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Computational Modeling of Biological Systems

The Cui Group makes extensive use of BU’s Shared Computing Cluster as it tackles some of the most challenging and exciting problems in molecular biophysics.

The Cui Group at Boston University, led by Qiang Cui, focuses on developing and applying computational modeling techniques to study complex biological systems at the molecular level. Their research spans a wide range of topics, including enzyme catalysis, protein dynamics, and biomolecular interactions, with a particular emphasis on understanding the fundamental principles that govern biological processes.

A key area of the Cui Group’s work involves the use of quantum mechanics and molecular mechanics hybrid methods to explore enzyme reactions and energy landscapes. These computational techniques allow the group to investigate the atomic-level mechanisms underlying enzyme catalysis and to predict how structural changes in proteins impact their function. By modeling reaction pathways, the group can provide valuable insights into how enzymes work and how they might be manipulated for applications in biotechnology or drug development.

The group also employs advanced molecular dynamics simulations to study protein conformational changes and interactions with other biomolecules, such as ligands or inhibitors. Their computational models provide a detailed picture of molecular motion and energy transfer within biological systems, offering a powerful tool for understanding complex biological phenomena. The Cui Group’s computational research contributes to a deeper understanding of molecular biology, with applications in biochemistry, pharmacology, and bioengineering.

Qiang Cui
Professor in the Departments of Chemistry, Physics, and Biomedical Engineering, Boston University

Research projects

The US ATLAS Northeast Tier 2 Center
Yale Budget Lab
Volcanic Eruptions Impact on Stratospheric Chemistry & Ozone
Towards a Whole Brain Cellular Atlas
Tornado Path Detection
The Kempner Institute - Unlocking Intelligence
The Institute for Experiential AI
Taming the Energy Appetite of AI Models
Surface Behavior
Studying Highly Efficient Biological Solar Energy Systems
Software for Unreliable Quantum Computers
Simulating Large Biomolecular Assemblies
Revolutionizing Materials Design with Computational Modeling
Quantum Computing in Renewable Energy Development
Pulling Back the Quantum Curtain on ‘Weyl Fermions’
New Insights on Binary Black Holes
Network Attached FPGAs in the OCT
Monte Carlo eXtreme (MCX) - a Physically-Accurate Photon Simulator
Modeling Hydrogels and Elastomers
Modeling Breast Cancer Spread
Impact of Marine Heatwaves on Coral Diversity
IceCube: Hunting Neutrinos
Global Consequences of Warming-Induced Arctic River Changes
Exact Gravitational Lensing by Rotating Black Holes
Evolution of Viral Infectious Disease
Evaluating Health Benefits of Stricter US Air Quality Standards
Ephemeral Stream Water Contributions to US Drainage Networks
Electron Heating in Kinetic-Alfvén-Wave Turbulence
Discovering Evolution’s Master Switches
Dexterous Robotic Hands
Developing Advanced Materials for a Sustainable Energy Future
Detecting Protein Concentrations in Assays
Denser Environments Cultivate Larger Galaxies
Deciphering Alzheimer's Disease
Dancing Frog Genomes
Asteroid Data Mining
Analyzing the Gut Microbiome
Adaptive Deep Learning Systems Towards Edge Intelligence
Accelerating Rendering Power
Computation + Machine Intelligence | Wu Tsai Institute
Computational Modeling of Biological Systems
Social Capital and Economic Mobility
MIT Brain and Cognitive Sciences
Building for Floods
Better Pathogen Targeting
Tracking Environmental Health Risks
AI for Cancer Diagnosis
Microplastic-Free by Design
Supporting Data-intensive Social Science
Sailing the Symbiosis Seascape
Wrangle Range Modeling
Shining a Light on Dark Matter
Grid Responsive Data Centers
Multifunctional 3D-Printed Materials
AI Pareidolia
Computing Hidden Health Threats from Heat
Staving off the Banana Apocalypse
CRISPR Mice, Smarter Science
Naval and Ocean Renewable Energy Hydrodynamics
AI That Speaks Human About Health
A Safer Way to See Inside Cells
How Monkeys - and Machines - See in 3D
FlowER: AI for Predicting Chemical Reactions
Supercomputers Reveal Ancient Atmospheric Battle
OSN - Open Storage Network
Massachusetts AI Hub
MGHPCC AI Computing Resource (AICR)
YARD: A Curation Workflow Tool
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