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

Time-Series Graph00:54

Time-Series Graph

4.3K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
4.3K
Continuous -time Fourier Transform01:11

Continuous -time Fourier Transform

300
The Fourier series is instrumental in representing periodic functions, offering a powerful method to decompose such functions into a sum of sinusoids. This technique, however, necessitates modification when applied to nonperiodic functions. Consider a pulse-train waveform consisting of a series of rectangular pulses. When these pulses have a finite period, they can be accurately represented by a Fourier series. Yet, as the period approaches infinity, resulting in a single, isolated pulse, the...
300
Introduction to R01:11

Introduction to R

253
R is a powerful software environment for statistical computing and graphics. Originating as an implementation of the S language, developed at Bell Laboratories, R has evolved into a robust, open-source statistical software favored by statisticians and data scientists worldwide. Its comprehensive suite includes data manipulation, calculation, and graphical display capabilities, making it versatile for data analysis and visualization. Its programming language is at the core of R's...
253
Discrete-time Fourier transform01:26

Discrete-time Fourier transform

278
The Discrete-Time Fourier Transform (DTFT) is an essential mathematical tool for analyzing discrete-time signals, converting them from the time domain to the frequency domain. This transformation allows for examining the frequency components of discrete signals, providing insights into their spectral characteristics. In the DTFT, the continuous integral used in the continuous-time Fourier transform is replaced by a summation to accommodate the discrete nature of the signal.
One of the notable...
278
Basic Continuous Time Signals01:22

Basic Continuous Time Signals

194
Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...
194
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

211
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
211

You might also read

Related Articles

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

Sort by
Same author

Metrics of left ventricular active relaxation reflect proteomic myocardial remodelling and reverse remodelling.

ESC heart failure·2026
Same author

Combined physical and pharmacological anabolic osteoporosis therapies increase bone response and mechanoregulation in female mice.

Nature communications·2026
Same author

Stretch-Induced Increase in Ca<sup>2+</sup>-Spark Rate in Rabbit Atrial Cardiomyocytes Requires TRPA1 and Intact Microtubule Network.

Journal of the American Heart Association·2025
Same author

Correction: Pfäffle et al. A 14-Day Double-Blind, Randomized, Controlled Crossover Intervention Study with Anti-Bacterial Benzyl Isothiocyanate from Nasturtium (<i>Tropaeolum majus</i>) on Human Gut Microbiome and Host Defense. <i>Nutrients</i> 2024, <i>16</i>, 373.

Nutrients·2025
Same author

Dynamic modelling of signalling pathways when ordinary differential equations are not feasible.

Bioinformatics (Oxford, England)·2024
Same author

Foxi1 regulates multipotent mucociliary progenitors and ionocyte specification through transcriptional and epigenetic mechanisms.

bioRxiv : the preprint server for biology·2024
Same journal

Cross-Domain Transfer Learning from Peptides to Metabolites Using a Multi-Property Fine-Tuned LLM.

Bioinformatics (Oxford, England)·2026
Same journal

Biomedical Concept Recognition with Error-aware Negative-enhanced Ranking Framework.

Bioinformatics (Oxford, England)·2026
Same journal

TEDLH: Domain HMMs for sensitive detection of remote homologues.

Bioinformatics (Oxford, England)·2026
Same journal

PLNFGL: Joint Estimation of Multi-Condition Gene Networks from Single-cell RNA-seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026
Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: Jun 11, 2025

Acquiring Fluorescence Time-lapse Movies of Budding Yeast and Analyzing Single-cell Dynamics using GRAFTS
17:01

Acquiring Fluorescence Time-lapse Movies of Budding Yeast and Analyzing Single-cell Dynamics using GRAFTS

Published on: July 18, 2013

12.8K

RTF: an R package for modelling time course data.

Eva Brombacher1,2,3,4, Clemens Kreutz1,2

  • 1Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center-University of Freiburg, 79104 Freiburg, Germany.

Bioinformatics (Oxford, England)
|October 9, 2024
PubMed
Summary
This summary is machine-generated.

The retarded transient function (RTF) approach models cellular signaling dynamics and is now available in an R package. This tool aids in analyzing time-dependent data and reducing model complexity for better biological insights.

More Related Videos

Studying Soft-matter and Biological Systems over a Wide Length-scale from Nanometer and Micrometer Sizes at the Small-angle Neutron Diffractometer KWS-2
11:27

Studying Soft-matter and Biological Systems over a Wide Length-scale from Nanometer and Micrometer Sizes at the Small-angle Neutron Diffractometer KWS-2

Published on: December 8, 2016

12.2K
Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study
07:49

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study

Published on: April 18, 2025

116

Related Experiment Videos

Last Updated: Jun 11, 2025

Acquiring Fluorescence Time-lapse Movies of Budding Yeast and Analyzing Single-cell Dynamics using GRAFTS
17:01

Acquiring Fluorescence Time-lapse Movies of Budding Yeast and Analyzing Single-cell Dynamics using GRAFTS

Published on: July 18, 2013

12.8K
Studying Soft-matter and Biological Systems over a Wide Length-scale from Nanometer and Micrometer Sizes at the Small-angle Neutron Diffractometer KWS-2
11:27

Studying Soft-matter and Biological Systems over a Wide Length-scale from Nanometer and Micrometer Sizes at the Small-angle Neutron Diffractometer KWS-2

Published on: December 8, 2016

12.2K
Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study
07:49

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study

Published on: April 18, 2025

116

Area of Science:

  • Systems Biology
  • Computational Biology
  • Biophysics

Background:

  • Cellular signaling processes exhibit complex dynamics that are crucial for understanding biological functions.
  • Traditional modeling methods like ordinary differential equations (ODEs) can be computationally intensive and may struggle with certain time-dependent behaviors.
  • The retarded transient function (RTF) approach offers an alternative or complementary method for modeling these dynamics.

Purpose of the Study:

  • To introduce a new R package that implements the retarded transient function (RTF) approach for modeling cellular signaling dynamics.
  • To provide a user-friendly tool for analyzing time and dose dependencies in biological systems.
  • To enable model reduction for minimizing overfitting and improving the interpretability of dynamic models.

Main Methods:

  • Implementation of the RTF approach in an R package, building upon the Data2Dynamics framework.
  • Facilitation of modeling time and dose dependencies in biological data.
  • Inclusion of model reduction techniques to prevent overfitting.
  • Application to experimental data or ODE model trajectories for dynamic characterization.
  • Generation of low-dimensional representations from fitted RTF parameters.

Main Results:

  • The R package provides a practical implementation of the RTF approach for dynamic modeling.
  • The package allows for effective modeling of time and dose dependencies.
  • Model reduction capabilities help in creating parsimonious models.
  • The approach can characterize dynamics from both experimental data and ODE simulations.
  • A low-dimensional representation can be generated to identify key targets of perturbations.

Conclusions:

  • The RTF approach, implemented in the new R package, is a valuable complementary method to ODEs for modeling cellular signaling dynamics.
  • The package facilitates comprehensive analysis of dynamic biological processes, including time and dose dependencies.
  • Model reduction and low-dimensional representation aid in understanding complex biological systems and identifying critical targets.
  • This tool enhances the ability to analyze and interpret time-resolved biological data.