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

Introduction to MATLAB01:24

Introduction to MATLAB

MATLAB stands for Matrix Laboratory. MathWorks developed MATLAB as a multi-paradigm numerical computing environment and proprietary programming language. It has evolved significantly over the years to become a tool utilized by engineers, scientists, and mathematicians for various tasks, including matrix calculations, developing algorithms, data analysis, and visualization. MATLAB's applications span various industries and disciplines. It's used in image and signal processing, communications,...
Introduction to R01:11

Introduction to R

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 functionality,...
Introduction to Limits01:30

Introduction to Limits

A limit describes the value a function approaches as its input moves closer to a particular point. Even when a function is undefined at a specific value, limits allow us to analyze its behavior near that point. This concept is fundamental in calculus and essential for understanding continuity, derivatives, and integrals.Mathematically, a function f(x) has a limit L at x = a if its values L approach x as x gets arbitrarily close to a. This is written as:This notation expresses that the function...
Sampling Theorem01:15

Sampling Theorem

In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
Overview of Minitab01:11

Overview of Minitab

Minitab is a statistical software package designed for data analysis. With its origins in the 1970s and development at Pennsylvania State University, Minitab has grown significantly in its capabilities and applications. It plays a crucial role in quality management projects, especially in Six Sigma initiatives, by offering tools for process improvement and statistical analysis. Minitab's significance lies in its user-friendly interface, making complex statistical analysis accessible to users...
Properties of Laplace Transform-II01:16

Properties of Laplace Transform-II

Time differentiation, convolution, integration, and periodicity are fundamental concepts in analyzing functions and signals over time. Each concept provides a unique perspective on how functions evolve, interact, and repeat, offering essential tools for various scientific and engineering applications.
Time differentiation involves analyzing the rate of change of a function over time. Mathematically, it is the derivative of a function with respect to time. This concept can be likened to tracking...

You might also read

Related Articles

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

Sort by
Same author

Modeling the spatiotemporal properties of crosstalk between RyR-mediated and IP<sub>3</sub>R-mediated local Ca<sup>2+</sup> release.

Frontiers in cell and developmental biology·2026
Same author

Corrigendum to "Comprehensive Proteomics Metadata and Integrative Web Portals Facilitate Sharing and Integration of LINCS Multiomics Data".

Molecular & cellular proteomics : MCP·2025
Same author

Combine the wet and dry ingredients: Mixing experimental and computational models for a deeper understanding of stem cell-derived cardiomyocytes.

The Journal of physiology·2025
Same author

Comprehensive Proteomics Metadata and Integrative Web Portals Facilitate Sharing and Integration of LINCS Multiomics Data.

Molecular & cellular proteomics : MCP·2025
Same author

Creating cell-specific computational models of stem cell-derived cardiomyocytes using optical experiments.

PLoS computational biology·2024
Same author

Multiscale mapping of transcriptomic signatures for cardiotoxic drugs.

Nature communications·2024
Same journal

ZNRF3 and RNF43 are active monomeric E3 ubiquitin ligases that self-associate.

Science signaling·2026
Same journal

Allosteric ligands with distinct properties uncover tissue-specific physiological regulation mediated by free fatty acid receptor 2.

Science signaling·2026
Same journal

Diacylglycerol kinase ζ in B lymphocytes supports CD40-mediated immune synapse formation, mTORC1 signaling, and plasma cell fate.

Science signaling·2026
Same journal

The APC/C adaptor Cdh1 stabilizes STING to potentiate innate immune activation in renal cell carcinoma.

Science signaling·2026
Same journal

Fattening mother's milk with oxytocin.

Science signaling·2026
Same journal

Virion display reveals MD-1 as an endogenous agonist for the orphan receptor GPRC5B.

Science signaling·2026
See all related articles

Related Experiment Video

Updated: May 29, 2026

Scalable Nanohelices for Predictive Studies and Enhanced 3D Visualization
08:03

Scalable Nanohelices for Predictive Studies and Enhanced 3D Visualization

Published on: November 12, 2014

An introduction to MATLAB.

Eric A Sobie1

  • 1Department of Pharmacology and Systems Therapeutics and Systems Biology Center New York, Mount Sinai School of Medicine, New York, NY 10029, USA. eric.sobie@mssm.edu

Science Signaling
|September 22, 2011
PubMed
Summary
This summary is machine-generated.

This lecture series introduces MATLAB for scientific computing, covering basic programming logic and MATLAB syntax. It emphasizes array manipulation for biological data analysis, using intracellular calcium imaging as an example.

More Related Videos

In Situ Mapping of the Mechanical Properties of Biofilms by Particle-tracking Microrheology
12:58

In Situ Mapping of the Mechanical Properties of Biofilms by Particle-tracking Microrheology

Published on: December 4, 2015

High-Throughput Analysis of Optical Mapping Data Using ElectroMap
07:36

High-Throughput Analysis of Optical Mapping Data Using ElectroMap

Published on: June 4, 2019

Related Experiment Videos

Last Updated: May 29, 2026

Scalable Nanohelices for Predictive Studies and Enhanced 3D Visualization
08:03

Scalable Nanohelices for Predictive Studies and Enhanced 3D Visualization

Published on: November 12, 2014

In Situ Mapping of the Mechanical Properties of Biofilms by Particle-tracking Microrheology
12:58

In Situ Mapping of the Mechanical Properties of Biofilms by Particle-tracking Microrheology

Published on: December 4, 2015

High-Throughput Analysis of Optical Mapping Data Using ElectroMap
07:36

High-Throughput Analysis of Optical Mapping Data Using ElectroMap

Published on: June 4, 2019

Area of Science:

  • Computational Biology
  • Scientific Computing
  • Data Analysis

Background:

  • Introduces MATLAB, a scientific computing language, requiring no prior programming experience.
  • Covers fundamental programming logic and MATLAB-specific syntax.
  • Focuses on array manipulation, crucial for processing 2D biological data like images and microarrays.

Discussion:

  • Explains MATLAB's proficiency in handling array objects, essential for biological data.
  • Details basic MATLAB commands and built-in functions for efficient data processing.
  • Differentiates between MATLAB scripts and functions, guiding optimal programming organization.

Key Insights:

  • MATLAB is well-suited for array manipulation in biological data analysis.
  • Understanding script vs. function organization enhances code efficiency.
  • Practical application through analysis of intracellular calcium concentration data.

Outlook:

  • Provides a foundation for students to utilize MATLAB in their research.
  • Enables effective analysis of complex biological datasets.
  • Facilitates the application of computational methods in life sciences.