ICQMB Center Seminar Fall 2025
Tuesday 2:00-3:20 pm PT
Organizers : Mark Alber / Jia Gou
Past Organizers : Qixuan Wang / Weitao Chen / Heyrim Cho / Jia Gou / Mykhailo Potomkin / Yiwei Wang
Tuesday 2:00-3:20 pm PT
Organizers : Mark Alber / Jia Gou
Past Organizers : Qixuan Wang / Weitao Chen / Heyrim Cho / Jia Gou / Mykhailo Potomkin / Yiwei Wang
The format of the seminar is hybrid. One might join the seminars in Skye Hall 268 or through zoom link. Please contact Dr. Jia Gou (jia.gou@ucr.edu) for the zoom link.
Fall 2025
Sep 30, 2:00 PM (Zoom): Organizational meeting
Oct 07, 2:00 PM (Skye 268): Dr. Stephen Williams (University of California, Merced)
Oct 08, 2:00 PM (MRB1, 1110): Dr. Jeremiah Zartman (University of Notre Dame)
Oct 15 (Wednesday), 10:00 AM (Skye 268): Dr. Seirin Lee (Kyoto University)
Oct 21, 2:00 PM (Skye 268): Dr. Alfonso Landeros (UC Riverside)
Oct 28, 2:00 PM (Zoom): Dr. David Lipshutz (Baylor College of Medicine)
Nov 04, 2:00 PM (Zoom): Dr. Felix Zhou (UT Southwestern Medical Center)
Nov 11, 2:00 PM: Campus Close
Nov 18, 2:00 PM (Zoom): Dr. Gordon McNicol (University of Waterloo)
Nov 25, 2:00 PM (Zoom): Dr. Chunmei Wang (University of Florida)
Dec 02, 2:00 PM (Zoom): Dr. Margherita De Marzio (Harvard Medical School and Brigham and Women’s Hospital)
Upcoming talks:
Oct 21, 2025, 02:00 PM - 03:00 PM Pacific Time
Dr. Alfonso Landeros, UC Riverside
Title: A modified tau-leaping method for gene regulatory networks
Abstract: Stochastic modeling is valuable in investigating rare phenomena, with simulation in particular serving as a computationally expensive yet accessible tool. Developments in the past several decades have accelerated simulation algorithms by reducing the cost per simulation step, in the case of exact methods, or by characterizing and addressing stiffness in the stochastic setting, in the case of approximate methods. However, existing algorithms are ill-equipped to deal with certain motifs in gene regulatory networks. Motivated by a gene regulatory model used to study breast cancer heterogeneity, this talk presents a modified tau-leaping method accelerating simulations that model molecular states. Applications extend beyond transcription factor binding to models involving other ligand binding motifs, post-translational modification of proteins, and synthesis and degradation, all possibly taking place away from equilibrium.
Bio: Dr. Alfonso Landeros is currently an Assistant Professor in the Department of Statistics at UCR. Prior to joining UCR in 2023, he was a postdoctoral researcher at UCLA in Computational Medicine. He also completed his PhD in Biomathematics at UCLA. His research seeks to apply mathematical modeling and optimization to problems in statistics, data science, and the life sciences.
Nov 04, 2025, 02:00 PM - 03:00 PM Pacific Time
Dr. Felix Zhou, UT Southwestern Medical Center
Title: Cell Morphological Control of Signaling in Cancer
Abstract: Form is function. Just as Darwin’s finches have beaks adapted to their ecological niche, so too has cell morphology adapted to the cell’s function and its local microenvironment. Consequently, cell shape changes are prominently used as phenotypic biomarkers of disease and an aggregate downstream readout of a cell’s molecular processes. However, high-resolution microscopy combined with advanced image analysis increasingly reveal a more complex interplay whereby cell shape plays a pivotal role in spatially organizing, and translating molecular signals to cell behavior. For example, we recently discovered a previously undescribed role of blebbing (local, dynamic, hemispherical surface protrusions) in melanoma cells to promote membrane recruitment of curvature-sensitive scaffolding Septin polymers, to amplify pro-survival signaling, enabling the cells to bypass anoikis, the normal checkpoint program of programmed cell death – a key prerequisite step for cancer metastasis. Systematic causal investigation of shape and signaling is notoriously difficult experimentally whereby perturbation may destroy the intricate feedback between the two. In this talk, I will present the development of alternative non-perturbation computational approaches, inspired by statistical inference techniques used in financial mathematics, neuroscience and ecology
to quantify causal relationships between local 3D cell surface morphology and membrane-associated signals in a spatiotemporal manner. I will show applications to high-resoolution microscopy videos of cell blebbing, cancer-immune cell interactions, and organoids.
Bio: I did my undergraduate at the University of Cambridge, UK, reading Engineering and getting drilled in the classical disciplines of mechanics, fluids and electronics. In my third year, I undertook a summer research fellowship at the California Institute of Technology in the lab of Erik Winfree where I was first introduced to DNA origami and biology. I enjoyed this so much that I decided to do a PhD at the Unversity of Oxford, UK in the Life Sciences Interface Doctoral Training Program. Here, I joined the lab of Xin Lu at the Ludwig Cancer Institute, with the intent initially of being a wetlab scientist working with organoids. Somehow, I ended up working on developing image analyses for microscopy and endoscopy imaging with Jens Rittscher, who was just starting his lab, and never looked back. In 2021 I moved to the USA just after COVID, joining the lab of Gaudenz Danuser at UT Southwestern Medical Center, USA as a postdoctoral fellow to pursue my research interest of developing causal inference techniques to understand how cell morphology can spatially organize molecular signals to affect cell fate in cancer.
Nov 18, 2025, 02:00 PM - 03:00 PM Pacific Time
Dr. Gordon McNicol, University of Waterloo
Title: Development and stability of cellular mechanosensing structures
Abstract: Cells respond to their local environment through mechanotransduction, converting mechanical signals into a biological response. Crucially, mechanical and biochemical perturbations initiate cell signalling cascades, which can induce responses such as growth, apoptosis, proliferation and differentiation. The cell cytoskeleton, particularly actomyosin stress fibres (SFs), and focal adhesions (FAs), which bind the cytoskeleton to the extracellular matrix (ECM), are central to this process, serving as mechanosensing structures that activate intracellular signalling in response to deformation. We present a novel bio-chemo-mechanical continuum model to describe the coupled formation and maturation of ventral SFs and FAs. We use a set of reaction–diffusion–advection equations to describe three sets of biochemical events: the polymerisation of actin and subsequent bundling into activated SFs; the formation and maturation of cell–substrate adhesions; and the activation of signalling proteins in response to FA and SF formation. The evolution of these key proteins is coupled to a Kelvin–Voigt viscoelastic description of the cell cytoplasm and the ECM, with additional mechanical contributions from the stiff nucleus and elastic plasma membrane. We employ this model to investigate how cells respond to external and intracellular cues in vitro. It reproduces several experimentally observed phenomena, including non-uniform cell striation and the formation of weaker SFs and FAs on softer substrates. Beyond this, we examine how ligand patterning, cell–cell communication through the ECM, and targeted inhibitors influence cytoskeletal and adhesion stability. Overall, our framework provides a predictive platform for linking cell mechanics and signalling, offering new insight into how cells regulate their structure and function in response to environmental and chemical cues.
Bio: Dr. Gordon McNicol is a Postdoctoral Fellow at the University of Waterloo. He earned his PhD in Mathematics from the University of Glasgow in 2024, where he also completed an MSci degree. His research applies mathematical modelling to study physiological, biological, and environmental systems, with a focus on revealing the mechanisms underlying their behaviour. His recent work includes theoretical fluid mechanics, mechanochemical models of cellular mechanotransduction, and models of greenhouse gas emissions from wetlands.
Previous talks:
Oct 07, 2025, 02:00 PM - 03:00 PM Pacific Time
Dr. Stephen Williams, University of California, Merced
Title: Identifiability, Sensitivity, and Genetic Algorithms in Bacterial Biofilm Selection Models
Abstract: Bacteria often develop distinct phenotypes to adapt to environmental stress. In particular, they can produce biofilms, dense communities of bacteria that live in a complex extracellular matrix. Bacterial biofilms provide a safe haven from environmental conditions by distributing metabolic workload and allowing them to perform complex multicellular processes. While previous studies have investigated how bacterial biofilms are regulated under laboratory conditions, they have not considered (1) the data requirements necessary to estimate model parameters and (2) how bacteria respond to recurring stressors in their natural habitats. To address (1), we adapted a mechanistic population model to explore the dynamics of biofilm formation in the presence of predator stress, using synthetic data. We used a Maximum Likelihood Estimation framework to measure crucial parameters underpinning the biofilm formation dynamics. We used genetic algorithms to propose an optimal data collection schedule that minimised parameter identifiability confidence interval widths. Our sensitivity analysis revealed that we could simplify the binding dynamics and eliminate biofilm detachment. To address (2), we proposed a structured version of our model to capture the long-term behaviour and evolutionary selection. In our extended model, the subpopulations feature different maximal rates of biofilm formation. We compared the selection under different predator types and amounts and identified key parameters that affected the speed of selection via sensitivity analysis.
Bio: Originally from the UK, I completed my PhD in Physics at the University of Warwick. I am currently a Postdoctoral Researcher in the Applied Mathematics Department at the University of California, Merced. I work as part of the Institute for Symbiotic Interactions, Training and Education in the face of Changing Climates (INSITE). In my research, I apply mechanistic models to try to understand the factors central to biological systems’ response to external stress. Additionally, I’m interested in how these models can be analysed through the lens of Optimal Experimental Design, Sensitivity Analysis, and Identifiability, to enhance our understanding of the underlying system further.
Oct 15, 2025, 10:00 AM - 11:00 AM Pacific Time
Dr. Sungrim Seirin-Lee, Kyoto University
Title: Pattern Formation in Skin Diseases and Its Application to Personalized Treatment in Dermatology
Abstract: Skin diseases typically appear as visible information-skin eruptions distributed across the body. However, the biological mechanisms underlying these manifestations are often inferred from fragmented, time-point-specific data such as skin biopsies. The challenge is further compounded for human-specific conditions like urticaria, where animal models are ineffective, leaving researchers to rely heavily on in vitro experiments and sparse clinical observations. To overcome the current limitations in understanding the pathophysiology of skin diseases, we propose a novel framework that connects the visible morphology of skin eruptions with the underlying pathophysiological dynamics in vivo, using a multidisciplinary approach that integrates mathematical modeling, in vitro experiments, clinical data, and data science. Furthermore, we will introduce an innovative methodology that combines mathematical modeling with topological data analysis and machine learning, allowing for the estimation of patient-specific parameters directly from morphological patterns of skin eruptions. This framework offers a new pathway for personalized analysis and mechanistic insight into complex skin disorders.
Bio: Prof. Seirin-Lee is a Professor of Mathematical Medicine at the Kyoto University Institute for Advanced Study (KUIAS) and the Graduate School of Medicine, Kyoto University. She obtained her PhD from Okayama University, Japan in 2010, having conducted part of her doctoral research at the Center for Mathematical Biology, University of Oxford, as a JSPS DC1 fellow. After postdoctoral training at the University of Tokyo and RIKEN, she was appointed Assistant Professor in 2014, Associate Professor in 2017, and Full Professor in 2020 at Hiroshima University. Since 2021, she has held her current position at Kyoto University. Her research focuses on pattern formation, mathematical medicine, mathematical dermatology, spatial immunology, and applied mathematics.