Research Interests:

• Modelling and controlling metabolic dynamics and regulation (metabolic engineering)
• Systems biology-based experimental and bioinformatics analysis of metabolism
• Synthetic biology for the development of biosensors and diagnostics

The main focus of the Styczynski group is the experimental and computational study of the dynamics and regulation of metabolism, with ultimate applications in metabolic engineering, biotechnology, and biosensors/diagnostics.

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 via “metabolomics”: measurement of the concentrations of the biochemical intermediates in that network — sugars, amino acids, etc. — that are direct, real-time readouts of cellular state. Tracking these intermediates over time reveals details about the cell’s metabolic dynamics that may then be used for modeling and analysis of metabolism. The group works with a variety of systems, including cancer cells, stem cells, yeast, and E. coli. 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 significant efforts in synthetic biology, including its use in the context of metabolic engineering. They are currently developing the underlying technology for diagnostics that use bacteria as biosensors that generate pigments as a visible readout. This application requires significant metabolic engineering of the cells to precisely control their pigment production, in terms of both time and pathway utilization.

Finally, the Styczynski group 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), includes multiple disparate types of data, and is often in time-course format, making it challenging to interpret. Group members use bioinformatics and machine learning to explore and exploit data.