific computing.

Speaker: Dr. Jay Newby

Date: Feb. 8th, 2pm (PST)

Title: Dynamic self organization and microscale fluid properties of nucleoplasm

Abstract: The principal function of the nucleus is to facilitate storage, retrieval, and maintenance of the genetic information. A unique feature of nucleoplasm—the fluid of the nucleus—is that it contains chromatin (DNA) and RNA. In contrast to other important biological polymer hydrogels, such as mucus and extracellular matrix, the nucleic acid polymers have a sequence that encodes both genetic information and strongly influences spatial organization. How does crowding in a sequence specific hydrogel influence spatial organization of the dynamic molecular components responsible for nuclear function? We are becoming increasingly aware of the role of liquid-liquid phase separation (LLPS) in cellular processes in the nucleus and the cytoplasm. Complex molecular interactions over a wide range of timescales can cause large biopolymers (RNA, protein, etc) to phase separate from the surrounding nucleoplasm or cytoplasm into distinct biocondensates (spherical droplets in the simplest cases). I will discuss recent work modelling the role of nuclear biocondensates in neurodegenerative disease and several ongoing projects related to modelling and microscopy image analysis.

Bio: Dr. Jay Newby joined the Department of Mathematical and Statistical Sciences at the University of Alberta in the summer of 2018. He graduated from the University of Utah in 2010 with a PhD in mathematics under the supervision of Paul Bressloff. Before starting his tenure track position at the University of Alberta, he worked as a postdoc at the University of Oxford, the Mathematical Biosciences Institute at Ohio State University, and at the University of North Carolina at Chapel Hill.

Speaker: Dr. Julie Simons

Date: Feb. 1st, 2pm (PST)

Title: Flagellar Motion in 3D

Abstract: The motion of thin structures like cilia and flagella is vital for many biological systems. In this talk, we will use reproduction and sperm motility as a primary motivator for studying the motion of flagella in 3D fluid environments. Mathematically, we can model a flagellum as a curve in space and approximate the fluid environment as a Stokesian, inertialess world. Many models for flagellar motion in such settings have been developed over the span of many decades, starting with early works using 2D approximations. More recent advancements--technologically, mathematically and computationally--have allowed for exploration of motion in fully three-dimensional contexts and some surprising results. We will describe the mathematical framework for recent work involving the Method of Regularized Stokeslets and preferred curvature and then present results involving individual swimmers near surfaces, groups of swimmers, and cooperative swimmers. We hypothesize that some species of animals have developed cellular structures that enable sperm to swim faster and more efficiently, perhaps in response to sperm competition due to mating behavior.

Bio: Professor Julie Simons is an applied mathematician who specializes in problems involving cellular motility. After earning her Ph.D. from the University of Wisconsin while studying bacterial chemotaxis, she transitioned to studying problems related to reproduction and flagellar motility at Tulane University. This work is at the interface of mathematical modeling, computational simulation, fluid dynamics, and biology. Now at CSU Maritime Academy, she continues to focus on problems in fertility and cellular biomechanics, and promote undergraduate research.

Speaker: Dr. Hong Qian

Date: Jan. 25th, 2pm (PST)

Title: The Gibbs Theory of Statistical Thermodynamics Revisited with Possible Applications to Biological Data

Abstract: If we consider entropy as a potential function, then its derivatives w.r.t. independent variables are entropic forces: temperature, pressure, and chemical potentials are all entropic forces. When two systems reach a thermodynamic equilibrium, their entropic forces are balanced, e.g., equal temperature, pressure, and chemical potential. This equal conjugate variables is actually a consequence of "equal treatment of data": When we add X1 to X2, we made an assumption to give them an "equal treatment". This yields their conjugate variables being equal. Therefore, the concept of thermodynamic equilibrium is in fact a fundamental theorem of data science. Applications of these results to data from single cells will be discussed.

Bio: Professor Hong Qian is Olga Jung Wan Endowed Professor of Applied Mathematics at University of Washington, Seattle. He received his B.A. in Astrophysics from Peking University and Ph.D. in Biochemistry from Washington University in St. Louis, and worked as postdoctoral researcher at University of Oregon and Caltech on biophysical chemistry and mathematical biology. He was elected a fellow of the American Physical Society in 2010. Professor Qian's main research interest is the mathematical narratives of biological systems, especially in terms of stochastic mathematics and nonequilibrium statistical thermodynamics. His recent book “Stochastic Chemical Reaction Systems in Biology” (2021), coauthored with H. Ge, has just been published by Springer.

Slides: [link]

Speaker: Dr. Shawn Ryan

Date: Jan. 18th, 2pm (PST)

Title: Mathematics Provides Insights Into Self-Organization in Biology

Abstract: In this talk we will consider how mathematical modeling, analysis, and simulation can be used to provide new insight into biological phenomena. In particular, we focus on the self- organization of large-scale groups of insects and swimming bacteria. This talk will show how simple models for active biosystems can address complex ecological problems as well as lead to the development of novel biomaterials. What makes these problems interesting is that individual interactions at the microscale lead to the onset of mesoscale and then macroscale patterns. In addition, when animals exhibit collective behavior one can observe remarkable properties such as enhanced movement speed, pattern formation, and increased mixing. Mathematics provides a deep understanding of how and why these properties emerge and is fundamental to pressing biological problems.

Short Bio: Dr. Shawn Ryan’s research focuses on using mathematical modeling, analysis, and simulation to gain greater understanding about problems in Biology, Physics, and Materials Science. He is concerned with developing novel differential equations based models and numerical approaches focused on using interactions at the microscale to study the onset of macroscopic behavior in active biosystems. Shawn graduated with his Ph.D. in Mathematics from Pennsylvania State University under the direction of Leonid Berlyand. He then went on to a postdoc with a dual appointment at the Liquid Crystal Institute and Department of Mathematical Sciences at Kent State University. Shawn joined the faculty at Cleveland State University in Fall 2016 and was recently promoted to Associate Professor and is currently serving as the co-Director of the Center for Applied Data Analysis and Modeling (ADAM).