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• W2891154289 abstract "Simulation optimization is attracting increasing interest within the modeling and simulationresearch community. Although much research e ort has focused on how to apply a varietyof simulation optimization techniques to solve diverse practical and research problems,researchers nd that existing optimization routines are di cult to extend or integrate andoften require one to develop their own optimization methods because the existing ones areproblem-speci c and not designed for reuse. A Semantically Enriched Environment for SimulationOptimization (SEESO) is being developed to address these issues. By implementinggeneralized semantic descriptions of the optimization process, SEESO facilitates reuse of theavailable optimization routines and more e ectively captures the essence of di erent simulationoptimization techniques. This enrichment is based on the existing Discrete-eventModeling Ontology (DeMO) and the emerging Simulation oPTimization (SoPT) ontologies.SoPT includes concepts from both conventional optimization/mathematical programmingand simulation optimization. Represented in ontological form, optimization routines canalso be transformed into actual executable application code (e.g., targeting JSIM or Scala-Tion). As illustrative examples, SEESO is being applied to several simulation optimizationproblems.Mass spectrometry (MS) has emerged as the preeminent tool for performing quantitativeglycomics analysis. However, the accuracy of these analyses is often compromised by theinstrumental artifacts, such as low signal to noise ratios and mass-dependent di erential ionresponses. Methods have been developed to address some of these issues by introducingstable isotopes to the glycans under study, but these methods require robust computationalmethods to determine the abundances of various isotopic forms derived from di erent experimental sources. An automated simulation framework for MS-based quantitative glycomics, GlycoQuant, is proposed and implemented to address these issues. Instead of manipulating the experimental data directly, GlycoQuant simulates the experimental data based on a glycan's theoretical isotopic distribution and takes various forms of error sources into consideration. It has been applied to analyze the MS raw data generated from IDAWG(TM) experiments and obtained satisfactory results in the estimation of (1) the ratio of relative abundances of 15N-enriched and natural abundance glycans in a mixture and (2) the 50% degradation time of 15N-enriched glycan and its remodeling coe cient at this time point." @default.
• W2891154289 created "2018-09-27" @default.
• W2891154289 creator A5034650648 @default.
• W2891154289 date "2012-08-01" @default.
• W2891154289 modified "2023-09-24" @default.
• W2891154289 title "Quantitative glycomics using simulation optimization" @default.
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