Jie Liang, Ph.D. Bioengineering Primary Grant Support: National Science Foundation Career Award, National Institutes of Health R01, Office of Naval Research, and the Whitaker Foundation Protein surface matching
Problem Statement and Motivation •
The structure of proteins provide rich information about how cells work. With the success of structural genomics, soon we will have all human proteins mapped to structures.
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However, we need to develop computational tools to extract information from these structures to understand how cell works and how new diseases can be treated.
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Therefore, the development of computational tools for surface matching and for function prediction will open the door for many new development for health improvement.
Evolution of function
Key Achievements and Future Goals
Technical Approach • • •
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We use geometric models and fast algorithm to characterize surface properties of over thirty protein structures. We develop evolutionary models to understand how proteins overall evolve to acquire different functions using different combination of surface textures. Efficient search methods and statistical models allow us to identify very similar surfaces on totally different proteins Probablistc models and sampling techniques help us to understand how protein works to perform their functions.
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We have developed a web server CASTP (cast.engr.uic.edu) that identify and measures protein surfaces. It has been used by thousands of scientists world wide. We have built a protein surface library for >10,000 proteins, and have developed models to characterize cross reactivities of enzymes. We also developed methods for designing phage library for discovery of peptide drugs. We have developed methods for predicting structures of beta-barrel membrane proteins. Future: Understand how protein fold and assemble, and designing method for engineering better proteins and drugs.
Prof. Andreas A. Linninger Funding by: NSF CBET 1010621, NSF RET EEC 1132694
Problem Statement and Motivation • Problem: Drug delivery in the Central Nervous System (CNS) is especially challenging due to the Blood Brain Barrier (BBB) and ranging cell types • IT-nanoparticle based drug delivery can circumvent the BBB • MDT reduces systemic toxicity by localizing drug concentration
• Improve patient outcomes and efficacy of therapeutics due to reduced drug dosages
Technical Approach • Develop gold coated magnetite nanovehicle for conjugated delivery • •
Targeted drug treatment; doxorubicin Secondary imagining modality; quantum dots
• In vitro cell culture studies: • •
Analyze nanovehicle uptake, efficacy, and toxicity Develop 3D live cell model of spinal cord tumor
• In silico human subject model: • •
Predict distribution of IT drug infusion Optimize magnet position and strength for guided localization of nanovehicle to a priori target regions
• In vivo model: • •
Use optimized parameters for MDT of specific tumor cell region Prove tumor cell death by reduced tumor size in the animal model using imaging techniques such as MRI
Key Achievements and Future Goals • Synthesis and characterization of nanoparticle-drug complex • Predict IT drug dispersion using CFD models with varying magnetic field configurations • Design live 3D spine tumor cell model for in vitro analysis MDT treatment using the drug delivery vehicle • Localized targeting of spinal cord tumors in the animal model to prove feasibility of in vivo MDT.
Hui Lu, Bioengineering Primary Grant Support: NIH, DOL
Problem Statement and Motivation Protein-DNA complex: gene regulation DNA repair cancer treatment drug design gene therapy
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Protein interacts with other biomolecules to perform a function: DNA/RNA, ligands, drugs, membranes, and other proteins.
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A high accuracy prediction of the protein interaction network will provide a global understanding of gene regulation, protein function annotation, and the signaling process.
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The understanding and computation of protein-ligand binding have direct impact on drug design.
Key Achievements and Future Goals
Technical Approach •
Data mining protein structures
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Molecular Dynamics and Monte Carlo simulations
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Machine learning
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Phylogenetic analysis of interaction networks
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Gene expression data analysis using clustering
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Binding affinity calculation using statistical physics
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Developed the DNA binding protein and binding site prediction protocols that have the best accuracy available. Developed transcription factor binding site prediction. Developed the only protocol that predicts the protein membrane binding behavior. Will work on drug design based on structural binding. Will work on the signaling protein binding mechanism. Will build complete protein-DNA interaction prediction package and a Web server.
Hui Lu, Ph.D., Bioengineering Primary Grant Support: Chicago Biomedical Consortium, NIH
Problem Statement and Motivation •
To efficiently function, cells need to respond properly to external physical and physical and chemical signals in their environment.
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Identifying disease states and designing drugs require a detailed understanding of the internal signaling networks that are activated in responses to external stimuli.
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In the center of these process is a particular group of protein that translocate to the cell membrane upon external activation.
Key Achievements and Future Goals
Technical Approach •
Combine machine learning techniques with characterization of the protein surface to identify unknown membrane binding proteins.
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Developed highly accurate prediction protocols for identifying novel cases of membrane binding proteins, based on properties calculated from molecular surface of the protein structure.
Atomic scale molecular dynamics simulation of the interactions between proteins and membranes
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Mathematical modeling is used for studying the spatial and dynamic evolution of the signal transduction networks within the cell when changes in the external environment occurs.
Determining membrane binding of properties of C2 domains in response to changes in ion placements and membrane lipid composition.
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Goal: To model the network dynamics to understand how changes in membrane binding properties of certain domains changes the efficiency of signal transduction in the cell.
Hui Lu, Ph.D., Robert Ezra Langlois, Ph.D.,Bioengineering; Primary Grant Support: NIH, Bioinformatics online
Problem Statement and Motivation •
Massive amount of biomedical data are available from high-throughput measurement, such as genome sequence, proteomics, biological pathway, networks, and disease data.
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Data processing become the bottleneck of biological discovery and medical analysis
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Problem: Protein function prediction, protein functional sites prediction, protein interaction prediction, disease network prediction, biomarker discovery.
Key Achievements and Future Goals
Technical Approach •
Formulate the problem in classification problem
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Developed machine learning algorithms for protein-DNA, proteinmembrane, protein structure prediction, disease causing SNP prediction, mass-spec data processing, DNA methylation prediction.
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Derive features to represent biological objects
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Develop various classification algorithms
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Developed an open-source machine learning software MALIBU
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Develop multiple-instance boosting algorithms
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Goal: Biological network analysis and prediction.
Andreas A. Linninger Department of Bioengineering
Problem Statement and Motivation Pressure (mmHg)
Flow (uL/min)
16
6
15
5
14
5
13
4
12
3
11
2
10
1
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Cerebrovascular disease (CVD) occurs in 40% of the U.S. population costing over $8.5 billion per year and is the 4th leading cause of death
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Analysis of perfusion maps obtained from MRI and CT do not reliably assess patient health
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Oxygen perfusion of tissue at microvasculature scale is uncertain due to limited resolution of imaging modalities
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May help physicians in surgery planning
Key Achievements and Future Goals
Technical Approach •
Centerline extraction from high-resolution medical images for apparent vessel modeling
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Arterial networks automatically reconstructed from medical MR angiography images
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Constrained constructive optimization creates morphologically consistent microvasculature
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Microvasculature model of for oxygen perfusion
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Modeling of the cerebrovasculature provides simulation results down to capillary level and can be used as a diagnostic and surgery planning tool
Artificial angiography from simulated dye convection and registration of dye projection to voxel matrix
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Acquiring high-resolution images for better reconstruction for both arterial and venous network
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Oxygen perfusion from capillaries to brain cells
Andreas A. Linninger Department of Bioengineering NSF CBET Process & Reactions Engineering Pressure, mmHg
pO2, mmHg
66
80
56
70
47
60
37
50
28
40
18
30
Problem Statement and Motivation •
Cerebrovascular Disease (CVD) occurs in 40% of the U.S. population costing over $8.5 billion per year and is the 4th leading cause of death
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Analysis of MR and CT images do not reliably capture the dynamics of tissue oxygen perfusion in response to vasomodulating factors
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Oxygen perfusion of tissue at microvasculature scale is uncertain due to limited resolution of imaging modality and difficulty of obtaining in vivo human measurements
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Modeling of the cerebrovasculature on the tissue scale provides missing meso-scale simulations between large cerebral vessels and capillary perfusion of brain cells
Key Achievements and Future Goals
Technical Approach •
Centerline extraction from high-resolution medical images for apparent vessel modeling coupled with constrained constructive optimization (CCO) to create morphologically consistent microvasculature
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Arterial and venous networks automatically reconstructed from medical MR angiography images and enhanced with space filling CCO algorithm to create morphologically accurate networks
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Biphasic blood rheology computed using novel kinetic plasmaskimming model to accurately predict oxygen distribution to deep cortical layers
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Microvasculature model of RBC distribution shows network-level hemoconcentration due to plasma skimming, increasing oxygen perfusion predictions over one phase models
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3D anisotropic oxygen perfusion from capillaries to neural and glial brain cells computed as a function of red blood cell distribution, vessel tone, and extracellular space geometry
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High-resolution images of arteriole, venule, capillary vessels as well as the spatial distribution of neurons and glial cells will be used to build more realistic tissue-level simulations
Andreas Linninger Prime Grant Support: NSF CBET EAGER
Problem Statement and Motivation • Cerebral vascular disease is the 4th leading cause of death in the U.S. • Mostly treated through digital subtraction angiography (DSA) • Currently, no method exists for quantifying blood flow in DSA
Technical Approach
Key Achievements and Future Goals
• Anatomical reconstruction of cerebral vasculature through image processing
• Pipelining of the image processing procedure for anatomical features
• Contrast agent convection simulation using computational modeling
• Development of optimization algorithm based on flow principle algorithms
• Optimization algorithm for blood flow measurement
• Artificial DSA projections for objective function in the optimization algorithm