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.
Spring 2025
Sep 30, 2:00 PM (Zoom): Organizational meeting
Oct 07, 2:00 PM (Skye 268): Dr. Stephen Williams (University of California, Merced)
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 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.
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.