Zuckerman Lab research overview
The Zuckerman Lab uses physics-based computational methods to study molecular, meso- and cell-scale systems. Starting from force laws for individual atoms, molecular simulation can provide a detailed picture of nanoscale biological behavior and aid rational drug design. The group develops and applies simulation methods to access important biological timescales and behaviors –such as protein conformational changes associated with ligand binding. The group also uses kinetic models to study "meso-scale" molecular machines, with a focus on the rotary ATPases which remarkably can act as proton pumps or ATP synthases. A new area of interest is modeling complex cellular morphologies and behaviors observed in cancer cells. All the group's work is underpinned by statistical physics and thermodynamics, which govern nano- to micro-scale biology.
Current Molecular Scale Research: Simulation, Sampling, Mechanism, Kinetics
The group develops and applies simulation technology for studying mechanism and kinetics in proteins and molecular machines. (i) We develop physics-based enhanced molecular dynamics algorithms and software, largely focusing on the weighted ensemble (WE) method and WESTPA software. (ii) We also study the theory of trajectories, particularly Markov and non-Markov discrete-state approximations to continuous dynamical behavior. (iii) We study mechanisms of molecular machines using a systems biology approach with discrete states and Bayesian inference of model parameters from experiments.
Current Cellular/subcellular Research: Dynamical behavior of complexes and proteins in cells
We perform biophysical analyses of electron microscopy (EM) and live-cell fluorescent imaging in close collaboration with experimental colleagues. Novel algorithms for analyzing EM images of heterogeneous multivalent complexes enable correction of apparent (but biased) oligomer populations through a self-consistency criterion. Similarly, physics-based analysis of single-particle tracking of cell-surface proteins enabled inference about trafficking processes not directly observed.
Current Cellular and Multi-cellular Research: Statistical mechanics analysis of morphodynamics
Research into cellular behavior focuses on analyzing trajectories – of entire cells or groups of cells – using the methods of statistical physics. Ultimately such work could lead to diagnostic stratification of cancer subtypes. Our approach uses dynamical information – multiple frames of live-cell imaging – as a key feature for analysis.
People and Projects
I work on developing simulation tools for biomolecular systems and methods for analyzing these simulations. These let us get better estimates of observables like rate constants more efficiently and accurately, with potential application to drug design. One focus is to better understand and manage the bias present in Markov models. My work has helped develop and bridge my interests through applying my physics and computational skills in the context of biology.
My research focuses on developing computational methods to study biomolecular machines. This work improves our understanding of essential biological processes and could aid in the development of synthetic machines and more effective drugs. I use Bayesian inference and Markov chain Monte Carlo sampling to estimate the kinetic parameters governing coarse-grained models of cotransporters such as EmrE. I believe that the skills and knowledge I gain through this research will make me a competitive candidate for industrial research positions.
I am working to develop data-driven methods which can rationalize and predict cell behaviors, in particular the dysregulation of cell motility and proliferation driving cancer metastasis. Single-cell morphodynamical trajectories extracted from live-cell imaging are the basis of our analysis and modeling efforts, with the goal of integrating this whole-cell trajectory information with mechanistic molecular detail. My hope is that with our OHSU and Knight Cancer Institute collaborators, the novel computational approaches we are developing will lead to biological insight contributing to the treatment of advanced cancers.
The group's research combines my passions for physics, biology, and computation. While physics and computation provide key tools for modern biology, they are also opening up our understanding of biological phenomema that previously were opaque. My favorite example is kinetic proofreading, in which the cell burns extra energy in exchange for increased accuracy in transcribing and translating genetic information. Physical principles and associated computational strategies apply to biology at every scale, creating a lot of great research opportunities. Our work is also beginning to exploit statistical/machine learning in exciting ways.
Shelby joined the group in Fall 2021 and will be using molecular dynamics simulations to study allostery in multivalent binding.
Harry joined the group late in 2021 and will work toward automated optimization of weighted ensemble simulations.
Prospective students and postdocs
Interested in this research? Please contact Prof. Zuckerman directly - email@example.com