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Quantum Chemistry Calculations for Metabolomics
Get PDFA primary goal of metabolomics studies is to fully characterize the small-molecule composition of complex biological and environmental samples. However, despite advances in analytical technologies over the past two decades, the majority of small molecules in complex samples are not readily identifiable due to the immense structural and chemical diversity present within the metabolome. Current gold-standard identification methods rely on reference libraries built using authentic chemical materials (“standards”), which are not available for most molecules. Computational quantum chemistry methods, which can be used to calculate chemical properties that are then measured by analytical platforms, offer an alternative route for building reference libraries, i.e., in silico libraries for “standards-free” identification. In this review, we cover the major roadblocks currently facing metabolomics and discuss applications where quantum chemistry calculations offer a solution. Several successful examples for nuclear magnetic resonance spectroscopy, ion mobility spectrometry, infrared spectroscopy, and mass spectrometry methods are reviewed. Finally, we consider current best practices, sources of error, and provide an outlook for quantum chemistry calculations in metabolomics studies. We expect this review will inspire researchers in the field of small-molecule identification to accelerate adoption of in silico methods for generation of reference libraries and to add quantum chemistry calculations as another tool at their disposal to characterize complex samples.A primary goal of metabolomics studies is to fully characterize the small-molecule composition of complex biological and environmental samples. However, despite advances in analytical technologies over the past two decades, the majority of small molecules in complex samples are not readily identifiable due to the immense structural and chemical diversity present within the metabolome. Current gold-standard identification methods rely on reference libraries built using authentic chemical materials (“standards”), which are not available for most molecules. Computational quantum chemistry methods, which can be used to calculate chemical properties that are then measured by analytical platforms, offer an alternative route for building reference libraries, i.e., in silico libraries for “standards-free” identification. In this review, we cover the major roadblocks currently facing metabolomics and discuss applications where quantum chemistry calculations offer a solution. Several successful examples for nuclear magnetic resonance spectroscopy, ion mobility spectrometry, infrared spectroscopy, and mass spectrometry methods are reviewed. Finally, we consider current best practices, sources of error, and provide an outlook for quantum chemistry calculations in metabolomics studies. We expect this review will inspire researchers in the field of small-molecule identification to accelerate adoption of in silico methods for generation of reference libraries and to add quantum chemistry calculations as another tool at their disposal to characterize complex samples
2/17/2021: Faculty Senate Meeting Agenda
No full textHandbook Changes: First reading: Thesis Committee updates
UCC Changes Course Change: Prerequisite changes for CS 651/661/665/682 - Approved (12,0,0) Course Change & Course Additions: Slight changes to CS 435/625 & 481/681 Add 600 level course - Approved (12,0,0) New Course: CS 483/683 Deep Learning - Returned to UCC for changes (9,2,1
2/17/2021: Faculty Senate Unapproved Meeting Minutes
No full textHandbook Changes: First reading: Thesis Committee updates
UCC Changes Course Change: Prerequisite changes for CS 651/661/665/682 - Approved (12,0,0) Course Change & Course Additions: Slight changes to CS 435/625 & 481/681 Add 600 level course - Approved (12,0,0) New Course: CS 483/683 Deep Learning - Returned to UCC for changes (9,2,1
2/17/2021: Course Change Form CS 483/683
No full textAs the need for AI in the curriculum is growing, there is a need for a class in Deep Learning. Topics such as the basics of Deep Learning, Generative Learning Networks, Deep Belief Networks, and deep Multi-layer Perceptrons are critical topics for students to understand in the fields related to AI. This course will provide a solid overview of various subjects related to deep learning. In discussions with the CE department regarding future directions in AI at KU, they agreed with the CS faculty that this would be an appropriate course to strengthen KU’s AI offerings
Adventures in the Islands - Enhancing Student Engagement in Teaching Statistics
Get PDFThe factors for enhancing student engagement frequently identified are active and problem-based learning as well as real-life experience relevant to students\u27 interests. The importance of using real data in teaching statistics has been repeatedly emphasized and its importance is growing. However, data collection, as part of a student project, faces serious practical problems. It is time-consuming, may require access to equipment, or raise ethical issues
