Quantitative glycomics using simulation optimization
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Simulation optimization is attracting increasing interest within the modeling and simulation research community. Although much research e ort has focused on how to apply a variety of simulation optimization techniques to solve diverse practical and research problems, researchers nd that existing optimization routines are di cult to extend or integrate and often require one to develop their own optimization methods because the existing ones are problem-speci c and not designed for reuse. A Semantically Enriched Environment for Simulation Optimization (SEESO) is being developed to address these issues. By implementing generalized semantic descriptions of the optimization process, SEESO facilitates reuse of the available optimization routines and more e ectively captures the essence of di erent simulation optimization techniques. This enrichment is based on the existing Discrete-event Modeling Ontology (DeMO) and the emerging Simulation oPTimization (SoPT) ontologies. SoPT includes concepts from both conventional optimization/mathematical programming and simulation optimization. Represented in ontological form, optimization routines can also be transformed into actual executable application code (e.g., targeting JSIM or Scala- Tion). As illustrative examples, SEESO is being applied to several simulation optimization problems. Mass spectrometry (MS) has emerged as the preeminent tool for performing quantitative glycomics analysis. However, the accuracy of these analyses is often compromised by the instrumental artifacts, such as low signal to noise ratios and mass-dependent di erential ion responses. Methods have been developed to address some of these issues by introducing stable isotopes to the glycans under study, but these methods require robust computational methods 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.