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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, 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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PEtab.jl: advancing the efficiency and utility of dynamic modelling.

Sebastian Persson1,2, Fabian Fröhlich3, Stephan Grein4

  • 1Department of Mathematical Sciences, Chalmers University of Technology, Gothenburg, SE-412 96, Sweden.

Bioinformatics (Oxford, England)
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We developed SBMLImporter.jl and PEtab.jl, two Julia tools that streamline computational modeling for biological processes. These packages enhance parameter estimation and identifiability analysis for dynamic systems.

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Bioinformatics

Background:

  • Dynamic models are essential for understanding complex biological systems like cell signaling and differentiation.
  • Building these models involves computationally intensive tasks such as parameter estimation and model exploration.
  • Existing workflows can be cumbersome, hindering efficient model development.

Purpose of the Study:

  • To introduce SBMLImporter.jl and PEtab.jl, two novel Julia packages designed to simplify and accelerate computational modeling workflows.
  • To leverage Julia's high-performance computing features for efficient model analysis.
  • To provide a comprehensive toolbox for parameter estimation and identifiability analysis in dynamic biological models.

Main Methods:

  • Development of SBMLImporter.jl for importing Systems Biology Markup Language (SBML) models.
  • Implementation of PEtab.jl to handle parameter estimation problems in the PEtab format.
  • Utilization of Julia's advanced capabilities, including symbolic pre-processing and efficient ODE solvers.

Main Results:

  • SBMLImporter.jl and PEtab.jl are implemented in Julia, offering high-performance computing advantages.
  • Both packages are available on GitHub and installable via the Julia package manager.
  • Continuous testing and support across multiple operating systems ensure reliability.

Conclusions:

  • SBMLImporter.jl and PEtab.jl significantly streamline the process of building and analyzing dynamic biological models.
  • These tools enhance the efficiency of parameter estimation and identifiability analysis.
  • PEtab.jl serves as a comprehensive Julia-accessible toolbox for the entire modeling pipeline.