The goal of the proposed work is to develop species-specific, dynamic, genome-scale metabolic models for sixteen different yeast species. Metabolism is the set of processes that catalyze the production of energy and cellular building blocks from the nutrients a cell takes up from the environment. The building blocks and energy that metabolism creates are critical for all cellular functions, from transcription to cell division. The structure of the metabolic network is surprisingly well-conserved across evolution, yet different species exhibit different metabolic characteristics through different regulation and utilization of the same reactions. Understanding how the same set of reactions can be used to generate such different metabolic behaviors, and in turn understanding some of the evolutionary underpinnings that lead to these different behaviors, would be beneficial for a variety of biomedical and biotechnological tasks. In this work, we leverage our recently-developed approaches for analysis and processing of metabolomics data and for creation of genome-scale, dynamic linear models based on metabolomics data to study the metabolism of sixteen different yeast species. We will measure the concentrations of metabolites for all of these species in time-course experiments in response to environmental and genetic perturbations. We will then use these data as the basis for creation of species-specific metabolic models, using our novel metabolic modeling approach. We will also further improve on those approaches to better enable the construction of these metabolic models. We will then use all of these models in a comparative context to study which metabolic regulatory and dynamic behaviors are conserved across evolution and which ones are more variable, and will use multiple methods to validate the regulatory interactions that our model predicts. This project will allow for fundamental insights into how metabolism behaves and is controlled across closely and distantly related yeast species, including two opportunistic pathogens and two sets of species exhibiting a metabolic phenotype very similar to that of cancer cells. Just like many biological principles were first established in yeast species before being confirmed in higher eukaryotes, a better understanding of the evolution of yeast metabolism will provide broad principles of regulation that can be brought to bear on understanding and modeling human metabolism. Moreover, successful completion of this work could provide a deeper understanding of the evolution of pathogenicity and its functional underpinnings, as well as an understanding of pathological changes in metabolism in human disease, such as diabetes or cancer.

Public Health Relevance

This work performs computational and experimental work needed to develop dynamic, genome- scale metabolic models for a variety of yeast. Understanding the evolution and conservation of metabolite dynamics and regulation across this set of yeasts could allow for more sophisticated manipulation of organisms for biotechnological ends, insight into metabolic behaviors common even among cancer cells, and even insight into the relationship between metabolism and pathogenicity.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Unknown (R35)
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Special Emphasis Panel (ZRG1)
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Resat, Haluk
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Georgia Institute of Technology
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Biomed Engr/Col Engr/Engr Sta
United States
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McNerney, Monica P; Styczynski, Mark P (2018) Small molecule signaling, regulation, and potential applications in cellular therapeutics. Wiley Interdiscip Rev Syst Biol Med 10:
Watstein, Daniel M; Styczynski, Mark P (2018) Development of a Pigment-Based Whole-Cell Zinc Biosensor for Human Serum. ACS Synth Biol 7:267-275
McNerney, Monica P; Styczynski, Mark P (2017) Precise control of lycopene production to enable a fast-responding, minimal-equipment biosensor. Metab Eng 43:46-53