Archives: Winter 2023, Fall 2022, Spring 2022, 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 weitaoc@ucr.edu if you are interested in attending this seminar.

Both virtual and in-person talks will provide hybrid options for seminar participants. One might join the seminars in Skye Hall 284 or online through Zoom.

Winter 2023 

April 4, Huijing Du, University of Nebraska - Lincoln

April 12, Benedetto Piccoli, Rutgers University (Department Colloquium)

April 18, Keisuke Ishihara, University of Pittsburgh

May 2, Linh Huynh, University of Utah

May 9, Lin Li, University of Texas at El Paso

May 16, Brittany Bannish Laverty, University of Central Oklahoma

May 23, Wei (Vivian) Li, University of California, Riverside

May 30, Junyuan Lin, Loyola Marymont University

June 6, Xiaolu Guo, University of California, Los Angeles

Incoming talk:

June 6, 2023, 2:00-3:00 PM Pacific Time

Dr. Xiaolu Guo, University of California, Los Angeles

Title: Modeling the heterogenous NFκB dynamics of single immune cells  

Abstract: Macrophages function as immune sentinel cells, initiating appropriate and specialized immune responses to a great variety of pathogens. The transcription factor NFκB controls macrophage gene expression responses, and its temporal dynamic enables stimulus-specificity of these responses. Using a fluorescent reporter mouse our laboratory recently generated large amounts of single-cell NFκB dynamic data and identified dynamic features, termed ‘signaling codons’, that convey information to the nucleus about stimulus ligand and dose. Here, we aimed to recapitulate the stimulus-specific but highly cell-to-cell heterogeneous NFκB dynamics with a mathematical model of the signaling network. The parameters that are subject to biological variation provide the potential to account for heterogeneity. We estimated parameter distributions using the Stochastic Approximation Expectation Maximization (SAEM) approach and then fit the individual cell data using Bayesian maximum a posteriori (MAP) estimation. Visual inspection revealed an excellent fit with the data. To quantitatively evaluate the fitting performance, we compared the experimental and predicted distributions of NFκB signaling codons. Further, we identified biochemical reactions that may account for the cellular heterogeneity in NFκB dynamics. Our results establish a mathematical modeling tool that may be used to study the molecular determinants of response specificity and dynamical coding in immune sentinel cells at the single cell level.

Past talks:

May 30, 2023, 2:00-3:00 PM Pacific Time

Dr. Junyuan Lin, Loyola Marymount University

Title: Diffusion-based Metric for Mining Protein-Protein Interaction Networks 

with Application to the Disease Module Identification DREAM Challenge  

Abstract: In this project, we present the award-winning algorithm that ranked No.1 in the Disease Module Identification DREAM international bioinformatics challenge. The goal of the challenge was to systematically assess module identification methods on the latest molecular networks, and to discover novel network modules/pathways underlying complex diseases. We defined a new diffusion-based metric for the Protein-Protein Interaction networks to measure protein similarity based on network properties. To compute the similarity between proteins in large interaction networks, we applied a modified Algebraic Multi-grid (AMG) solver and random projection method to reduce computational complexity. Finally, we applied spectral clustering to partition the protein network into disease modules and predict their functionalities. 

May 23, 2023, 2:00-3:00 PM Pacific Time

Dr. Wei Li, University of California, Riverside

Title: Statistical methods for analyzing and comparing single-cell gene expression data

Abstract: Single-cell gene expression data provide an opportunity to characterize the molecular features of diverse cell types and states in tissue development and disease progression. However, it remains a challenge to construct a comprehensive view of single-cell transcriptomes in health and disease, due to the knowledge gap in properly modeling the high-dimensional, sparse, and noisy data. In this talk, I will introduce two statistical methods we have developed for analyzing and comparing single-cell gene expression data. The first one is an integration method which enables joint analysis of single-cell samples from different biological conditions. This method can learn coordinated gene expression patterns that are common among, or specific to, different biological conditions, and identify cellular types  across single-cell samples. I will also discuss the applicability of our method in diverse biomedical problems. The second one is a computational method for identifying, quantifying, and comparing RNA transcripts from scRNA-seq data. Accurate and sensitive profiling of RNA transcripts is of great importance in understanding the mechanisms and consequences of gene expression regulation and can have diagnostic values in clinical settings. We propose a method to address computational questions arising from this biological problem.

May 16, 2023, 2:00-3:00 PM Pacific Time

Dr. Brittany Bannish Laverty, University of Central Oklahoma

Title: A Multiscale Interdisciplinary Approach to Blood Clot Degradation  

Abstract: Blood clots are critical to prevent bleeding, but dangerous complications such as heart attack and stroke can arise when clots are not degraded effectively. A clot is composed of red blood cells and platelets held together by a mesh of fibrin fibers. The conditions in which a clot forms impact the resulting clot structure, hence the ease with which the clot is enzymatically degraded. I will present a stochastic multiscale model of clot degradation that includes structural and biochemical details from the single fiber to full clot scales. I will discuss several modifications of the model that we have used successfully to investigate the effects of fiber spacing, fiber diameter, clot composition, and enzyme concentration on clot degradation and on the effective diffusion of enzymes through the clot.  Additionally, I will highlight how mathematical modeling can be used in tandem with laboratory experimentation to yield physiological insights that were impossible with models or experiments alone. Ultimately, the goal of this work is to elucidate the mechanisms underlying clot degradation so that safer and more effective stroke treatments can be developed.

May 9, 2023, 2:00-3:00 PM Pacific Time

Dr. Lin Li, University of Texas, El Paso

Title: Developing computational approaches to study biomolecule interactions  

Abstract: Anti-mitotic drugs are highly desirable chemotherapy drugs for cancer treatment. Traditional anti-mitotic drugs destroy microtubule dynamics by depolymerizing or stabilizing microtubules, which blocks the mitosis and then kills the overactive cancer cells. Even though these anti-mitotic drugs have achieved great success in chemotherapy for cancer treatment, they face significant issues including serious side effects and strong drug resistance for some types of cancers. While microtubules provide the scaffold for mitosis, it is the interaction of kinesins with microtubule that is responsible for mitotic separation. Human kinesin-5s (Eg5) thus present ideal alternative anti-mitotic drug targets, especially for cancers that are resistant to microtubule targeting drugs. Understanding the fundamental mechanisms of Eg5 is highly demanded. Using computational physics approaches, we systematically studied the interaction between Eg5 and the microtubule. The electrostatic features indicate that the charge distribution on the motor domains of Eg5 provides complicated interactions to the microtubule. The analyses on hydrogen bonds and salt bridges demonstrate that on the binding interfaces of Eg5 and tubulin heterodimer, the salt bridge plays the most significant role in holding the complex structure. Compared with the salt bridges between Eg5 and α-tubulin interfaces, the salt bridges between Eg5 and β-tubulin have a greater number and higher occupancies. This asymmetric salt bridge distribution may play a significant role in Eg5’s directionality. The residues involved in hydrogen bonds and salt bridges are identified in this work, which may be helpful for anticancer drug design.

May 2, 2023, 2:00-3:00 PM Pacific Time

Dr. Linh Huynh, University of Utah

Title: Harnessing noise to parse out birth and death rates from population size time series data  

Abstract: Models of population dynamics are usually formulated and analyzed with net growth rates. However, separately identifying birth and death rates is significant in various biological applications such as disambiguating (1) exploitation vs. interference competition in ecology, (2) bacteriostatic vs. bactericidal antibiotics in clinical treatments, and (3) enhanced-fecundity vs. reduced-mortality mechanisms in drug resistance. In each of these three contexts, the mechanisms are different, but could be manifest in the same mean-field population size. 

In this talk, I will discuss a nonparametric method that utilizes stochastic fluctuations to extract birth and death rates from population size time series data. I will demonstrate the method on logistic growth to study density dependence, but the method can be applied to general birth-death processes and does not require a priori assumptions on the rates. I will also discuss how to implement the theory on sample data and our estimation error analysis. Time permitting, I will discuss extensions of this work to specific cell types and heterogeneous cell populations.  

The main part of this talk is joint work with Peter Thomas (Case Western Reserve University) and Jacob Scott (Cleveland Clinic) and can be found here: Inferring density-dependent population dynamics mechanisms through rate disambiguation for logistic birth-death processes | SpringerLink.  

April. 18, 2023, 2:00-3:00 PM Pacific Time

Dr. Keisuke Ishihara, University of Pittsburgh

Title: Topological control of organoid morphogenesis

Abstract: Our lab takes a synthetic approach to study how cells form tissues. “Synthetic” embodies the experimental creation of states of physical organization and gene expression that push a multicellular system to all possible extremes. The synthetic approach allows us to discover novel regulatory molecules, dormant genetic programs, and general physical principles, which we will critically evaluate as next generation strategies for organ engineering. In this seminar, we will cover our recent work 1 , in which we quantitatively captured 3D tissue morphogenesis through imaging, computation, and molecular profiling. In the future, we are interested in expanding such approach to develop genetic and chemical tools to engineer in vitro tissues such as human brain organoids and cardiac organoids.

Reference: Ishihara, K., Mukherjee, A., Gromberg, E., Brugués, J., Tanaka, E.M., Jülicher, F., 2023. Topological morphogenesis of neuroepithelial organoids. Nat. Phys. 1–7. https://doi.org/10.1038/s41567-022-01822-6

April. 12, 2023, 4:00-5:00 PM Pacific Time, Department Colloquium

Dr. Benedetto Piccoli, Rutgers University

Title: Multiagent systems and the world largest AV traffic experiment

Abstract: We will revise recent advances in modeling and control of multiagent systems. Then we will focus on traffic applications describing a recent experiment using 100 autonomous vehicles to smooth traffic and increase fuel efficiency.

April. 4, 2023, 2:00-2:50 PM Pacific Time

Dr. Huijing Du, University of Nebraska - Lincoln

Title: Multiscale modeling of complex biological systems

Abstract: Multiscale modeling is being increasingly utilized to enable more comprehensive analysis of biological processes in ways that are relevant to experiments. A new hybrid framework is developed that combines the discrete stochastic Subcellular Element Model for individual cellular activity and continuum partial differential equations of extracellular biomolecule signaling and hydrodynamical mechanism, in order to set up an approach spanning multiple levels of biophysical reality. Our findings show how a modeling approach coupling biologically relevant scales can provide new insights into the complex biological problems connected to bacterial swarming and epidermal tissue regeneration.