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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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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 squares (OLS)...
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Related Experiment Video

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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

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Published on: December 7, 2021

A data-driven framework for identifying nonlinear dynamic models of genetic parts.

Kirubhakaran Krishnanathan1, Sean R Anderson, Stephen A Billings

  • 1Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield S1 3JD, UK. k.krishnanathan@sheffield.ac.uk

ACS Synthetic Biology
|May 9, 2013
PubMed
Summary

Synthetic biology needs better genetic part characterization. We introduce the Nonlinear AutoRegressive Moving Average model with eXogenous inputs (NARMAX) for dynamic modeling, showing superior accuracy over biochemical models for genetic parts.

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Area of Science:

  • Synthetic Biology
  • Control Engineering
  • Biotechnology

Background:

  • Characterizing genetic parts is crucial for synthetic biology design.
  • Existing methods may not be optimal for dynamic analysis.

Purpose of the Study:

  • To introduce and evaluate the Nonlinear AutoRegressive Moving Average model with eXogenous inputs (NARMAX) for genetic part characterization.
  • To assess NARMAX's effectiveness compared to traditional biochemical models.

Main Methods:

  • Application of the NARMAX framework to identify the dynamics of genetic part BBa_T9002.
  • Development of a concise, data-driven model for system dynamics.

Main Results:

  • A compact and consistent NARMAX model accurately represented system dynamics.
  • NARMAX demonstrated significantly higher prediction accuracy than a biochemical model.
  • The model showed consistency across different cell populations.

Conclusions:

  • The data-driven NARMAX framework is a powerful and accurate technique for dynamic modeling of genetic parts in synthetic biology.
  • NARMAX offers a novel and effective approach for component characterization, advancing synthetic biology design capabilities.