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Related Concept Videos

Mechanistic Models: Overview of Compartment Models01:21

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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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Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
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Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
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Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
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Bridging the gap between mechanistic biological models and machine learning surrogates.

Ioana M Gherman1, Zahraa S Abdallah1, Wei Pang2

  • 1Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom.

Plos Computational Biology
|April 20, 2023
PubMed
Summary

Complex mechanistic models are computationally demanding. Surrogate machine learning (ML) models offer a computationally efficient alternative for simulating complex biological systems, enabling faster analysis on standard hardware.

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Area of Science:

  • Computational Biology
  • Machine Learning
  • Systems Biology

Background:

  • Mechanistic models are essential for understanding complex biological processes but face significant computational challenges.
  • Increasing model complexity leads to high computational demands, limiting simulations and real-time applications.
  • Existing computational limitations hinder the widespread use of complex biological models.

Purpose of the Study:

  • To provide a comprehensive overview of surrogate machine learning (ML) models for approximating mechanistic models.
  • To explore the theoretical underpinnings, design, and training of ML surrogate models.
  • To demonstrate the application of ML surrogates in approximating biological mechanistic models and discuss their potential.

Main Methods:

  • Literature review covering applicability and theoretical aspects of ML surrogate models.
  • Focus on the design and training methodologies for machine learning models used as surrogates.
  • Analysis of existing applications of ML surrogates to various mechanistic models.

Main Results:

  • Surrogate ML models significantly reduce computational demands compared to mechanistic models.
  • ML surrogates can effectively approximate the behavior of complex mechanistic models.
  • Demonstrated applications of ML surrogates in areas like metabolism and whole-cell modeling.

Conclusions:

  • Surrogate ML models offer a computationally feasible solution for simulating complex biological systems.
  • These models can enable complex biological simulations on standard desktop computers.
  • ML surrogates hold significant potential for industrial applications in biology and systems modeling.