Abstract : The advent of big data in biology offers many new challenges and opportunities. In this minisymposium, we will discuss advances in calibration and validation of biologically-driven mathematical models, spanning biological applications such as cancer, symbiosis, and circadian rhythms. An equally diverse set of mathematical techniques will be discussed such as Bayesian approaches, agent-based parameter estimation, and machine learning approaches. The first session of our minisymposium will focus on challenges specific to cancer modeling such as leveraging population-level data while preserving inter-individual heterogeneity, while the second session of the minisymposium will focus on broader methodology development in inferring mechanisms from data.
[01710] Multiscale spatiotemporal reconstruction of single-cell genomics data
Format : Talk at Waseda University
Author(s) :
Qing Nie (University of California, Irvine)
Abstract : Cells make fate decisions in response to dynamic environments, and multicellular structures emerge from multiscale interplays among cells and genes in space and time. The recent single-cell genomics technology provides an unprecedented opportunity to profile cells. However, those measurements are taken as static snapshots of many individual cells that often lose spatial information. How to obtain temporal relationships among cells from such measurements? How to recover spatial interactions among cells, such as cell-cell communication? In this talk I will present our newly developed computational tools that dissect transition properties of cells and infer cell-cell communication based on nonspatial single-cell genomics data. In addition, I will present methods to derive multicellular spatiotemporal pattern from spatial transcriptomics datasets. Through applications of those methods to systems in development and regeneration, we show the discovery power of such methods and identify areas for further development for spatiotemporal reconstruction of single-cell genomics data.
[01731] Integrating quantitative MRI with computational modeling to predict the response of breast cancers to neoadjuvant therapy
Format : Online Talk on Zoom
Author(s) :
Thomas Yankeelov (The University of Texas at Austin)
Chase Christenson (The University of Texas at Austin)
Casey Stowers (The University of Texas at Austin)
Reshmi Patel (The University of Texas at Austin)
Chengyue Wu (The University of Texas at Austin)
Abstract : We will discuss how magnetic resonance imaging data (MRI) can initialize and constrain mathematical models describing cancer proliferation, migration/invasion, vascular status, and drug-related growth inhibition and cell death. More specifically, we will focus on 1) incorporating patient-specific MRI data into biology-based mathematical models, and 2) optimizing outcomes via patient-specific digital twins in breast cancer. The long-term goal is to provide a rigorous methodology that allows for optimizing therapeutic interventions on a patient-specific basis.
[04716] Deep Hybrid Modeling of Neuronal Dynamics using Generative Adversarial Networks
Format : Online Talk on Zoom
Author(s) :
Casey Diekman (New Jersey Institute of Technology)
Soheil Saghafi (New Jersey Institute of Technology)
Abstract : Mechanistic modeling and machine learning methods are powerful techniques for approximating biological systems and making accurate predictions from data. However, when used in isolation these approaches suffer from distinct shortcomings: model and parameter uncertainty limit mechanistic modeling, whereas machine learning methods disregard the underlying biophysical mechanisms. To address these shortcomings, we build Deep Hybrid Models (DeepHMs) that combine deep learning with mechanistic modeling to identify the distributions of mechanistic modeling parameters coherent to the data. We employed DeepHM to identify which ionic conductances are responsible for the altered excitability properties of CA1 pyramidal neurons in mouse models of Alzheimer’s disease.
[03082] Gene regulatory network dynamics in single cells
Format : Online Talk on Zoom
Author(s) :
Adam L MacLean (University of Southern California)
Megan Rommelfanger (University of Southern California)
Abstract : Single-cell genomics offer unprecedented resolution with which to study cell fate decision-making. We present new tools to infer gene regulatory networks (GRNs) controling cell fate decisions and model their multiscale dynamics. We introduce popInfer, single-cell multi-modal GRN inference via regularized regression, and demonstrate its potential for network discovery. We develop a single-cell resolved multiscale model coupling cell-cell communication with gene regulatory network dynamics, with which we discover a profound role for cell-cell communication in hematopoiesis.
[02727] The role of bacterial chemotaxis in microbial symbiosis
Format : Online Talk on Zoom
Author(s) :
Douglas Brumley (The University of Melbourne)
Abstract : Bacterial motility, symbioses, and marine nutrient cycling unfold at the scale of individual microbes, and are inherently dynamic. In this talk, I will outline how iteratively combining video-microscopy, image processing and mathematical modelling can resolve dynamic microscale processes which underpin the ecology of microbes. I will also demonstrate how the highly-resolved processes at the scale of individual cells can be connected to bulk measurements at the population-level through calibrated mathematical models.
[04509] Multi-scale modelling of the uterus and the 12 Labours project
Format : Online Talk on Zoom
Author(s) :
Alys Rachel Clark (University of Auckland)
Shawn Means (University of Auckland)
Claire Miller (University of Auckland)
Mathias Roesler (University of Auckland)
Amy Garrett (University of Auckland)
Leo Cheng (University of Auckland)
Abstract : Uterine contractions contribute to fertility, menstruation, and delivery of babies, and the uterus has unique properties compared to other smooth muscle organs (including extensive stretch in pregnancy without contraction). Here, I present cell-to-tissue models of the uterus which form part of the 12 Labours project. This project takes a data-driven and reproducible approach to modelling physiological systems, which aims to integrate models into clinical workflows and provide feedback to wearable devices that monitor the uterus.
[02752] Bayesian discovery of mechanics and signaling during collective cell migration
Format : Online Talk on Zoom
Author(s) :
Simon Martina Perez (Oxford)
Ruth E. Baker (University of Oxford)
Abstract : Collective cell migration results from a complex interplay of cell-cell interactions and whole-tissue mechanics. Experimental data enables Bayesian inference to identify the role of mechanics and cell-cell interactions. While mathematical models can be identified with sufficiently detailed data, the relationship between observation noise and uncertainty in the learned models remains unexplored. We explore how to combine data sets to quantify uncertainty, and draw mechanistic conclusions about the underlying biophysical process in morphogenesis and cancer invasion.
[05213] PIEZO1 regulates cellular coordination during collective cell migration
Format : Talk at Waseda University
Author(s) :
Jinghao Chen (University of California, Irvine)
Jesse Holt (University of California, Irvine)
Beth Evans (University of California, Irvine)
John Lowengrub (University of California, Irvine)
Medha Pathak (University of California, Irvine)
Abstract : The mechanically-activated ion channel PIEZO1 was recently identified to play an inhibitory role during wound healing. Through an integrative experimental and mathematical modeling approach, we elucidate PIEZO1’s contributions to keratinocyte collective migration, an essential component of the healing process. Here, through a 2D-multiscale model of wound closure which links observations at both the single and multicell scales, and subsequent experimental validation, we identify cell directionality as being impacted by PIEZO1 activity during wound closure.
