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Related Concept Videos

Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

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A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
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Instrument Calibration01:12

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Instrument calibration is essential for ensuring that instruments produce accurate and consistent results. It is vital in manufacturing, healthcare, testing laboratories, and scientific research. Calibration processes are specific to each instrument and help enhance data accuracy. Each instrument has a unique calibration process tailored to its design and function to improve data accuracy.
Analytical Balance Calibration
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Calibration Curves: Correlation Coefficient01:10

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In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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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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Glassware Calibration01:11

Glassware Calibration

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Accurate calibration of glassware, such as volumetric flasks, pipettes, and burettes, is essential to ensure accurate measurements in the analytical laboratory. Calibration helps maintain consistency across measurements and prevents errors arising from inaccurate volumes.
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Linear Approximation in Time Domain01:21

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Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
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Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb
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A protocol for dynamic model calibration.

Alejandro F Villaverde1, Dilan Pathirana2, Fabian Fröhlich3

  • 1Universidade de Vigo, Department of Systems Engineering & Control, Vigo 36310, Galicia, Spain.

Briefings in Bioinformatics
|October 7, 2021
PubMed
Summary
This summary is machine-generated.

This protocol simplifies parameter estimation for dynamic biological models. It guides users through model calibration, addressing challenges like identifiability and data limitations for accurate predictions.

Keywords:
dynamic modellingidentifiabilityidentificationoptimizationparameter estimationsystems biology

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Area of Science:

  • Systems biology
  • Computational biology
  • Mathematical modeling in life sciences

Background:

  • Ordinary differential equation (ODE) models are crucial for describing biological processes.
  • Parameter estimation (model calibration) in ODE models is complex due to unknown parameters and experimental data limitations.
  • Challenges include poor parameter identifiability, insufficient data, and local minima in objective functions, especially for large models.

Purpose of the Study:

  • To provide a comprehensive protocol for calibrating dynamic models.
  • To guide users, particularly non-experts, through the parameter estimation process.
  • To offer reproducible code and methodology for analyzing biological models.

Main Methods:

  • Development of a step-by-step protocol for dynamic model calibration.
  • Illustration of the methodology using two case study models.
  • Provision of all necessary code for result reproduction and independent analysis.

Main Results:

  • A structured approach to address challenges in parameter estimation for ODE models.
  • Demonstration of the protocol's utility with practical examples.
  • Availability of code enabling users to apply the protocol to their own models.

Conclusions:

  • The protocol offers a consolidated and accessible guide for biological model calibration.
  • It aims to improve the accuracy of model predictions and the reliability of conclusions drawn from biological models.
  • This resource empowers researchers and practitioners in biological modeling to effectively calibrate their dynamic models.