Training Strengths

Training Strengths
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Center for Biological Chemistry Graduate Program

broadening your perspective

Our program is positioned at the interface of the physical sciences (bioinorganic, bio-organic, biophysics), the biological sciences (genetics and molecular biology), and the applied sciences (biotechnology). Such breadth in expertise provides a rich training environment that encourages creative interdisciplinary problem-solving and discovery. Our Department fosters growth of productive students into successful career professionals. Within the below training areas, faculty and students are investigating a variety of experimental questions relevant to basic, agricultural, and biomedical research.

Structural and Chemical Basis of Protein Function

Structural and Chemical Basis of Protein Function





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Metabolism and Metabolic Engineering





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Metabolism and Metabolic Engineering
Molecular Mechanisms of Disease

Molecular Mechanisms of Disease

The NIH-funded Molecular Mechanisms of Disease (MMoD) predoctoral training program provides innovative training and career development in biomedical research centered on molecular interactions and chemical transformations. The goal of the program is to develop outstanding new scientists who work in collaborative multi-disciplinary teams to research disease mechanisms using quantitative approaches that ultimately yield tangible strategies for prevention and therapy.

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Plant and Microbial Biochemistry and Biotechnology

Researchers use their expertise in biochemistry, genetics, and molecular biology to understand metabolism and development of plants and microbes. Basic science discoveries are translated into commercial products and processes such as increased crop nutrition, higher biofuel yields, or generating drugs and chemicals from renewable feedstocks.

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Plant and Microbial Biochemistry and Biotechnology
Computational and Systems Biology

Computational and Systems Biology

Computational and Systems Biology in the Department of Biochemistry is aimed at discovering patterns in cell metabolism, signalling, stress response and disease using computer science and information theory.

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