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

Cell Signaling Feedback Loops01:07

Cell Signaling Feedback Loops

Positive and negative feedback loops are crucial for regulating biological signaling systems. These feedback loops are processes that connect output signals to their inputs.
Negative feedback loops
Most signaling systems have negative feedback loops that can perform different functions such as output limiter, and adaptation.
Output limiter
Upon receiving an input signal, the cellular response rapidly increases until a threshold is reached. Beyond this threshold, a negative feedback loop...
Interactions Between Signaling Pathways01:19

Interactions Between Signaling Pathways

Signaling cascades usually lack linearity. Multiple pathways interact and regulate one another, allowing cells to integrate and respond to diverse environmental stimuli.
Convergence and divergence, and cross-talk between signaling pathways
Two distinct signaling pathways can converge on a single functional unit, which may either be a single protein or a complex of proteins. The response is either functionally distinct or synergistic between the two pathways but different from the response...
Hedgehog Signaling Pathway02:33

Hedgehog Signaling Pathway

The Hedgehog gene (Hh) was first discovered due to its control of the growth of disorganized, hair-like bristles phenotype in Drosophila, much like hedgehog spines. Hh plays a crucial role in the development of organs and the maintenance of homeostasis in both invertebrates and vertebrates. However, while Drosophila has only one Hh protein, mammals have multiple functional Hedgehog proteins - Sonic (Shh), Desert (Dhh), and Indian Hedgehog (Ihh). All of these homologous proteins have adapted to...
Hedgehog Signaling Pathway02:33

Hedgehog Signaling Pathway

The Hedgehog gene (Hh) was first discovered due to its control of the growth of disorganized, hair-like bristles phenotype in Drosophila, much like hedgehog spines. Hh plays a crucial role in the development of organs and the maintenance of homeostasis in both invertebrates and vertebrates. However, while Drosophila has only one Hh protein, mammals have multiple functional Hedgehog proteins - Sonic (Shh), Desert (Dhh), and Indian Hedgehog (Ihh). All of these homologous proteins have adapted to...
The JAK-STAT Signaling Pathway01:20

The JAK-STAT Signaling Pathway

Several cytokine receptors have tightly bound Janus kinase or JAK proteins attached at their cytosolic tail. Small signaling molecules such as cytokines, growth hormones, or prolactins bind to the cytokine receptors and initiate their dimerization. The dimerization brings the cytosolic JAKs together that trans-phosphorylate and activates each other. The activated JAKs now phosphorylate cytosolic tails of the cytokine receptors, which serve as binding sites for adaptor proteins such as  SH2...
Notch Signaling Pathway03:14

Notch Signaling Pathway

The Notch signaling pathway is a major intracellular signaling pathway that is highly conserved over a broad spectrum of metazoan species. It stands unique from other intracellular signaling mechanisms in animals because notch protein itself acts as the receptor as well as the primary signaling molecule.
The Notch gene came into the limelight in 1914 after the discovery that its mutation in Drosophila melanogaster leads to a serrated (or "notched") wing margin phenotype. It was not until 1985...

You might also read

Related Articles

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

Sort by
Same author

Quantum deep learning by sampling neural nets with a quantum annealer.

Scientific reports·2023
Same author

Deep Learnability: Using Neural Networks to Quantify Language Similarity and Learnability.

Frontiers in artificial intelligence·2021
Same author

Neural network identification of people hidden from view with a single-pixel, single-photon detector.

Scientific reports·2018
Same author

Deep learning for real-time single-pixel video.

Scientific reports·2018
Same author

A Bayesian approach for parameter estimation in the extended clock gene circuit of Arabidopsis thaliana.

BMC bioinformatics·2013
Same author

Modelling and inference reveal nonlinear length-dependent suppression of somatic instability for small disease associated alleles in myotonic dystrophy type 1 and Huntington disease.

Journal of the Royal Society, Interface·2013

Related Experiment Video

Updated: Jun 26, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Bifurcation analysis informs Bayesian inference in the Hes1 feedback loop.

Catherine F Higham1

  • 1Faculty of Biomedical and Life Sciences, University of Glasgow, Glasgow, Scotland, UK. c.higham.1@research.gla.ac.uk

BMC Systems Biology
|January 28, 2009
PubMed
Summary
This summary is machine-generated.

Mathematical analysis of gene regulation models informs prior selection for Bayesian parameter estimation. This approach aids in understanding system dynamics and improving model calibration using experimental data.

More Related Videos

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Differentiation of Mouse Breast Epithelial HC11 and EpH4 Cells
09:32

Differentiation of Mouse Breast Epithelial HC11 and EpH4 Cells

Published on: February 27, 2020

Related Experiment Videos

Last Updated: Jun 26, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Differentiation of Mouse Breast Epithelial HC11 and EpH4 Cells
09:32

Differentiation of Mouse Breast Epithelial HC11 and EpH4 Cells

Published on: February 27, 2020

Area of Science:

  • Systems Biology
  • Computational Biology
  • Mathematical Biology

Background:

  • Ordinary differential equations (ODEs) model biological system dynamics.
  • Parameter estimation calibrates ODE models using experimental data.
  • Bayesian inference frameworks and Markov chain Monte Carlo methods are used for parameter estimation.

Purpose of the Study:

  • To demonstrate how a priori mathematical analysis can guide prior distribution selection in Bayesian inference.
  • To study a gene regulation negative feedback loop model with a time delay.
  • To improve parameter estimation for complex biological models.

Main Methods:

  • Utilized a priori mathematical analysis focusing on Hopf Bifurcation.
  • Derived analytical expressions linking model parameters to system dynamics.
  • Employed computational tests on simulated and experimental data.

Main Results:

  • Analytical expressions and constraints were derived for model parameters.
  • Hopf Bifurcation analysis provided insights into oscillatory behavior onset.
  • Computational tests validated the usefulness of the derived analytical insights.

Conclusions:

  • Mathematical analysis offers insights into gene expression model dynamics.
  • A priori analysis can inform prior selection in Bayesian parameter inference.
  • This approach enhances the calibration of ODE models with experimental data.