Department of Biomedical Engineering
John A. White, Jr. Engineering Hall
790 W. Dickson St. Suite 120
Fayetteville, AR 72701
Phone: 479-575-4667
Fax: 479-575-5619
Molecular Engineering
Dr. Qian's Laboratory
Primary Investigator: Xianghong Qian
Dr. Qian’s major research interests focus on investigating the fundamental processes involved in carbohydrate chemistry, smart polymers for drug delivery and protein purifications, virus clearance, protein folding and misfolding using complementary experimental and theoretical and computational tools. Ab initio and classical molecular dynamics simulation methods combined with static quantum mechanical calculations are used to elucidate the many underlying chemical and biochemical processes at the molecular level. She is the recipient of 2009 NSF CAREER award and has over 70 peer-reviewed journal articles and book chapter publications.
Precision Genome Engineering Laboratory
Primary Investigator: Christopher Nelson, Ph.D.
The goal of our research group is to develop next-generation gene therapies for debilitating and fatal genetic diseases. Specifically, we are interested in developing safe and effective strategies to deliver CRISPR/Cas9 to target cells and tissues. We are also interested in applying genome engineering to control aberrant gene expression. For more information, please contact nelsonc@uark.edu or visit our website.
Computational Systems Biology Laboratory
Primary Investigator: Leonard A. Harris, Ph.D
The Computational Systems Biology Laboratory is an interdisciplinary research group that focuses on constructing detailed, mechanistic models of intracellular signaling pathways and cell-cell interactions in biological systems, particularly cancer. We work closely with experimental collaborators to build, experimentally validate, and refine computational models to aid in the interpretation of experimental data and drive future experiments that perturb system behavior. Our primary area of interest is cancer, where we combine chemical kinetics, computational science, bioinformatics, high-performance computing, and engineering principles to explore the root causes of tumor heterogeneity and acquired drug resistance in a variety of cancer types, including lung cancer, melanoma, and bone-metastatic breast cancer. The ultimate goal is to use computational models as in silico platforms to identify novel therapeutic targets that can increase anticancer treatment effectiveness and improve patient outcomes.