Our research interests lie in understanding molecular-level phenomena governing complex biological processes and material science problems using theory and computer simulation techniques.
Polymer based Gene Delivery
Gene therapy is the deliberate introduction of therapeutic DNA into target cells, a process called transfection. Viral delivery agents, while effective at transfection, can elicit dangerous immunogenic responses. Non-viral gene delivery agents, on the other hand, are not as effective at transfection as viral vectors, but have the advantage of being non-immunogenic. Polycations have emerged as promising non-viral delivery agents due to their propensity to bind the polyanionic DNA backbone, neutralizing the charge of the polymer-DNA complex and facilitating endocytosis. Numerous polycations with differing efficacies have been synthesized, but structure-function relationships for these transfection agents are not yet apparent. In one project, we use simulations to reveal the molecular-level interactions in polycation-DNA complexes and elucidate the thermodynamics behind why some polycations are better transfection agents than others. In another project, we use simulations to design multivalent ligands for plasmid-DNA purification, in order to produce pure plasmid DNA for gene therapy.
Experimental Collaborator: (non-viral gene delivery) Dr. Todd Emrick, U Mass Amherst
Experimental Collaborator: (multivalent ligand design for bioseparations) Dr. Kaushal Rege, Arizona State
DNA damage- recognition and repair:
Nanomaterials, such as titanium dioxide (TiO2) nanoparticles and carbon nanotubes produce reactive oxygen species upon exposure to UV radiation, and cause DNA damage in living things. High rates of cancer are associated with the diminished capacity of the cells to repair the DNA damage. Ironically, the drugs that are used to treat cancer are designed to damage the DNA in order to inhibit continuous cell growth. Concurrently, increased repair of DNA damage caused by an anti-cancer drug can lead to tumor resistance to that drug. It is thus important to develop a better understanding of the cell's ability to repair DNA damage, not only to prevent malignancy, but also to avoid development of tumor resistance to current cancer therapies. Our research involves molecular simulation studies of protein-DNA complexes: 1) to explain how DNA structure, solvent and electrostatics influence the ability of repair proteins to recognize and repair DNA damage sites, and 2) to study the role of repair proteins in the development of tumor resistance to anti-cancer drugs.
Polymers and Nanomaterials
We use computer simulations and theory to study the phase behavior of functionalized particles in a solvent or a polymer matrix, specifically for the design of metamaterials and organic photovoltaics. Our goal is to connect molecular features of the constituents (e.g. chemistry, flexibility, polydispersity, etc.) to the morphology of the polymer nanocomposite.
Experimental Collaborator: (Functionalized Nanoparticles in Polymer Nanocomposites) Dr. Ramanan Krishnamoorti, University of Houston Experimental Collaborator: (Functionalized Nanoparticles for Metamaterials) Dr. Won Park, University of Colorado-Boulder Experimental Collaborator: (Organic PV) Dr. Garry Rumbles, NREL
Methods we use to conduct these studiesWe use statistical thermodynamics, computer simulations and theory to study the problems stated above. Here is some background reading that describe these tools.
- Allen, M.P. and Tildesley, D.J. (1987). Computer Simulation of Liquids. Oxford University Press. ISBN 0-19-855645-4.
- Frenkel, D. and Smit, B. (2001). Understanding Molecular Simulation. Academic Press. ISBN 0-12-267351-4.
- Binder, K. and Heermann, D.W. (2002). Monte Carlo Simulation in Statistical Physics. An Introduction (4th edition). Springer. ISBN 3-540-43221-3.
- Monte Carlo simulations
- Molecular dynamics simulations
- Brownian dynamics simulations
- Polymer Reference Interaction Site Model (PRISM) theory
- Molecular Dynamics with CHARMM
- Fortran routines for computer simulations - by Allen and Tidesley