The STMC Data Analysis and Informatics Core has three principal goals: 1) to improve the mathematical and statistical analysis of quantitative image data generated through both electron and fluorescence microscopy in order to test predicitive models linking membrane organization and dynamics to the control of signaling;2) to provide expertise and computational resources in information acquisition, representation, storage and exchange;and 3) to facilitate sharing and dissemination of STMC-generated data and models. The Data Analysis and Informatics Core will contribute to the state-of-the-art for biomedical image storage, retrieval and analysis and will integrate image data with biological pathways and networks data and with mathematical and statistical models. The STMC Data Anaysis and Informatics Core will operate as a subcore of the CRTC's Bioinformatics and Computational Biology Core (BCB) and will have full access to the expertise and effort of its Director, Dr. Susan Atlas, and professional staff. Through this arrangement, STMC users access a highly functional computational core at a small fraction of its actual cost. The resource is physically located in the UNM Center for High Performance Computing (CHPC), a campus wide facility for computing and data base management and development. As one of five resident Resources of the CHPC, the BCB has highest priority access to its hardware that includes an 8-processor parallel Oracle server, Delphi, a 512-processor Linux supercluster and a new (2005) 20-processor shared-memory IBM supercomputer. The resource has an additional 220 nsf office on the first floor of CRF (immediately below the STMC administrative office) that provides office space and computers for consultation and interactive research. Images of live and fixed cells, including time series images, are critical tools for the biology driving several STMC projects. Initial research will therefore focus on developing, improving and populating image databases and on upgrading an interoperable environment to represent, store, retrieve and share these images among the researchers. The core will additionally provide links to pathways data bases and it will import new bioinformatics and computational biology approaches to the quantitative analysis and modeling of cell signaling and image analysis data established by other groups and centers. Additionally, it will disseminate STMC models and codes to other users.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Specialized Center (P50)
Project #
Application #
Study Section
Special Emphasis Panel (ZGM1-CBCB-4)
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of New Mexico
United States
Zip Code
Termini, Christina M; Gillette, Jennifer M (2017) Tetraspanins Function as Regulators of Cellular Signaling. Front Cell Dev Biol 5:34
Harmon, Brooke; Chylek, Lily A; Liu, Yanli et al. (2017) Timescale Separation of Positive and Negative Signaling Creates History-Dependent Responses to IgE Receptor Stimulation. Sci Rep 7:15586
Schwartz, Samantha L; Cleyrat, Cédric; Olah, Mark J et al. (2017) Differential mast cell outcomes are sensitive to Fc?RI-Syk binding kinetics. Mol Biol Cell 28:3397-3414
Kocha?czyk, Marek; Kocieniewski, Pawe?; Koz?owska, Emilia et al. (2017) Relaxation oscillations and hierarchy of feedbacks in MAPK signaling. Sci Rep 7:38244
Mrass, Paulus; Oruganti, Sreenivasa Rao; Fricke, G Matthew et al. (2017) ROCK regulates the intermittent mode of interstitial T cell migration in inflamed lungs. Nat Commun 8:1010
Medina, Christopher S; Manifold-Wheeler, Brett; Gonzales, Aaron et al. (2017) Automated Computational Processing of 3-D MR Images of Mouse Brain for Phenotyping of Living Animals. Curr Protoc Mol Biol 119:29A.5.1-29A.5.38
Graus, Matthew S; Neumann, Aaron K; Timlin, Jerilyn A (2017) Hyperspectral fluorescence microscopy detects autofluorescent factors that can be exploited as a diagnostic method for Candida species differentiation. J Biomed Opt 22:16002
Mahajan, Avanika; Youssef, Lama A; Cleyrat, Cédric et al. (2017) Allergen Valency, Dose, and Fc?RI Occupancy Set Thresholds for Secretory Responses to Pen a 1 and Motivate Design of Hypoallergens. J Immunol 198:1034-1046
Freed, Daniel M; Bessman, Nicholas J; Kiyatkin, Anatoly et al. (2017) EGFR Ligands Differentially Stabilize Receptor Dimers to Specify Signaling Kinetics. Cell 171:683-695.e18
Cleyrat, Cédric; Girard, Romain; Choi, Eun H et al. (2017) Gene editing rescue of a novel MPL mutant associated with congenital amegakaryocytic thrombocytopenia. Blood Adv 1:1815-1826

Showing the most recent 10 out of 123 publications