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Related Concept Videos

Rational Expressions01:28

Rational Expressions

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Rational expressions are algebraic fractions in which both the numerator and the denominator are polynomials. These expressions follow the arithmetic rules of numerical fractions but require extra care due to the presence of variables. A fundamental part of working with rational expressions is identifying values that make the expression undefined, typically those that result in division by zero or undefined radicals.Determining the DomainThe domain of a rational expression includes all real...
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Rationalizing Substitutions01:29

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Integrals involving non-rational functions are often difficult to evaluate using standard techniques, especially when radicals appear in the integrand. Rationalizing substitution provides a systematic method for simplifying such integrals by converting them into rational forms that are easier to handle.Consider a rod whose linear mass density depends on a constant linear density, a characteristic length, and the distance from the left end of the rod. Determining the total mass requires...
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The arithmetic mean is the most commonly used measure of the central tendency of a data set. It is defined as the sum of all the elements constituting the data set, divided by the total number of elements. It is sometimes loosely referred to as the “average.”
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In engineering applications, the representation of the numerical value is critical. Presenting or reporting the answer is one of the essential parts of engineering practices. Numerical calculations are performed using handheld calculators or computers since numerically accurate answers are always preferred.
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Real Number Operations01:27

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The concept of real numbers includes all the values that can be represented on a continuous number line. The system began with basic counting values used for enumeration. It later expanded to include values that represent the absence of quantity and opposites of the counting values. When situations required expressing parts of a whole or dividing quantities evenly, values capable of representing such proportions were developed. When written using decimal notation, these values can end or repeat...
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Asymptotes in Rational Functions01:30

Asymptotes in Rational Functions

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A rational function is defined as the quotient of two polynomials:  where Q(x)≠0, These functions often exhibit asymptotes, which are the lines that the graph approaches but never touches. These asymptotes are classified based on how the function behaves near specific values of the input.Vertical asymptotes occur where the denominator is zero, and the numerator is not, causing the function to be undefined. These are found by solving Q(x)=0. For example:  has a vertical...
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The MONGOOSE Rational Arithmetic Toolbox.

Christopher Le1, Leonid Chindelevitch2

  • 1School of Computing Sciences, Simon Fraser University, Burnaby, BC, Canada.

Methods in Molecular Biology (Clifton, N.J.)
|December 10, 2017
PubMed
Summary
This summary is machine-generated.

Constraint-based modeling of metabolic networks faces challenges with floating-point arithmetic. The MONGOOSE toolbox uses rational arithmetic for reproducible analysis and model checking of genome-scale metabolic models.

Keywords:
Constraint-based analysisMetabolic networksRational arithmeticReproducibilityUser interface

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Metabolic Engineering

Background:

  • Genome-scale metabolic models are crucial for understanding cellular metabolism.
  • Constraint-based modeling is a popular framework for analyzing these models.
  • Ensuring the quasi-steady-state assumption in metabolic models is computationally challenging due to model complexity and floating-point arithmetic.

Purpose of the Study:

  • To present a protocol for comprehensive metabolic network model analysis using the MONGOOSE toolbox.
  • To highlight MONGOOSE's capability as a model-checking platform during and after model construction.
  • To address the challenges of ensuring model consistency and reproducibility in metabolic network modeling.

Main Methods:

  • Utilizing the MONGOOSE toolbox, which employs rational arithmetic for metabolic network analysis.
  • Employing a newly developed graphical user interface (GUI) for the MONGOOSE toolbox.
  • Implementing a protocol for complete analysis of metabolic network models.

Main Results:

  • The MONGOOSE toolbox ensures reproducible analysis by using rational arithmetic, overcoming limitations of floating-point arithmetic.
  • The GUI facilitates straightforward model analysis and checking.
  • The toolbox provides a robust platform for validating metabolic models against their underlying assumptions.

Conclusions:

  • The MONGOOSE toolbox offers a reliable solution for analyzing genome-scale metabolic models with enhanced accuracy and reproducibility.
  • Its model-checking capabilities are valuable throughout the metabolic model lifecycle.
  • Rational arithmetic in MONGOOSE ensures consistent adherence to the quasi-steady-state assumption.