Mark Styczynski
Assistant Professor
Contact Information
Building: Ford ES&T
Office:
L1222
Phone: 404.894.2825
Fax: 404.385.2713
email
Mailing Address
Georgia Institute of Technology
School of Chemical &
Biomolecular Engineering
311 Ferst Drive, N.W.
Atlanta, GA 30332-0100
Links
Mark Styczynski
Education
B.S. 2002, University of Notre Dame
Ph.D. 2007, Massachusetts Institute of Technology
Research Interests
The unifying theme of the Styczynski lab is the study of the dynamics and regulation of metabolism, with ultimate applications in metabolic engineering, biotechnology, biofuels, and drug development. Group members use high-throughput analytical techniques, coupled with computational modeling and statistical analysis, to learn how cellular metabolism behaves and how it is regulated, and then to attempt to control those metabolic behaviors.
Metabolism, which is the process of cells taking in nutrients and turning them into energy and the building blocks for more cells, is at the core of many biotechnological processes, as well as numerous diseases. The Styczynski lab studies the network of reactions that constitutes metabolism by measuring the concentrations of the biochemical intermediates in that network—sugars, amino acids, etc.—as direct, real-time readouts of cellular state. Using chromatography coupled to mass spectrometry, the Styczynski lab tracks the concentrations and turnover rates of metabolites, revealing details about the cell’s metabolic dynamics that may then be used for modeling and analysis of metabolism.
The Styczynski lab works on a variety of systems, including cancer cells, stem cells, and yeast cells. The ultimate aim is to use an increased understanding of metabolic dynamics in order to exert control over the cells, whether by keeping cancer cells from proliferating or by metabolic engineering of yeast to overproduce valuable chemical feedstocks. The group also has an interest in synthetic biology, including its use in the context of metabolic engineering.
Finally, the Styczynski lab uses extensive computational modeling and bioinformatics analysis in order to analyze and interpret data. The data generated in the lab is high-dimensional (many variables) and often in time-course format, so it is challenging to interpret. Group members use standard analysis techniques (clustering, PCA), plus more detailed machine learning and modeling techniques (e.g., Bayesian networks) to explore and exploit data. The Styczynski lab also has significant interest in integrating multiple disparate data types —for example, metabolite concentrations and transcriptional levels —for a fuller, systems-level understanding of the system.