Christopher Lee
· Assistant ProfessorUniversity of California, San Diego · Molecular Biology
Active 1990–2024
About
Chris Lee received a B.Sc. in chemistry, B.A. in computer science, and an M.Sc. in biochemistry from the University of Virginia. He earned his Ph.D. in 2019 from UC San Diego, where he worked with Drs. Rommie Amaro and J. Andrew McCammon on molecular simulations. He continued his research at UC San Diego working with Drs. Padmini Rangamani and Michael Holst on models for cellular membrane mechanics and cell signaling. He joined the Molecular Biology faculty at UC San Diego in 2024. His research focuses on developing multiscale physical models to bring biological scenes at the subcellular and cellular scales to life, similar to molecular dynamics of proteins. His work aims to derive new biophysical insights by creating modeling approaches that complement experimental control of model organisms and systems. His scientific goals include understanding the biophysics governing cell shape and disease, with a focus on predicting and discovering causal mechanisms behind these phenomena.
Research topics
- Computer Science
- Artificial Intelligence
- Political Science
- Physics
- Computational science
- Computer Security
- Computer graphics (images)
- Marketing
- Risk analysis (engineering)
- Medicine
- Law
- Mathematical analysis
- Mathematics
- Business
- Algorithm
- Geometry
Selected publications
The Lancet · 2021 · 79 citations
- Computer Security
- Computer Science
- Political Science
PLoS Computational Biology · 2020 · 73 citations
1st authorCorresponding- Computer Science
- Computational science
- Computer Science
Recent advances in electron microscopy have enabled the imaging of single cells in 3D at nanometer length scale resolutions. An uncharted frontier for in silico biology is the ability to simulate cellular processes using these observed geometries. Enabling such simulations requires watertight meshing of electron micrograph images into 3D volume meshes, which can then form the basis of computer simulations of such processes using numerical techniques such as the finite element method. In this paper, we describe the use of our recently rewritten mesh processing software, GAMer 2, to bridge the gap between poorly conditioned meshes generated from segmented micrographs and boundary marked tetrahedral meshes which are compatible with simulation. We demonstrate the application of a workflow using GAMer 2 to a series of electron micrographs of neuronal dendrite morphology explored at three different length scales and show that the resulting meshes are suitable for finite element simulations. This work is an important step towards making physical simulations of biological processes in realistic geometries routine. Innovations in algorithms to reconstruct and simulate cellular length scale phenomena based on emerging structural data will enable realistic physical models and advance discovery at the interface of geometry and cellular processes. We posit that a new frontier at the intersection of computational technologies and single cell biology is now open.
An Open-Source Mesh Generation Platform for Biophysical Modeling Using Realistic Cellular Geometries
Biophysical Journal · 2020 · 37 citations
1st authorCorresponding- Computer Science
- Computational science
- Computer Science
Frequent coauthors
- 51 shared
Padmini Rangamani
University of California, San Diego
- 23 shared
Rommie E. Amaro
University of California, San Diego
- 17 shared
Miriam Bell
University of California, San Diego
- 15 shared
Michael Holst
- 14 shared
Justin G. Laughlin
University of California, San Diego
- 13 shared
Cuncheng Zhu
- 10 shared
Manolis Maragkakis
National Institute on Aging
- 10 shared
J. Andrew McCammon
University of California, San Diego
Labs
Education
- 2019
PhD, Chemistry & Biochemistry
University of California San Diego
- 2012
M.S., Chemistry
University of Virginia
- 2011
B.A., Computer Science
University of Virginia
- 2011
B.S. Chemistry with a Specialization in Biochemistry, Chemistry
University of Virginia
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