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Related Concept Videos

Solving Equations Graphically01:27

Solving Equations Graphically

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Graphical methods provide an intuitive and visual means of solving equations by representing functions on the coordinate plane. These methods are especially helpful for estimating solutions, analyzing complex expressions, or understanding the behavior of functions.To solve an equation graphically, it must first be expressed in the form y = f(x). The solution to the original equation corresponds to the x-values where the graph intersects the x-axis, meaning where f(x) = 0.For example, the linear...
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Graphical Representation of Inequalities01:28

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The graph of the equation where y equals x squared forms a curve known as a parabola. This curve acts as a boundary in the coordinate plane, dividing it into distinct regions based on the relative position of points.When the equality sign in the equation is replaced with an inequality—such as greater than, less than, greater than or equal to, or less than or equal to—the graphical representation changes from a single curve into a broader shaded area that signifies the set of all...
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Solving Inequalities Graphically01:24

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Solving inequalities graphically involves using a visual approach to determine where a mathematical expression meets a specific condition, such as being greater than or less than another value. By examining the position of a graph relative to the x-axis or another graph, it becomes possible to identify the range of x-values that satisfy the inequality. This method provides an intuitive understanding of solution intervals by showing where the inequality holds true.Graphical solutions to...
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Graphical and Analytic Representation of Sinusoids01:20

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Analyzing two sinusoidal voltages with equal amplitude and period but different phases on an oscilloscope, an instrument used to display and analyze waveforms, involves a three-step process.
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Velocity and position can be calculated from the known function of acceleration as a function of time. The total area under the acceleration-time graph and the velocity-time graph gives the change in velocity and position, respectively. In the case of an airplane, its acceleration is tracked using the inertial navigation system. The pilot provides the input of the airplane's initial position and velocity before takeoff. The inertial navigation system then uses the acceleration data to...
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Related Experiment Video

Updated: Feb 13, 2026

A Web Tool for Generating High Quality Machine-readable Biological Pathways
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A graphical and computational modeling platform for biological pathways.

Alessandra Livigni1, Laura O'Hara1,2, Marta E Polak3,4

  • 1The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, UK.

Nature Protocols
|March 16, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a biologist-friendly graphical modeling scheme for constructing and simulating biological pathways. The novel approach simplifies pathway modeling without complex equations, enabling efficient hypothesis generation and system behavior prediction.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Molecular Biology

Background:

  • Systems biology aims to model biological pathways for understanding structure and function.
  • Existing modeling approaches often require complex mathematical formulations like equations and rate constants.

Purpose of the Study:

  • To present a biologist-friendly graphical modeling protocol for constructing and simulating biological pathways.
  • To enable detailed network diagram construction and dynamic behavior simulation without extensive mathematical expertise.

Main Methods:

  • Utilized modified Edinburgh Pathway Notation (mEPN) and yEd software for network diagram assembly.
  • Employed 'tokens' for model parameterization based on component activity or amount.
  • Performed model testing via visualization and quantitative analysis using Graphia Professional.

Main Results:

  • Developed a novel approach combining a sophisticated notation scheme with Petri net-based simulation and visualization.
  • Demonstrated a method that bypasses the need for constructing rate equations for parameterization.
  • Showcased rapid simulation runs (seconds) for complex, parameterized models.

Conclusions:

  • The presented protocol offers a powerful tool for knowledge management and information exchange in systems biology.
  • This approach facilitates hypothesis generation, testing, and prediction of biological system behavior under perturbation.
  • Graphical pathway models provide an intuitive yet powerful alternative to traditional mathematical modeling techniques.