Project 1 focuses on the fundamental challenge of integrating different levels of information comprising different molecular species and operating across different time scales. In particular, two model systems (yeast and halobacterium) will be exploited to develop approaches for understanding the principles and rules governing how information from the environment is propagated to coordinate multiple responses and complex phenotypes. We will use models that traverse across signaling, gene regulatory and metabolic networks. Tools and insights developed through this project will be applicable to all biological system developmental, physiological and structural. Moreover, the malfunction of signaling processes leads to phenotypic and metabolic changes underlying human disease, including cancers, neuropathies, myopathies and diabetes. Understanding the dynamic nature of these networks is a window into understanding disease states and rational strategies for effective interventions the basis of a systems view of medicine.
Mast, Fred D; Herricks, Thurston; Strehler, Kathleen M et al. (2018) ESCRT-III is required for scissioning new peroxisomes from the endoplasmic reticulum. J Cell Biol 217:2087-2102 |
Pacheco, Derek; Warfield, Linda; Brajcich, Michelle et al. (2018) Transcription Activation Domains of the Yeast Factors Met4 and Ino2: Tandem Activation Domains with Properties Similar to the Yeast Gcn4 Activator. Mol Cell Biol 38: |
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Tuttle, Lisa M; Pacheco, Derek; Warfield, Linda et al. (2018) Gcn4-Mediator Specificity Is Mediated by a Large and Dynamic Fuzzy Protein-Protein Complex. Cell Rep 22:3251-3264 |
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Holden, Jennifer M; Koreny, Ludek; Obado, Samson et al. (2018) Involvement in surface antigen expression by a moonlighting FG-repeat nucleoporin in trypanosomes. Mol Biol Cell 29:1100-1110 |
Hoopmann, Michael R; Winget, Jason M; Mendoza, Luis et al. (2018) StPeter: Seamless Label-Free Quantification with the Trans-Proteomic Pipeline. J Proteome Res 17:1314-1320 |
Slama, Patrick; Hoopmann, Michael R; Moritz, Robert L et al. (2018) Robust determination of differential abundance in shotgun proteomics using nonparametric statistics. Mol Omics 14:424-436 |
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