Archives: Winter 2022, 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 (heyrim.cho@ucr.edu) if you are interested in attending this seminar.

Spring 2022 Schedule

March. 29th Organization meeting

Apr. 5th Dr. David Hormuth, University of Texas at Austin

Apr. 12th Dr. Jianhua Xing, University of Pittsburgh

Apr. 19th Dr. Geoffrey Schiebinger, University of British Columbia

Apr. 26th Dr. Naveen Vaidya, San Diego State University

May 3rd Dr. Wanda Strychalski, Case Western Reserve University

May 10th Dr. Zixuan Cang, North Carolina State University

May 17th Dr. Katarzyna (Kasia) Rejniak, H. Lee Moffitt Cancer Center & Research Institute

May 24th Dr. Nancy Rodriguez, University of Colorado Boulder

May 31st Dr. James Greene, Clarkson University

Future Talk Titles & Abstracts:

Speaker: Dr. James Greene, Clarkson University

Date: May 31st, (Tue), 2 - 3:20pm (PST)

Title: Mathematical models and control strategies related to the COVID-19 pandemic and infectious diseases generally

Abstract: Since the onset of the COVID-19 pandemic, there has been much scientific interest in the ability of mathematical models to both predict disease dynamics, as well as their use in designing intervention strategies that can mitigate disease burden on medical infrastructure, reduce transmission, minimize negative economic and psychological impacts, etc. In this talk, we will present a number of recent modeling projects which address different questions of interest related to the COVID-19 pandemic and infectious diseases generally. Specifically, we will discuss early novel models of the spread of COVID-19, which capture both the effect of asymptomatic transmission and social distancing via explicit compartments. We will then discuss the role of non-pharmaceutical interventions in both reducing peak infection numbers ("flattening the curve") while simultaneously minimizing time spent in strict lockdowns; general optimal design strategies can be numerically seen to exist throughout a large class of epidemic models, which we show to be rigorously justified in the SIR model. Opening/closing strategies in schools/universities will also be studied, where we analyze robust feedback laws which maximize in-person instruction while keeping infections below a critical threshold. Furthermore, as mutations lead to new viral strains, such as the Omicron and Delta variants, important questions related to evolutionary fitness/competition exist, such as the effect of selection with respect to infectivity vs. disease severity. Again utilizing relatively simple mathematical models, we study the impact of selection on mutant variants, and characterize necessary parameter changes yielding a fitness advantage. We note that in almost all of the scientific questions of interest addressed here, transient disease dynamics are of fundamental importance, which will be a theme throughout this talk.

Bio: James Greene has been an Assistant Professor in the Mathematics Department at Clarkson University since 2019. He received his Ph.D in Mathematics from the University of Maryland, College Park, and subsequently held a Hill Assistant Professorship position in the Mathematics Department at Rutgers University, New Brunswick. His work is broadly related to systems and mathematical biology, and he works on problems relating to cancer evolution, drug resistance, transcription/translation dynamics, enzymatic circuits, and epidemiology. He is also the Co-Director of a new (as of 2022) REU site at Clarkson University, which focuses on introducing undergraduate students from both biology and mathematics to interdisciplinary research in mathematical biology.

Past Talk Titles & Abstracts:

Speaker: Dr. David A. Hormuth (University of Texas at Austin)

Date: Apr. 5th (Tue), 2 - 3:20pm (PST)

Title: Towards imaging-informed modeling of radiotherapy response in brain tumors

Abstract: There is a long history of developing phenomenological and mechanistic mathematical models of tumor growth and response. While these models can be used to generate testable hypothesis or investigate a host of biological scenarios, it is not until recently that these models have had the potential to be personalized for individual subjects. A limitation to existing models is that they often contain model parameters which are impossible or impractical to determine for individual patients. Through non-invasive quantitative magnetic resonance imaging (MRI) methods, such as dynamic contrast enhanced MRI and diffusion weighted MRI, we are now able to quantify (in 3D) tumor properties such as perfusion, vascularity, proliferation, and cellularity for individual tumors.

These measurables can be used to initialize and calibrate models of tumor growth and response. In this talk, I will present an application of using quantitative MRI data to inform a clinical model of tumor growth and response to radiotherapy. For this scenario, we will evaluate the accuracy of tumor growth predictions at the voxel and global levels. With further development, we hypothesize that these subject-specific modeling techniques could deliver the opportunity to predict patient response early in the course of therapy, simulate patient-specific treatment regimens, and eventually optimize or adapt therapy for individual tumors.

Bio: David A. Hormuth, II Ph.D. is a Research Associate in the Center for Computational Oncology in the Oden Institute for Computational Engineering & Sciences at the University of Texas at Austin. He received his Ph.D. from Vanderbilt University and was a cancer imaging trainee in the Vanderbilt University Institute for Imaging Science. His current research interests are the integration of medical imaging data with mathematical and computational techniques to predict response to radiotherapy, optimize therapeutic regimens, simulating radiopharmaceutical delivery, and translating pre-clinical efforts to clinical setting.

Speaker: Dr. Jianhua Xing, University of Pittsburgh, USA

Date: Apr. 12th (Tue), 2 - 3:20pm (PST)

Title: Reconstructing cell phenotypic transition dynamics from single cell data

Abstract: Recent advances in single-cell techniques catalyze an emerging field of studying how cells convert from one phenotype to another, i.e., cell phenotypic transitions (CPTs). Two grand technical challenges, however, impede further development of the field. Fixed cell-based approaches can provide snapshots of high-dimensional expression profiles but have fundamental limits on revealing temporal information, and fluorescence-based live cell imaging approaches provide temporal information but are technically challenging for multiplex long- term imaging.

My lab is tackling these grand challenges from two directions, with the ultimate goal of integrating the two directions to reconstruct the spatial-temporal dynamics of CPTs. In one direction, we developed a live-cell imaging platform that tracks cellular status change in a composite multi-dimensional cell feature space that include cell morphological and texture features readily through fluorescent and transmission light imaging. We applied the framework to study human A549 cells undergoing TGF-β induced epithelial-to-mesenchymal transition (EMT)^1,2. In another direction, we aim at reconstructing single cell dynamics and governing equations from single cell genomics data³. We developed a procedure of learning the analytical form of the vector field F(x) and the equation dx/dt = F(x) in the Reproducing Kernel Hilbert Space. Further differential geometry analysis on the vector field reveals rich information on gene regulations and dynamics of various CPT processes.

Bio: Dr Xing received B.S. in Chemistry from Peking University, M.S. in Chemical Physics from University of Minnesota, and PhD in Theoretical Chemistry from UC Berkeley. After being a postdoc researcher in theoretical biophysics at UC Berkeley and an independent fellow at Lawrence Livermore National Laboratory, he assumed his first faculty position at Virginia Tech, then moved to University of Pittsburgh in 2015. Currently Dr Xing is a professor in the Computational and Systems Biology Department, School of Medicine, and an affiliated faculty member of Department of Physics and Astronomy, University of Pittsburgh. He is also an affiliated member of University of Pittsburgh Hillman Cancer Center. Dr Xing’s research uses statistical and chemical physics, dynamical systems theory, mathematical/computational modeling in combination with quantitative measurements to study the dynamics and mechanics of biological processes. Recently his lab focuses on reconstructing cell phenotypic transition dynamics from live cell time-lapse images and snapshot high-throughput single cell data. Another related direction is to study how three-dimensional chromosome structure and dynamics, epigenetic modification, and gene regulation are coupled.

Speaker: Dr. Geoffrey Schiebinger, University of British Columbia

Date: Apr. 19th (Tue), 2 - 3:20pm (PST)

Title: Towards a mathematical theory of development

Abstract: New measurement technologies like single-cell RNA sequencing are bringing 'big data' to biology. One of the most exciting prospects associated with this new trove of data is the possibility of studying temporal processes, such as differentiation and development. In this talk, we introduce the basic elements of a mathematical theory to answer questions like How does a stem cell transform into a muscle cell, a skin cell, or a neuron? How can we reprogram a skin cell into a neuron? We model a developing population of cells with a curve in the space of probability distributions on a high-dimensional gene expression space. We design algorithms to recover these curves from samples at various time-points and we collaborate closely with experimentalists to test these ideas on real data.

Bio: Geoffrey Schiebinger received his PhD in Statistics from UC Berkeley, where he was supervised by Ben Recht and also worked with Martin Wainwright, Bin Yu and Aditya Guntuboyina. He did his postdoctoral studies with Eric Lander, Aviv Regev and Philippe Rigollet at the Broad Institute and the MIT Center for Statistics and Data Science. He is now an Assistant Professor of Mathematics and an Associate Member of Biomedical Engineering at the University of British Columbia. He has won numerous awards including the 2021 Maud Menten New Principal Investigator Prize in Genetics, a Career Award at the Scientific Interface from the Burroughs Wellcome Fund, and Best Contribution to the 2017 conference on Statistical Challenges in Single Cell Analysis.

Speaker: Dr. Naveen Vaidya, San Diego State University

Date: Apr. 26th (Tue), 2 - 3:20pm (PST)

Location: Skye 361 (hybrid)

Title: Within-Host Modeling of Human Viruses: Looking into Animal Models is Precious

Abstract: The use of animal models is quite common in studying human viruses. Specifically, the pre-clinical development of antiviral agents, such as antiretroviral therapy and vaccines, involves experimental trials in animals. In this talk, I will present mathematical models driven by animal data to describe the within-host dynamics of several Human virus infections (SARS-CoV-2, HIV, Hepatitis B). Using the developed models, I will demonstrate how data sets from controlled animal experiments can be precious to get insight into viral and immune response dynamics. We will further extend the models to evaluate antiviral agents and grasp critical epidemiological characteristics of these viral diseases.

Bio: Dr. Naveen K. Vaidya is an Associate Professor of Mathematics at San Diego State University (SDSU). Before joining SDSU, he had obtained experience in an assistant professorship at the University of Missouri – Kansas City and postdoctoral research at Los Alamos National Laboratory, New Mexico, and Western University, Canada. He received PhD in Applied Mathematics and M.Sc. in Industrial and Applied Mathematics from York University, Canada. He has also received M.Sc., B.Sc., and B.Ed. Degrees from Tribhuvan University, Nepal. Dr. Vaidya’s research interests include applied mathematics, with specific areas of interest in mathematical biology (viral dynamics and immune systems, epidemiology, and ecology), mathematical and computational modeling, differential equations, dynamical systems, optimal control, biostatistics, and machine learning. His primary current focus lies in developing models of infectious diseases, pathogen evolution, and their controls. He has established an externally funded research program in SDSU-DiMoLab (Disease Modeling Lab) at San Diego State University.

Speaker: Dr. Wanda Strychalski, Case Western Reserve University

Date: May 3rd (Tue), 2 - 3:20pm (PST)

Title: Going with the flow: cytoplasmic streaming during 3D cell migration

Abstract: Cell migration is critical for many vital processes, such as wound healing, as well as harmful processes, like cancer metastasis. Recent experiments of cells migrating in 3D fibrous matrices reminiscent of biological tissue show some cells experience large shape changes and are thought to utilize cytoplasmic streaming and intracellular pressure in generating leading edge protrusions. It has been hypothesized that adhesion of the cell to its external environment does not play an important role in this type of motility in contrast to cell migration on flat surfaces. It is not well understood how cells generate forces to facilitate migration in using this amoeboid mode. In this talk, dynamic computational models of single-cell migration are presented in a fibrous extracellular matrix and in a confined channel. The models are formulated using the method of regularized Stokeslets to simulate fluid-structure interactions. Results show the non-trivial relationship between cell rheology and its external environment during migration with cytoplasmic streaming.

Bio: Wanda Strychalski is an associate professor at Case Western Reserve University in the Department of Mathematics, Applied Mathematics, and Statistics. She received her Ph.D. in applied mathematics in 2009 from the University of North Carolina at Chapel Hill and previously held an appointment as a Krener assistant professor at the University of California, Davis. She studies the role fluid mechanics in cellular processes and also develops numerical methods for fluid-structure interaction.

Speaker: Dr. Zixuan Cang, North Carolina State University

Date: May 10th (Tue), 2 - 3:20pm (PST)

Title: Optimal transport for single-cell omics data analysis

Abstract: The recent development of high-resolution omics level technologies has reshaped modern biological research. These high-dimensional and noisy datasets are accumulating at a fast pace. Efficient and biologically meaningful algorithms are needed to extract biological insights from these raw datasets. In this talk, I will discuss using optimal transport, a powerful geometric data analysis method, to integrate multimodal omics datasets and infer cell-cell communications, a crucial process that drives the correct developments and functions of biological systems. I will also talk about a new formulation of optimal transport called supervised optimal transport inspired by these biological applications.

Bio: Zixuan Cang is an assistant professor in the department of mathematics at North Carolina State University. His research focuses on 1) utilizing mathematical tools such as topological data analysis and optimal transport paired with machine learning to extract biological insights from data such as spatial transcriptomics and 2) developing novel mathematical methods motivated by these applications. He is also interested in developing data-driven models to further study certain biological systems in detail.

Speaker: Dr. Katarzyna (Kasia) Rejniak, H. Lee Moffitt Cancer Center & Research Institute

Date: May 17th (Tue), 2 - 3:20pm (PST)

Title: Micro-pharmacology: modeling the tissue barriers in drug delivery

Abstract: Systemic chemotherapy is one of the main anticancer treatments used for most kinds of clinically diagnosed tumors. However, the efficacy of these drugs can be hampered by the physical attributes of the tumor tissue, such as irregular vasculature, specific cellular and ECM architecture, metabolic gradients, or non-uniform expression of the cell membrane receptors. This can prevent therapeutic agents to reach tumor cells in quantities sufficient to exert the desired effect. To examine ways to improve drug delivery on a cell-to- tissue scale (single-cell pharmacology), we developed the micropharamcology computational framework that helps to design optimal combination drug schedules, optimal drug properties, or investigate the development of drug-induced resistance.

Bio: Kasia gained her MSc degree in Applied Mathematics and Computer Science from the University of Gdansk in Poland, and PhD degree from Tulane University in New Orleans, USA. She held postdoctoral positions at the Mathematical Biosciences Institute in Ohio, and University of Dundee in Scotland. She is currently Associate Member at the Integrated Mathematical Oncology Department at the Moffitt Cancer Center in Florida. Her main research interests involve use of computational and mathematical methods to study drug and metabolites delivery to tumor tissues, the impact of the tumor microenvironmenton on its response to therapies, and optimization of anti-cancer treatment schedules using histological data. She developed a suit of hybrid agent-based and fluid structure-interaction (microPKPD, MultiCell-LF, IBCell) models.

Speaker: Dr. Nancy Rodriguez, University of Colorado Boulder

Date: May 24th (Tue), 2 - 3:20pm (PST)

Title: Partial Differential Equation models in Ecological and Sociological Phenomena.

Abstract: In this talk I will discuss how we can use partial differential equation models to gain insight into complex social, ecological, and biological phenomena. We will see how we can use the framework of partial differential equations to encompass many types of phenomena when we are interested in studying global structures, such as the dynamics of a population versus an individual. We will look at applications in urban crime, social outburst of activity, territory formations in ecology. We explore various important mathematical questions from the point of view of the applications and discuss the limitation of our framework.

Bio: I am an Assistant Professor in applied mathematics at the University of Colorado, Boulder. I received my Ph.D. from UCLA under the guidance of Andrea Bertozzi. I was an NSF postdoctoral fellow at Stanford University from 2011-2014. My research focuses on nonlinear partial differential equations (PDEs), in particular those with applications to urban crime, segregation, biological aggregation, chemotaxis, and ecology. Fundamentally, I am interested in the mathematical modeling and the use of numerical and mathematical analysis to shed light into social, biological, and ecological systems. I have contributed to the advancement of the theory for non-local PDEs and have brought insight into crime propagation and prevention, social segregation, and pest-control. I love biking, hiking, skiing, and anything that allows me to be outside.

Speaker: Dr.

Date: 2pm (PST)

Title:

Abstract:

Bio: