Dinner Group
The Dinner Group investigates how complex biological behaviors emerge from molecular interactions, focusing on systems far from equilibrium that involve irreversible energy consumption. They attempt to understand growth, movement, and responses to stimuli in living systems, analyzing experimental data quantitatively. Their research constructs physical models to interpret statistical observations and develops algorithms to simulate dynamic processes efficiently. They specialize in enhancing the sampling of rare events and recovering their statistics, building models to interpret cellular dynamics analytically and numerically. They apply quantitative approaches to interpret data, aiming to uncover fundamental principles underlying biological phenomena at various scales.
Aaron Dinner
Noah Gamble
Chatipat Lorpaiboon
Jordan Shivers
Nhan Nguyen
Ian Bongalonta
Spencer Guo
Zihan Pengmei
Ondrej Maxian
Nico Romeo
Chris Chi
Kwanghoon Jeong
Qi-Nan Huang
Nils Strand
Weizhou Wang
Carlos Floyd
Darren Liu
Sarah Root
Yihang Wang
Recent
Inexact iterative numerical linear algebra for neural network-based spectral estimation and rare-event prediction (2023)
Understanding dynamics in complex systems is challenging because there are many degrees of freedom, and those that are most important for describing events of interest are often not obvious. The leading eigenfunctions of the transition operator are useful for visualization, and they can provide an efficient basis for computing statistics, such as the likelihood and average time of events (predictions). Here, we develop inexact iterative linear algebra methods for computing these eigenfunctions (spectral estimation) and making predictions from a dataset of short trajectories sampled at finite intervals. We demonstrate the methods on a low-dimensional model that facilitates visualization and a high-dimensional model of a biomolecular system. Implications for the prediction problem in reinforcement learning are discussed.
Dynamics of activation in the voltage-sensing domain of Ci-VSP (2023)
The Ciona intestinalis voltage-sensing phosphatase (Ci-VSP) is a membrane protein containing a voltagesensing domain (VSD) that is homologous to VSDs from voltage-gated ion channels responsible for cellular excitability. Two crystal structures of Ci-VSD in putative resting and active conformations suggest a helical-screw voltage sensing mechanism in which the S4 helix translocates and rotates to enable exchange of salt-bridge partners. By combining extensive molecular dynamics simulations with a computational framework based on dynamical operators, we elucidate the microscopic mechanism of the resting-active transition at physiological membrane potential. Sparse regression reveals a small set of coordinates that distinguish intermediates hidden from electrophysiological measurements. The intermediates arise from a noncanonical helical-screw mechanism in which translocation, rotation, and side-chain movement of the S4 helix are only loosely coupled. These results provide new insights into existing experimental and computational findings on voltage sensing and suggest ways of further probing its mechanism.
A unified model for the dynamics of ATP-independent ultrafast contraction (2023)
In nature, several ciliated protists possess the remarkable ability to execute ultrafast motions using protein assemblies called myonemes, which contract in response to Ca2+ ions. Existing theories, such as actomyosin contractility and macroscopic biomechanical latches, do not adequately describe these systems, necessitating development of models to understand their mechanisms. In this study, we image and quantitatively analyze the contractile kinematics observed in two ciliated protists (Vorticella sp. and Spirostomum sp.), and, based on the mechanochemistry of these organisms, we propose a minimal mathematical model that reproduces our observations as well as those published previously. Analyzing the model reveals three distinct dynamic regimes, differentiated by the rate of chemical driving and the importance of inertia. We characterize their unique scaling behaviors and kinematic signatures. Besides providing insights into Ca2+-powered myoneme contraction in protists, our work may also inform the rational design of ultrafast bioengineered systems such as active synthetic cells.