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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 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...
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Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
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Mechanistic Modeling of Biochemical Systems without A Priori Parameter Values Using the Design Space Toolbox v.3.0.

Miguel Á Valderrama-Gómez1, Jason G Lomnitz2, Rick A Fasani3

  • 1Department of Microbiology & Molecular Genetics, University of California, Davis, CA 95616, USA.

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|June 13, 2020
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Summary

The Design Space Toolbox v.3.0 (DST3) enables mechanistic biochemical modeling without prior parameter knowledge by predicting values for desired phenotypes. This computational tool aids in understanding biological design and creating synthetic circuits.

Keywords:
Biological SciencesIn Silico BiologySystems Biology

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Area of Science:

  • Biochemistry
  • Systems Biology
  • Computational Biology

Background:

  • Mechanistic models are crucial for understanding biochemical systems and biological design.
  • A major challenge in mechanistic modeling is the large number of unknown parameter values.
  • Existing methods require extensive parameter knowledge, limiting model application.

Purpose of the Study:

  • Introduce Design Space Toolbox v.3.0 (DST3), a software for mechanistic modeling.
  • Enable modeling without prior knowledge of parameter values.
  • Facilitate elucidation of biological design principles and synthetic circuit design.

Main Methods:

  • Implement the Design Space formalism in DST3 software.
  • Employ a phenotype-centric modeling approach.
  • Decompose systems into biochemical phenotypes and predict parameter values for desired phenotypes.

Main Results:

  • DST3 enables mechanistic modeling without requiring parameter values.
  • The software predicts parameter values that realize specific phenotypes.
  • DST3 is the most generally applicable implementation of the Design Space formalism.

Conclusions:

  • DST3 offers a powerful tool for mechanistic biochemical modeling.
  • The software overcomes the bottleneck of unknown parameter values.
  • DST3 advances the understanding of biological design and synthetic biology.