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Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
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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...
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Approximate Bayesian inference in semi-mechanistic models.

Andrej Aderhold1, Dirk Husmeier1, Marco Grzegorczyk2

  • 11School of Mathematics and Statistics, Glasgow University, Glasgow, UK.

Statistics and Computing
|April 1, 2020
PubMed
Summary
This summary is machine-generated.

Reconstructing biological networks from differential equations is complex. This study introduces a novel pipeline using gradient matching to evaluate kinetic models, statistical methods, and numerical procedures for improved network inference accuracy.

Keywords:
ANOVABayesian model selectionMarkov jump processesNetwork InferenceSemi-mechanistic modelSystems biologyWidely applicable information criteria (WAIC, WBIC)

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Area of Science:

  • Systems biology
  • Computational statistics
  • Mathematical modeling

Background:

  • Inferring interaction networks from differential equations is a complex challenge across scientific fields.
  • Semi-mechanistic modeling using gradient matching offers a promising approach.

Purpose of the Study:

  • To investigate the impact of kinetic models, statistical formulations, and numerical methods on network reconstruction performance.
  • To provide guidance for computational statisticians in model selection for network inference.
  • To assess the accuracy of different inferential paradigms and numerical procedures.

Main Methods:

  • Employed a semi-mechanistic modeling approach based on gradient matching.
  • Developed a novel inferential pipeline utilizing an ANOVA scheme to disambiguate confounding factors.
  • Evaluated various information criteria and numerical procedures for approximating Bayes factors.

Main Results:

  • Identified key factors influencing the performance of network reconstruction.
  • Quantified the accuracy of different modeling and inference strategies.
  • Demonstrated the effectiveness of the proposed inferential pipeline in systematically evaluating methods.

Conclusions:

  • The study provides a systematic framework for evaluating methods in network inference.
  • Findings offer practical insights for computational statisticians regarding model selection and performance assessment.
  • The developed pipeline enhances the reliability and accuracy of inferring interaction networks.