Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Lewis Symbols and the Octet Rule02:36

Lewis Symbols and the Octet Rule

80.5K
Chemical bonds are complex interactions between two or more atoms or ions, which reduce the potential energy of the molecule. Gilbert N. Lewis developed a model called the Lewis model that simplified the depiction of chemical bond formation and provided straightforward explanations for the chemical bonds seen in most common compounds.
80.5K
Exceptions to the Octet Rule02:55

Exceptions to the Octet Rule

37.3K
Many covalent molecules have central atoms that do not have eight electrons in their Lewis structures. These molecules fall into three categories:
37.3K
The Aufbau Principle and Hund's Rule03:02

The Aufbau Principle and Hund's Rule

72.5K
To determine the electron configuration for any particular atom, we can build the structures in the order of atomic numbers. Beginning with hydrogen, and continuing across the periods of the periodic table, we add one proton at a time to the nucleus and one electron to the proper subshell until we have described the electron configurations of all the elements. This procedure is called the aufbau principle, from the German word aufbau (“to build up”). Each added electron occupies the...
72.5K
The Quotient Rule01:30

The Quotient Rule

50
The quotient rule is a fundamental differentiation technique in calculus used to differentiate functions expressed as a ratio of two differentiable functions. Given a function of the form:Where g(x) and h(x) are both differentiable and h(x) ≠ 0, the derivative of f(x) is given by:Example:The quotient rule is beneficial when differentiating rational functions, trigonometric ratios, and exponential functions. For example, given:applying the quotient rule,This rule is essential in solving...
50
Midpoint Rule01:20

Midpoint Rule

55
Approximating areas under curved boundaries is a common problem in applied mathematics, particularly when an exact calculation is difficult or impractical. One effective numerical method for this purpose is the Midpoint Rule, which provides an estimate of the area under a curve by using rectangular approximations over a specified interval.Description of the Midpoint RuleThe Midpoint Rule begins by dividing the given interval into a number of equal subintervals. For each subinterval, the...
55
The Product Rule01:24

The Product Rule

121
In calculus, the Product Rule provides a method for differentiating expressions that are the product of two functions. It states that the derivative of the product of two differentiable functions equals the first function times the rate of change of the second, plus the second function times the rate of change of the first.This rule ensures that the rate of change of the product accounts for the simultaneous variation of both functions.A compelling way to understand the Product Rule is through...
121

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

TRAM-LAG1-CLN8 domain-containing protein TMEM56 regulates cell migration by changing intracellular ceramide levels.

BMC biology·2026
Same author

A detailed molecular network map and model of the NLRP3 inflammasome.

Frontiers in immunology·2023
Same author

Reduced Humoral and Cellular Immune Response to Primary COVID-19 mRNA Vaccination in Kidney Transplanted Children Aged 5-11 Years.

Viruses·2023
Same author

kboolnet: a toolkit for the verification, validation, and visualization of reaction-contingency (rxncon) models.

BMC bioinformatics·2023
Same author

Common Attractors in Multiple Boolean Networks.

IEEE/ACM transactions on computational biology and bioinformatics·2023
Same author

Cu-doped TiO<sub>2</sub> nanoparticles improve local antitumor immune activation and optimize dendritic cell vaccine strategies.

Journal of nanobiotechnology·2023
Same journal

Tracking Synthetic Adhesins on Bacterial Surfaces with Immunofluorescence Microscopy.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Post-Selection Methods for Analyzing mRNA Display Selections and Optimization of Hits.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

High-Performance Computing in Tandem Mass Spectrometry (MS/MS) Peptide Identification.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Engineering and Adapting Disulfide-Containing Proteins to Enable Intracellular Functionality.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

AI-Driven Protein Research: From Prediction to Design.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Methods for the In Vitro Selection of Protein and Peptide Libraries Using mRNA Display.

Methods in molecular biology (Clifton, N.J.)·2026
See all related articles

Related Experiment Video

Updated: Jan 26, 2026

Design and Development of Aptamer&#8211;Gold Nanoparticle Based Colorimetric Assays for In-the-field Applications
08:23

Design and Development of Aptamer–Gold Nanoparticle Based Colorimetric Assays for In-the-field Applications

Published on: June 23, 2016

12.7K

Using rxncon to Develop Rule-Based Models.

Jesper Romers1, Sebastian Thieme1, Ulrike Münzner1,2

  • 1Institute of Biology, Humboldt-Universität zu Berlin, Berlin, Germany.

Methods in Molecular Biology (Clifton, N.J.)
|April 5, 2019
PubMed
Summary
This summary is machine-generated.

We developed a protocol to build and validate complex signal transduction network models. This method uses the reaction-contingency language (rxncon) to improve model scalability and accuracy for biological systems.

Keywords:
Boolean/logical modelingNetwork reconstructionRule-based modelingSignal transductionrxncon

More Related Videos

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.4K
Development of an Alpha-synuclein Based Rat Model for Parkinson's Disease via Stereotactic Injection of a Recombinant Adeno-associated Viral Vector
08:33

Development of an Alpha-synuclein Based Rat Model for Parkinson's Disease via Stereotactic Injection of a Recombinant Adeno-associated Viral Vector

Published on: February 28, 2016

13.8K

Related Experiment Videos

Last Updated: Jan 26, 2026

Design and Development of Aptamer&#8211;Gold Nanoparticle Based Colorimetric Assays for In-the-field Applications
08:23

Design and Development of Aptamer–Gold Nanoparticle Based Colorimetric Assays for In-the-field Applications

Published on: June 23, 2016

12.7K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.4K
Development of an Alpha-synuclein Based Rat Model for Parkinson's Disease via Stereotactic Injection of a Recombinant Adeno-associated Viral Vector
08:33

Development of an Alpha-synuclein Based Rat Model for Parkinson's Disease via Stereotactic Injection of a Recombinant Adeno-associated Viral Vector

Published on: February 28, 2016

13.8K

Area of Science:

  • Systems Biology
  • Computational Biology
  • Biochemical Network Modeling

Background:

  • Signal transduction networks are complex, posing challenges for modeling due to combinatorial complexity and sparse data.
  • Existing methods for biomolecular reaction networks based on site dynamics offer variable resolution, improving adaptability to empirical data.
  • Formalizing current knowledge in large signal transduction networks remains a significant hurdle.

Purpose of the Study:

  • To present a robust protocol for building, validating, and simulating large-scale signal transduction network models.
  • To introduce and demonstrate the utility of the reaction-contingency (rxncon) language for mechanistic network modeling.
  • To provide a step-by-step guide for creating adaptable and accurate computational models of biological signaling.

Main Methods:

  • A five-step workflow based on the rxncon language.
  • Mechanistic network model creation, conversion to an executable Boolean model, and iterative network evaluation.
  • Exporting rxncon models into a rule-based format for broader compatibility and simulation.

Main Results:

  • Demonstrated a method to build and validate large signal transduction network models with improved scalability and accuracy.
  • Provided an introduction to the rxncon language and an annotated protocol for its application.
  • Developed a small-scale model of the insulin signaling pathway to illustrate the protocol's practical implementation and challenges.

Conclusions:

  • The presented protocol offers a scalable and accurate approach to modeling complex signal transduction networks.
  • The rxncon language facilitates the formalization and simulation of biological signaling pathways.
  • The workflow addresses challenges in signal transduction modeling, offering solutions for improved model development.