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  • Edward Lyman, Associate Professor

    Associate Professor
    University of Delaware
    258 Sharp Lab
    Newark, DE 19716
    (302) 831-3519


    (b. 1976 ) B.S. 1998 Pennsylvania State University; Ph.D. 2004 Virginia Polytechnic and State Institution; Postdoctoral Associate 2004-2007 Univeristy of Pittsburgh Medical School; Postdoctoral Fellow 2007-2010 University of Utah

    Current Research

    Our knowledge of the molecular basis of human physiology and disease is expanding at an unprecedented rate. Perhaps the single most salient feature to emerge from this effort is a deep appreciation for the richness of life at the molecular level — in biomedical science, details matter, and yet a cellular level of description is often needed as well. For example, the decision of a cell whether or not to divide is clearly a cell level decision, and yet that decision can be hijacked by an Ångstrom scale change in a single protein. Indeed, the challenge to the biomedical scientist is often to rationalize observations at vastly disparate scales, generating in the process predictive models of disease states, which may then guide the development of novel therapies. But where does one acquire such models?

    The multiscale architecture of human physiology is the source of its uniquely confounding complexity, which demands a new approach to model building. The “multiscale modeling” paradigm of computational science has emerged as a powerful tool to tackle such complexity. Multiscale models, as the name suggests, connect experimental data and models across a broad range of length and time scales, building a direct, predictive correspondence between the single molecule scale and the cell or tissue scale. Experimental advances, especially “ultrascale” techniques — whole cell tomography, single particle electron microscopy, and superresolution fluorescence microscopy — are now offering insights with unprecedented detail into cellular function at micron scales and beyond. These techniques together with advances inultrascale modeling will yield a new generation of hybrid methods that promise a qualitatively new way of understanding human health.

    In silicico drug discovery and design

    ​Advances in the hardware, software, and models used for molecular simulation of proteins and drug-like molecules are driving a new wave of efforts in virtual drug discovery. A major obstacle is to account for the flexibility of both ligand and receptor in such calculations, in a computationally expedient manner. A major effort in my research group is to leverage recent advances in molecular simulation for the purpose of discovering novel small molecule leads, especially targeting the family of G-protein coupled receptors.

    Lipids, sterols, and proteins: A rich mixture

    ​The morphologies of cells and the structure of cell membranes have puzzled biophysicists and biochemists for decades. An atom’s eye view would be invaluable in the effort to unravel the complex thermodynamic forces that drive cell membrane structure. While molecular simulations can in principle provide such a view (image below shows defect structure and curvature of a lipid bilayer), major hurdles prevent the direct application of “off the shelf” methods. We aim, through the development of advanced algorithms for the simulation of condensed phase systems, to make physiologically realistic mixtures of lipids, sterols, and proteins computationally tractable.

    Representative Publications

    • E. Lyman, H. Cui and G. A. Voth "Water under the BAR," Biophysical Journal, (2010) 99, 1783–1790.
    • E. Lyman, C. Higgs, B. Kim, D. Lupyan, J. C. Shelley, R. Farid and G. A. Voth "A Role for a Specific Cholesterol Interaction in Stabilizing the Apo Configuration of the Human A2A Adenosine Receptor," Structure, (2009) 17, 1660–1668.
    • E. Lyman, J. Pfaendtner and G. A. Voth "Systematic Multiscale Parameterization of Heterogeneous Elastic Network Models of Proteins," Biophysical Journal, (2008)95, 4183–4192.
    • E. Lyman and D. M. Zuckerman "On the Structural Convergence of Biomolecular Simulations by Determination of the Effective Sample Size," J. Phys. Chem. B, (2007) 111, 12876-12882.
    • E. Lyman, F. M. Ytreberg and D. M. Zuckerman "Resolution Exchange Simulation," Physical Review Letters, (2006) 96, 028105.
    • D. M. Zuckerman and E. Lyman "A Second Look at Canonical Sampling of Biomolecules Using Replica Exchange Simulation," J. Chem. Theory Comput., (2006) 2, 1200-1202.



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  • Chemistry and Biochemistry
  • 102 Brown Laboratory
  • University of Delaware
  • Newark, DE 19716, USA
  • Phone: 302-831-1247