ICQMB Center Seminar Fall 2024
Tuesday 2:00-3:20 pm PT
Organizers : Mark Alber / Qixuan Wang
Past Organizers : Qixuan Wang / Weitao Chen / Heyrim Cho / Jia Gou / Mykhailo Potomkin / Yiwei Wang
Tuesday 2:00-3:20 pm PT
Organizers : Mark Alber / Qixuan Wang
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 361 or through zoom link. Please contact Dr. Qixuan Wang (qixuanw@ucr.edu) for the zoom link.
Spring 2025
Apr 1, 2:00 PM: No seminar
Apr 8, 2:00 PM: TBA
Apr 15, 2:00 PM: Dr. Meisam Zaferani (Princeton University)
Apr 22, 2:00 PM: Dr. Tianchi Xin (Yale School of Medicine)
Apr 29, 2:00 PM: No seminar
May 6, 2:00 PM: Dr. Derek Moulton (Oxford)
May 13, 2:00 PM: Dr. Zirui Zhang (UCI)
May 19 (Monday), 11:00 AM, Skye 268: Dr. Vishal Patil (UCSD)
May 20, 2:00 PM: Dr. Sara Stahley (Penn State Cancer Institute)
May 27, 2:00 PM: Dr. Simon Martina-Perez (Trinity College, U Oxford)
Jun 3, 2:00 PM: Dr. On Shun Pak (Santa Clara Univeristy)
CANCELLED Jun 6 (Friday), 1:00pm: Dr. Adam MacLean (USC)
Upcoming talks:
Jun 3, 2025, 02:00 PM - 03:00 PM Pacific Time
Dr. Sina Heydari, Santa Clara University
Title: Bio-Inspired Locomotion Across Scales Using Artificial Intelligence
Abstract: Biological organisms exhibit remarkable proficiency in locomotion. From sea stars walking on rocky and uneven terrains to microscopic organisms swimming through
viscous fluids, they are well-adapted to their environments. Effective motion in such environments requires the ability to sense local cues, infer information about their surroundings, and use that information for effective navigation and control. This challenge becomes particularly pronounced in complex fluid environments, such as vortical flows, where swimmers must adapt dynamically to unpredictable conditions, or in Stokes flow where viscous forces are dominant and inhibit swimming. In this talk, I will explore how recent advances in artificial intelligence (AI) can be applied to the design of bio-inspired swimmers operating in various environments. I will specifically focus on three model problems: a three-link fish in potential flow, a pair of schooling swimmers, and a microswimmer navigating a low-Reynolds number environment. In each case, I will show how AI-powered control enables these agents to overcome the inherent difficulties of locomotion in their respective environments. Together, these results highlight the transformative potential of AI in enabling adaptive and robust locomotion in fluid environments.
Bio: Dr. Sina Heydari is currently a Research Associate and Lecturer at Santa Clara University and an incoming Assistant Professor in the Department of Mechanical Engineering at California State University, Northridge. He earned a Ph.D. in Mechanical Engineering from the University of Southern California in 2023. He also holds an M.S. in Mechanical Engineering from the University of Southern California and a B.S. in Mechanical Engineering from Sharif University of Technology. His research focuses on
locomotion of biological systems, with an emphasis on fluid-structure interactions and their applications to complex biological systems, such as schools of fish. An emerging
area of his work integrates physics-based models with Artificial Intelligence algorithms to control and optimize bioinspired engineering systems. Dr. Heydari’s research has
been featured in prominent media outlets, including the BBC and KQED.
Previous talks:
Apr 8, 2025, 02:00 PM - 03:00 PM Pacific Time
Ethan Nowaski, UCR
Title: Computational Model of Eversion During Development of Drosophila Wing
Abstract: One of the most important open questions in developmental biology is determining how organs and tissues form and maintain their shape. The robust formation of organs during development depends on the careful regulation of cellular processes such as cell adhesion, mechanical stiffness of the cell membrane, and internal pressure to create the tissue-scale architecture. A sophisticated communication system coordinates these developmental processes. This project utilizes the fruit fly wing imaginal disc, a powerful biological model system, to study how downstream effectors of ecdysone signaling contribute to regulating cell mechanical properties that influence cell shapes and overall tissue structures during wing disc eversion. Eversion is one of the final parts of development involving decline of cell proliferation and folding of the wing driven by cell rearrangements and morphological changes driven by actomyosin dynamics. The project workflow includes iteration between quantitative experiments and biologically calibrated computational model simulations. In this talk, a novel extension of the two-dimensional subcellular element computational model of the developing Drosophila wing [2] will be described and model simulations will be used to demonstrate potential mechanisms of wing folding during eversion
Apr 15, 2025, 02:00 PM - 03:00 PM Pacific Time
Dr. Meisam Zaferani, Princeton University
Title: Circling-and-Wandering: Toward a Quantitative Model of Mammalian Sperm Chemotaxis
Abstract: To fertilize the oocyte, sperm must navigate the female reproductive tract while enduring strong selective pressures. This journey is facilitated by key physiological transitions, including a motility shift known as hyperactivation. In this talk, I will present experimental results showing that hyperactivated bull sperm exhibits three distinct gaits: wandering, circling, and a mixed circling-and-wandering behavior. Using mathematical modeling and stochastic simulations, I demonstrate that while wandering and circling support exploration and exploitation strategies, respectively, the mixed phase balances both to potentially improve search efficiency and enhance transport through complex environments. As such, I will suggest that the circling-and-wandering phase may serve as a chemotactic strategy within the female reproductive tract, addressing a long-standing question in mammalian reproductive biology. Finally, I will discuss how novel computational approaches, such as reinforcement learning, can help uncover potential cooperative interactions between sperm and the female reproductive tract to regulate this circling-and-wandering behavior.
Bio: I was born and raised in Tehran, Iran. After completing high school and earning two medals in the National Physics and Mathematics Olympiads, I pursued an undergraduate degree in Electrical Engineering at the University of Tehran, followed by a master’s degree in applied physics. During my master’s studies, I developed a deep passion for biological physics, particularly after reading Physics of Chemoreception by Howard Berg. This interest led me to Cornell University, where I obtained my PhD and received training in both experimental and theoretical biophysics. I then joined Princeton University’s vibrant biophysics and bioengineering community as an Omenn-Darling Fellow, where I study cytoskeletal self-organization at the molecular level, working with microtubules to develop neuro-inspired nanotechnologies. In addition, I am leading an experimental and theoretical study on mammalian sperm chemotaxis.
Apr 22, 2025, 02:00 PM - 03:00 PM Pacific Time
Dr. Tianchi Xin, Yale School of Medicine
Title: Insights into tissue architecture formation and maintenance through intravital imaging
Abstract: Proper tissue architecture is fundamental to normal tissue function, relying on the orchestrated behavior of tissue resident cells, such as stem cells and their niche. Disruption of this coordination by oncogenic mutations can lead to architectural abnormalities and cancer development. While many genes and pathways involved in these processes have been identified, how cells dynamically act and communicate to build and maintain tissue remains poorly understood. My research leverages intravital imaging to tackle these questions and unravel the principles of tissue architecture regulation, using the mouse hair follicle as a mammalian model system. Previously, I led research with my colleagues discovering that, despite molecular heterogeneity, individual hair follicle stem cells and their progeny can flexibly adopt diverse fates based on local spatial cues (PMID: 37419106). Through mentoring a graduate student as a co-corresponding author, I also showed that the hair follicle mesenchyme undergoes dynamic architectural remodeling to enhance its structural robustness and fine-tune epithelial functions (PMID: 37419106). Most recently, by combining an oncogenic Kras mutation with a dynamic ERK signaling sensor, I led and co-corresponded a study that uncovered the pivotal role of signaling dynamics in coordinating stem cell behaviors to maintain normal tissue architecture (PMID: 38689013). These findings provide new insights into the mechanisms that drive tissue regeneration and remodeling, as well as reveal how their disruption can contribute to the early stages of cancer initiation.
Bio: Dr. Tianchi Xin is a research scientist in the Department of Genetics at Yale University. He received his Ph.D. at Shanghai Jiao Tong University, where he studied the regulatory mechanisms of stem cell fate and tissue structure using Drosophila as a model organism. He then joined the laboratory of Dr. Valentina Greco at Yale as a postdoc, where he helped establish and advance an intravital imaging approach that allows to longitudinally track and manipulate the same cells at the single-cell level in live mice across various time scales. His postdoctoral work was recognized with several honors, including the New York Stem Cell Foundation Druckenmiller Fellowship and the Dermatology Foundation Research Grant. In 2022, Dr. Xin was promoted to his current position.
May 6, 2025, 02:00 PM - 03:00 PM Pacific Time
Dr. Derek Moulton, Oxford
Title: Morphorods: A modelling framework for active slender structures
Abstract: Elastic filamentary structures are ubiquitous in nature, and can be found at all scales, from microscopic chains of molecules to hairs, vines, umbilical cords, and elephant trunks. Our interest in this talk is active slender elastic structures, which are capable of changing their properties in response to their environment and/or to accomplish a task. For biological structures, this may be achieved through some combination of growth, remodelling, or muscle activity. For instance, a vine searching for a pole to climb actively generates a helical shape through a directed change in growth hormone, while an elephant modulates the shape of its trunk by contraction of muscle groups. Active filamentary structures also have strong relevance in engineering applications, with great potential in biomedical devices and in the expanding field of soft robotics.
Due to their inherent slenderness, the mechanical behaviour of growing filaments is well-characterised by a one-dimensional continuum representation. In this talk I will outline a modelling framework for describing the mechanical behavior of active slender elastic structures, which we term morphoelastic rods, or simply morphorods. I will demonstrate the utility of the theory via a number of diverse applications, from pattern formation in seashells to tropic growth of plants to the remarkable dexterity of an elephant's trunk.
Bio: Dr Derek Moulton is a Professor of Applied Mathematics at the Mathematical Institute at the University of Oxford. He received his PhD in Mathematical Sciences from the University of Delaware in 2008. He was a postdoc at the University of Arizona and then at Oxford, before taking his current faculty position in 2013. His research focuses on the development and analysis of physics-based mathematical models, applying and adapting tools from continuum mechanics to investigate morphogenesis, growth, pattern formation, physiology, biomimetics, and biomedical devices.
May 13, 2025, 02:00 PM - 03:00 PM Pacific Time
Dr. Zirui Zhang, UCI
Title: Bilevel Local Operator Learning for PDE Inverse Problems: From Personalized Prediction of Tumor Infiltration to Adaptive Digital Twins
Abstract: Predicting brain tumor infiltration from MRI scans is crucial for understanding tumor progression and optimizing personalized treatment. While mathematical models of tumor growth provide valuable insights, estimating patient-specific parameters from clinical data remains a challenging inverse problem due to sparse and noisy data. We first developed a Physics-Informed Neural Network (PINN) approach to estimate these parameters from a single MRI scan, integrating multimodal MRI data with a reaction–diffusion PDE model. However, we observe that the soft constraints in PINNs lead to a trade-off between enforcing physical laws and fitting noisy data.
To address this limitation, we introduce Bilevel Local Operator Learning (BiLO) for PDE inverse problems. BiLO formulates the inverse problem as a bilevel optimization problem, eliminating the need to balance fitting data and solving PDEs, while improving robustness to sparse and noisy data. Furthermore, by leveraging transfer learning techniques, we extend BiLO to enable efficient sampling and uncertainty quantification within a Bayesian framework.
Beyond tumor modeling, many scientific challenges—from modeling stochastic gene expression snapshots to understanding the accumulation of misfolded proteins in Alzheimer’s research—require fitting increasingly complex mathematical models to ever-evolving data. We explore how our approach can be extended to build an adaptive digital twin framework that adjusts to new data and new models, accelerating scientific discovery across disciplines.
Bio: Ray Zirui Zhang is a Visiting Assistant Professor in the Department of Mathematics at the University of California, Irvine. He earned his Ph.D. in Mathematics from the University of California, San Diego in 2022. His research focuses on scientific machine learning and numerical analysis, with applications in biophysics and cancer research. https://sites.google.com/uci.edu/rayzhang
May 19, 2025, Monday, 11:00 AM - 12:00 PM Pacific Time, Skye 268
Dr. Vishal Patil, UCSD
Title: Topological Dynamics of Knotted and Tangled Matter
Abstract: Topology and adaptivity play fundamental roles in controlling the dynamics of biological and physical systems, from chromosomal DNA and biofilms to cilia carpets and worm collectives. How topological rules give rise to adaptive, self-optimizing dynamics in soft and living matter remains poorly understood. Here we investigate the interplay between topology, geometry and reconfigurability in knotted and tangled matter. We first identify topological counting rules which predict the relative mechanical stability of human-designed knots, by developing a mapping between elastic knots and long-range ferromagnetic spin systems. Building upon this framework, we then examine the adaptive topological dynamics exhibited by California blackworms, which form living tangled structures in minutes but can rapidly untangle in milliseconds. Using blackworm locomotion datasets, we construct stochastic trajectory equations that explain how the dynamics of individual active filaments controls their emergent topological state. By identifying how topology and adaptivity produce stable yet responsive structures, these results have applications in understanding broad classes of adaptive, self-optimizing systems.
Bio: Vishal Patil is an assistant professor in the mathematics department at the University of California, San Diego. He received a BA and MMath (Part III) from the University of Cambridge (2016) and completed his PhD in mathematics at MIT (2021), where he was a MathWorks fellow. Prior to joining UCSD, he was a Stanford Science Fellow from 2021-2024. His research focuses on problems in applied mathematics, with an emphasis on living systems, soft matter and fluid dynamics. A central theme is exploring the role of geometry, topology and information in these systems.
May 20, 2025, 02:00 PM - 03:00 PM Pacific Time
Dr. Sara Stahley, Penn State Cancer Institute
Title: Regulation of planar cell polarity by atypical cadherin Celsr1
Abstract: Planar cell polarity (PCP), the collective polarization of cells along a tissue plane, is essential for embryonic development and tissue patterning. Disruptions in the PCP pathway result in developmental disorders ranging from spina bifida and cardiomyopathies to tissue patterning defects of the inner ear and skin. A hallmark feature of PCP is the asymmetric localization of core PCP proteins Fz6 and Vangl2 at an intercellular junctional complex organized by cadherin family member Celsr1. Our studies of Celsr1 in mouse revealed that Celsr1 functions not only as a trans-adhesive molecular bridge, but also as an organizer of Fz6 and Vangl2 asymmetry at cell junctions through lateral, cis-interactions and dimerization. Furthermore, we determined stable Celsr1 adhesion via cis-interactions is also required for PCP complex internalization and trafficking during mitosis to preserve tissue polarity. Additionally, Celsr1 mutations occur in patients with developmental defects such as congenital heart defects and neural tube defects, and in patients with epidermal-derived tumors such as carcinomas and melanoma. To test the hypothesis that these human disease-associated Celsr1 mutations perturb PCP establishment and maintenance by impairing Celsr1 adhesion, we tested the function of WT Celsr1 and a panel of disease-associated Celsr1 mutants using a combination of cell adhesion assays, advanced optical imaging, and in vivo murine genetic approaches. We identified mutations in the Celsr1 cadherin repeats from developmental disorders that displayed either gain- or loss-of-function activity as assessed by border recruitment and fluorescent recovery after photobleaching assays. Interestingly, mutations identified in melanoma result in Celsr1 variants that display significantly reduced adhesive function and disrupt PCP in vivo. Overall, our findings indicate that Celsr1 adhesive interactions play a key role in vertebrate development to coordinate tissue polarity and suggest that Celsr1 adhesion and PCP are altered in both human developmental disorders and skin cancers.
Short bio: Sara Stahley, PhD, is an Assistant Professor in the Departments of Dermatology and Cellular and Molecular Physiology at the Penn State College of Medicine. A graduate of Clarkson University in Potsdam, NY, she received her PhD in Biochemistry, Cell and Developmental Biology from Emory University in Atlanta, GA and completed postdoctoral training with Alexa Mattheyses at Emory prior to pursuing a postdoctoral fellowship in the lab of Danelle Devenport at Princeton University. Dr. Stahley joined the faculty at Penn State in 2021 and leads an NIH-funded research program utilizing the mouse epidermis as a model system to investigate the cell-cell interactions that coordinate organ development and tissue patterning. With past support from the Dermatology Foundation, Children’s Miracle Network and National Institutes of Health, her research efforts have uncovered how epithelial junction organization is regulated to control cell adhesion and tissue polarity during development and disease.
May 27, 2025, 02:00 PM - 03:00 PM Pacific Time
Dr. Simon Martina-Perez, Trinity College, University of Oxford
Title: From developmental differentiation to optimal combination therapy: mathematical strategies for modelling heterogeneous cell populations in disease
Abstract: Cell-state heterogeneity is a defining feature of many biological systems, shaping development, disease progression, and responses to therapy. I will first outline a model describing how heterogeneous populations arise through differentiation processes in neuroblastoma. Then, I will present a general mathematical framework exploiting cell heterogeneity when designing multi-drug regimens with synergistic effects.
1. Phenotypically structured differentiation model. Starting from a generic lineage graph, we describe cell populations by a system of birth-death-transition equations whose parameters are inferred with Bayesian methods from single-cell transcriptomics data. The model captures how variations in transition rates and proliferation potentials translate into distinct phenotype distributions and growth kinetics. As an illustrative application, fitting the framework to clinical samples from healthy adrenal tissue and four neuroblastoma subtypes reveals how modest shifts in developmental flux can amplify malignant traits and create lineage-specific vulnerabilities.
2. Optimal-control treatment model for interacting drugs. We then embed the differentiated phenotypes in an optimal-control system that governs their responses to treatment with multiple drugs. Coupled semi-linear ODEs encode phenotype-specific sensitivities and higher-order drug synergies. Throughout, the exact differentiation dynamics are shown to have a significant impact on treatment strategies.
Linking the two models provides a quantitative pipeline from developmental dynamics to therapy design, showing that maintaining controlled differentiation flux while timing synergistic drug exposures can delay resistance. I will highlight open challenges in parameter identifiability and translating mathematically optimal schedules into practicable protocols, charting a path toward data-driven, heterogeneity-aware treatments in diverse biological contexts.
Bio: Simon Martina-Perez is a Foulkes Foundation Fellow at the University of Oxford. He received his DPhil (PhD) in Mathematical Biology from the University of Oxford working with Prof Ruth Baker. He received the 2024 SMB H. D. Landahl Mathematical Biophysics Award for his work on data-driven modelling approaches to study collective cell migration in the context of pathology, regeneration, and development. After obtaining his DPhil, Simon has taken on a new challenge: training as a medical doctor. Currently, he combines his time between clinical training and research.