Collaborative Projects

Principle investigator: Dr. Jian Chen, School of Computing, University of Southern Mississippi

Collaborators:

  • Dr. Preetam Ghosh and Dr. Chaoyang (Joe) Zhang, School of Computing, the University of Southern Mississippi
  • Dr. Robert Hester, Department of Physiology, University of Mississippi Medical Center
  • Dr. Bindu Nanduri, College of Veterinary Medicine, Mississippi State University

Research Focus Areas: Computational Biology (CompBio) and Biological Systems Simulation (BioSim)

Developing models of biological systems is a complex process that requires integration of data from multiple sources and the use of many different analysis steps. This collaborative research brings together computer scientists from the University of Southern Mississippi (USM), physiologists from the University of Mississippi Medical Center (UMMC), and biologists from Mississippi State University (MSU) to develop new approaches for analyzing and visualizing time-varying data for improved knowledge discovery. The goal is to help interpret the complex parameter space: such as those in HumMod and in the Yeast datasets. Our approach is to integrate depicting and embedding algorithms that will allow scientists to quickly perceive pattern changes, thus optimizing the knowledge discovery process. The outcome will be taxonomy and tools for time-varying graph visualization; the results will be disseminated through open-source software, experimental data, publications, presentations, and classroom educations via the project website https://sites.google.com/site/timevaryingbiovis/.

Principle investigator: Bindu Nanduri, College of Veterinary Medicine, Mississippi State University

Co-Principal Investigators:

  • Shane Burgess, College of Veterinary Medicine, Mississippi State University
  • Mariola Edelmann, Life Sciences and Biotechnology Institute, Mississippi State University

Collaborators:

  • Keith Walters, Department of Mechanical Engineering, Mississippi State University
  • Dr. Robert Hester, Department of Physiology, University of Mississippi Medical Center
  • Dr. Nan Wang, School of Computing, University of Southern Mississippi
  • Dr. Raphael Isokpehi, Department of Biology, Jackson State University

Research Focus Areas: Computational Biology (CompBio) and Biological Systems Simulation (BioSim)

Nanoparticles have gained wide use in many industrial applications and have the potential for even wider application. However, little is known about the impact of these nanoparticles on human health. This is of particular concern in urban and industrial settings where pollution levels for particulate matter can be very high. Research that links the molecular mechanisms responsive to nanoparticle exposure to whole human body physiology is critical for generating predictive models for human exposure. Researchers from CompBio are collaborating with researchers from BioSim to fill this knowledge gap. CompBio researchers will use gene and protein expression data to model the effects of nanoparticles in the context of biological networks. The results of the network analysis will be integrated into the DigitalLung framework being developed by the BioSim group.

Principle investigator: Keisha Walters, Swalm School of Chemical Engineering, Mississippi State University

Co-Principal Investigators:

  • Greg Tschumper, Department of Chemistry and Biochemistry, University of Mississippi
  • Rebecca Toghiani, Swalm School of Chemical Engineering, Mississippi State University

Research Focus Areas: Computational Chemistry (CompChem) and Biological Systems Simulation (BioSim)

The BioSim research group is developing a multi-scale model of particle deposition in the human lung (DigitalLung). This model will have many applications including modeling the inhalation of asthma medications and environmental nanomaterials. Accurate modeling of particle deposition in the lung requires knowledge of how these particles interact with the mucus layer in the lung. Although the properties of inhaled particles can be determined using clinical and experimental approaches, these methods are prohibitively costly and time-consuming. In this project, predictive computational chemistry models will be developed that relate nanoparticle chemistry to experimentally determined transport parameters. These parameters will be used to tailor the DigitalLung model for specific types of particles.

Principle investigator: Dr. Robert Doerksen, Department of Medicinal Chemistry, University of Mississippi

Co-Principal Investigators:

  • Dr. Yixin Chen, Department of Computer and Information Sciences, University of Mississippi
  • Dr. Dawn Wilkins, Department of Computer and Information Sciences, University of Mississippi
  • Dr. Raphael Isokpehi, Department of Biology, Jackson State University
  • Dr. Nan Wang, School of Computing, University of Southern Mississippi

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Research Focus Areas: Computational Chemistry (CompChem) and Computational Biology (CompBio)

Protein kinases are an important class of proteins which bind to and modify a variety of other proteins. The overall goal of this project is to use computational methods to better understand how protein kinases work. Dr. Robert Doerksen has previously conducted an extensive study of the structure and function of this important class of proteins and the relationship of structure to function. In the current study, Dr. Chen and Dr. Wilkins will work with Dr. Doerksen to apply new machine learning methods to prepare better models of the interaction of protein kinases and other molecules. Dr. Doerksen and Dr. Wang, with assistance from Dr. Chen and Dr. Wilkins, will use computational algorithms to analyze patterns in protein-kinase structure and protein-protein interaction networks with a focus on the relationship of human protein kinases to their protein substrates. Dr. Doerksen and Dr. Isokpehi will analyze protein sequence data of proteins that contain both the universal stress protein domain and the kinase domain to predict the structure and function of the domains.

Non-technical title: Using computational chemistry to improve protein identification for biological modeling

Principle investigator: Dr. Tibor Pechan, Life Sciences and Biotechnology Institute, Mississippi State University

Co-Principal Investigator: Dr. Steve Gwaltney, Department Chemistry, Mississippi State University

Research Focus Areas: Computational Chemistry (CompChem) and Computational Biology (CompBio)

Systems biology modeling requires knowledge of the proteins that are present in a biological system. Current methods for identifying proteins from biological samples using high throughput mass spectrometry make use of only the mass from the spectral data and either ignore the intensities of spectra or employ simple empirical rules to account peak intensities. This project will use quantum mechanical calculations to identify suitable computational methods for predicting spectra intensities. These methods will then be used as a validation tool for individual protein identifications with the long-term goal of building a virtual predicted spectral library to identify proteins with high confidence. Improvements in protein identification will then lead to the ability to build higher quality models of biological systems.

Principle investigator: Dr. John Correia, Department of Biochemistry, University of Mississippi Medical Center

Collaborator: Dr. Charles McCormick, School of Polymers and High Performance Materials, University of Southern Mississippi

Research Focus Areas: Computational Chemistry (CompChem)

Dr. Charles McCormick’s group at the University of Southern Mississippi has developed a novel method to produce a variety of self-assembling nanomaterials that can potentially be used for inhaled drug delivery. The behavior of these particles upon inhalation will depend on their size distribution. Dr. Correia will use sedimentation velocity measurements to accurately measure the size distribution of these nanoparticles in the presence of gold and small interfering RNA. These measurements will in turn be used in computational chemistry models to simulate the behavior of the nanomaterials and predict the biological availability.