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Related Concept Videos

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
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Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
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Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
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Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
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Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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A Bayesian based functional mixed-effects model for analysis of LC-MS data.

Getachew K Befekadu1, Mahlet G Tadesse, Habtom W Ressom

  • 1Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20057, USA.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|December 8, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian multilevel model for aligning liquid chromatography-mass spectrometry (LC-MS) spectra. The method corrects for retention time (RT) and mass-to-charge (m/z) variability, improving data analysis.

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Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS)

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Area of Science:

  • Analytical Chemistry
  • Computational Biology
  • Statistical Modeling

Background:

  • Liquid chromatography-mass spectrometry (LC-MS) is crucial for analyzing complex biological samples.
  • LC-MS data often suffers from retention time (RT) and mass-to-charge (m/z) variability, complicating comparative analysis.
  • Accurate alignment of LC-MS spectra is essential for reliable identification and quantification of metabolites.

Purpose of the Study:

  • To develop a novel Bayesian multilevel functional mixed-effects model for robust LC-MS spectral alignment.
  • To incorporate group-specific random-effects to account for inter-sample variability.
  • To handle variations in both retention time (RT) and mass-to-charge ratio (m/z) using affine transformations.

Main Methods:

  • A Bayesian multilevel functional mixed-effects model is proposed.
  • The model utilizes affine transformations to correct for RT and m/z shifts.
  • Markov chain Monte Carlo (MCMC) methods, specifically Gibbs sampling, are employed for parameter inference.

Main Results:

  • The developed model effectively aligns LC-MS spectra by addressing RT and m/z variability.
  • The approach demonstrated computational tractability.
  • Prior knowledge can be integrated into the inference process, enhancing model flexibility.

Conclusions:

  • The proposed Bayesian framework offers a powerful tool for LC-MS data analysis.
  • The method provides accurate spectral alignment, crucial for downstream biological interpretation.
  • The model's ability to incorporate prior information and handle complex variability makes it valuable for LC-MS studies.