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Related Concept Videos

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.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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)...
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

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.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

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...
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...

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Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
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Published on: January 31, 2014

When the optimal is not the best: parameter estimation in complex biological models.

Diego Fernández Slezak1, Cecilia Suárez, Guillermo A Cecchi

  • 1Laboratorio de Sistemas Complejos, Depto de Computación, FCEyN, Buenos Aires University, Buenos Aires, Argentina. dfslezak@dc.uba.ar

Plos One
|November 5, 2010
PubMed
Summary
This summary is machine-generated.

Estimating parameters for complex cancer growth models is challenging. We found that the best data fit may yield biologically unrealistic parameters, necessitating alternative selection criteria.

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Area of Science:

  • Computational biology
  • Mathematical modeling
  • Cancer research

Background:

  • Advancements in computational power enable complex mathematical models of cancer growth.
  • Determining model parameters is a significant challenge, with in vivo measurements often difficult or impossible.
  • Fitting parameters under data-poor conditions can lead to biologically implausible values.

Purpose of the Study:

  • To explore methodological approaches for parameter estimation in complex biological models.
  • To investigate parameter space in a model of avascular tumor growth using high-performance computing.
  • To address the challenge of obtaining biologically relevant parameters from model fitting.

Main Methods:

  • Utilized high-performance computing (Blue Gene) for extensive parameter space studies.
  • Analyzed the cost function landscape for a mathematical model of avascular tumor growth.
  • Investigated methodological approaches for parameter estimation in complex biological systems.

Main Results:

  • The cost function landscape exhibits a rugged surface with numerous local minima.
  • The global minimum, representing the best data fit, can correspond to unrealistic biological solutions.
  • Demonstrated that optimal data fitting does not guarantee biologically plausible model parameters.

Conclusions:

  • Model parameters that best fit data may not be biologically optimal.
  • Propose selecting suboptimal parameters that meet additional biological criteria to avoid force-fitting.
  • Highlight the need for a general theory to address the complex interplay of models, data, and optimization approaches.