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Detecting Protein Concentrations in Assays

The goal of the Ping Nano/Bio Interfaces and Applications Lab at UMass Amherst led by Jinglei Ping is to determine the fundamental principles governing applications of nanomaterials and nanomaterial-based device structures in eg biotechnology, healthcare, and environmental monitoring. In its work, it uses computers housed at the MGHPCC.

Many studies in biology and medicine – including those involved in clinical diagnostics, many therapeutics, and large-scale studies of proteins – rely on detecting protein concentrations in samples. Such studies often depend on detecting minute protein concentrations and are thus constrained by the protein-detection limit of the sensors used. Faced with this limitation, researchers are forced to employ more-expensive detectors or more-complicated assays. In response to this challenge, a research team led by Jinglei Ping – an assistant professor in the UMass Amherst Mechanical and Industrial Engineering (MIE) Department who is also affiliated with the Institute for Applied Life Sciences – has developed a groundbreaking method for concentrating proteins in such samples, amplifying the detection limit in their protein analysis, and measuring these protein concentrations less expensively and more effectively.

Ping and his teams’ revolutionary method employs an inexpensive isoelectric focusing technique, which can detect proteins at concentrations four times lower, and therefore four times more effective, than is currently possible.

Jinglei Ping
Associate Professor, Mechanical and Industrial Engineering; Adjunct, Biomedical Engineering

Research projects

A Future of Unmanned Aerial Vehicles
Yale Budget Lab
Volcanic Eruptions Impact on Stratospheric Chemistry & Ozone
The Rhode Island Coastal Hazards Analysis, Modeling, and Prediction System
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
SEQer – Sequence Evaluation in Realtime
Revolutionizing Materials Design with Computational Modeling
Remote Sensing of Earth Systems
QuEra at the MGHPCC
Quantum Computing in Renewable Energy Development
Pulling Back the Quantum Curtain on ‘Weyl Fermions’
New Insights on Binary Black Holes
NeuraChip
Network Attached FPGAs in the OCT
Monte Carlo eXtreme (MCX) – a Physically-Accurate Photon Simulator
Modeling Hydrogels and Elastomers
Modeling Breast Cancer Spread
Measuring Neutrino Mass
Investigating Mantle Flow Through Analyses of Earthquake Wave Propagation
Impact of Marine Heatwaves on Coral Diversity
IceCube: Hunting Neutrinos
Genome Forecasting
Global Consequences of Warming-Induced Arctic River Changes
Fuzzing the Linux Kernel
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
Energy Transport and Ultrafast Spectroscopy Lab
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
Cyber-Physical Communication Network Security
Avoiding Smash Hits
Analyzing the Gut Microbiome
Adaptive Deep Learning Systems Towards Edge Intelligence
Accelerating Rendering Power
ACAS X: A Family of Next-Generation Collision Avoidance Systems
Neurocognition at the Wu Tsai Institute, Yale
Computational Modeling of Biological Systems
Computational Molecular Ecology
Social Capital and Economic Mobility
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Outreach & Education Projects

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