Bulletin of the American Physical Society
APS March Meeting 2023
Volume 68, Number 3
Las Vegas, Nevada (March 5-10)
Virtual (March 20-22); Time Zone: Pacific Time
Session S13: Data-driven Dynamical Systems in Biology and Soft matterInvited
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Sponsoring Units: DBIO Chair: Jianhua Xing, University of Pittsburgh; Enkeleida Lushi, New Jersey Institute of Technology Room: Room 238 |
Thursday, March 9, 2023 8:00AM - 8:36AM |
S13.00001: Machine Learning for Scientific Discovery Invited Speaker: Steven L Brunton This work describes how machine learning may be used to develop accurate and efficient nonlinear dynamical systems models for complex natural and engineered systems. We explore the sparse identification of nonlinear dynamics (SINDy) algorithm, which identifies a minimal dynamical system model that balances model complexity with accuracy, avoiding overfitting. This approach tends to promote models that are interpretable and generalizable, capturing the essential "physics" of the system. We also discuss the importance of learning effective coordinate systems in which the dynamics may be expected to be sparse. This sparse modeling approach will be demonstrated on a range of challenging modeling problems in fluid dynamics, and we will discuss how to incorporate these models into existing model-based control efforts. Because fluid dynamics is central to transportation, health, and defense systems, we will emphasize the importance of machine learning solutions that are interpretable, explainable, generalizable, and that respect known physics. |
Thursday, March 9, 2023 8:36AM - 9:12AM |
S13.00002: Multiphysics and multiscale modeling of erythrophagocytosis Invited Speaker: He Li Erythrophagocytosis is an essential human physiological process responsible for daily removal of 16-24 billion damaged and senescent red blood cells (RBCs) from the circulating blood. Biochemical signaling pathways mediated by the ligand-receptor interactions have been considered as the key factors that initiate and drive the phagocytosis of these abnormal RBCs by the tissue-resident macrophages in the spleen and liver. However, increased evidence has underscored the significance of the physical properties of phagocytic targets in modulating the engulfing process. Because of the lack of experimental approaches to quantify the chemical kinetics and cell dynamics during phagocytosis, it is not clear how biological signaling pathways cooperate with biophysical alterations of the phagocytes and their targets to ensure the timely and efficient removal of such an immense number of RBCs. To fill this knowledge gap, we develop a new biochemical signaling model to simulate the initiation of erythrophagocytosis by the interaction between the ligands on the RBCs and their receptors on the macrophages. This model is developed based on system biology-informed neural networks (SBINNs), which can infer the unknown parameters, e.g. reaction rates, and dynamics of species, in the model with a few experimental data as well as enable continuous model refinement using emerging experimental data. Next, we will develop new biophysical models to simulate the macrophage adhering and phagocytosing RBCs. These mechanistic models are used to investigate and quantify the impact of the rigidity of the macrophages as well as the rigidity and morphologies of aged RBCs and RBCs in diseases, such as sickle cell disease (SCD), hereditary spherocytosis and elliptocytosis, on the efficiency of the engulfing processes. The model predictions will be validated against phagocytosis experiments from our collaborators and in the literature. Then, we will integrate the biochemical signaling model with the biophysical phagocytosis model to investigate how biochemical and biophysics are intertwined in dictating the dynamics of phagocytosis. |
Thursday, March 9, 2023 9:12AM - 9:48AM |
S13.00003: Symmetry-informed model inference for living matter Invited Speaker: Jorn Dunkel Recent experimental advances enable high-resolution observations of biological and synthetic active matter across a wide range of length and time scales. A major interdisciplinary challenge is to translate high-dimensional live-imaging and gene-expression data into low-dimensional mathematical models that will allow us to predict and understand the emergent behaviors of complex biophysical systems. In this talk, I will describe our current efforts to develop computational inference frameworks capable of learning interpretable dynamical equations directly from spatio-temporal data provided by our experimental collaborators. After outlining theoretical and computational challenges posed by state-of-the-art sequencing and microscopy data, we will show how symmetry concepts and modern algorithmic approaches can be combined to construct efficient mode representations and robust inference schemes for biophysical model discovery. To illustrate the practical potential, we present example applications ranging from cell migration dynamics and animal locomotion to the collective swarming of active colloids and fish. |
Thursday, March 9, 2023 9:48AM - 10:24AM |
S13.00004: Data-Driven Reduction of Intrisically Nonlinear Dynamics to Spectral Submanifolds: Theory and Applications Invited Speaker: George Haller Machine learning has been a major development in applied science and engineering, with impressive success stories in static learning environments like image, pattern, and speech recognition. Yet the modeling of dynamical phenomena—such as nonlinear vibrations of solids and transitions in fluids—remains a challenge for classic machine learning. Indeed, neural net models for nonlinear dynamics tend to be complex, uninterpretable and unreliable outside of their training range. |
Thursday, March 9, 2023 10:24AM - 11:00AM |
S13.00005: Principles of cellular behavior: integrating cellular structure, dynamics, and decision making in a unicellular walker Invited Speaker: Benjamin Larson Although it may be easy to think of cells as little more than the simple building blocks of more complex organisms, single cells are capable of remarkably sophisticated behaviors. Such behaviors, necessary for survival of microbes in the diverse environments they inhabit as well as for the proper function and development of our own bodies, emerge from the interactions among myriad molecular components in conjunction with physical constraints and mechanisms that dictate interactions between the cell and its environment. We seek to navigate this mechanistic complexity using Euplotes, a ciliate that walks across surfaces using motile appendages (cirri) composed of bundles of cilia, as a model system. Drawing on ideas from non-equilibrium physics and computer science, we demonstrate finite state machine-like processing embodied in walking Euplotes eurystomus cells. We found that cellular walking entails regulated transitions between a discrete set of gait states. The set of observed transitions decomposes into a small group of high-probability, temporally irreversible transitions and a large group of low-probability time-symmetric transitions, thus revealing stereotypy in sequential patterns of state transitions. Simulations and experiments suggest that the sequential logic of the gait is functionally important. Taken together, these findings implicate a finite state machine-like process. Cirri are connected by microtubule bundles (fibers), and we found that the dynamics of cirri involved in different state transitions are associated with the structure of the fiber system. Perturbative experiments revealed that the fibers mediate gait coordination, suggesting a mechanical basis of gait control. Ultimately, we aim to elucidate general principles of the regulation and evolution of cellular behavior by integrating understanding across scales of biological organization, linking cellular structure and physiology to patterns of behavior to environmental contexts. |
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