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

Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear.
Classification of Signals01:30

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length, the...
Linear time-invariant Systems01:23

Linear time-invariant Systems

A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be calculated...
SFG Algebra01:16

SFG Algebra

In Signal Flow Graph (SFG) algebra, the value a node represents is determined by the sum of all signals entering that node. This summed value is then transmitted through every branch leaving the node, making the SFG a powerful tool for visualizing and analyzing control systems.
Each node in an SFG corresponds to a variable, and the interactions between nodes are represented by branches with associated gains. When multiple branches lead into a node, the value at that node is the sum of the...

You might also read

Related Articles

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

Sort by
Same author

Ill Fate of Rectal Mucinous Adenocarcinoma: A Defect in Immunosurveillance or a Mucin Coating Effect?-The IMMUNOREACT 20 Study.

Cancers·2026
Same author

N2SIMBA: from Network topology to SIMulation of interactions and BActerial abundance, using microbial consumer resource model.

Frontiers in bioinformatics·2026
Same author

Active learning-guided mechanistic modeling reveals context-specific regulators of CXCL9 expression in pancreatic cancer cells.

Molecular systems biology·2026
Same author

Environmental Personal Exposure Clusters to Investigate Multiple Sclerosis and Amyotrophic Lateral Sclerosis Progression.

Studies in health technology and informatics·2026
Same author

IMMUNOREACT 4: Peritumoral Microenvironment Associated with Anastomotic Leaks After Surgery for Rectal Cancer.

Cancers·2026
Same author

The association of environmental exposure with multiple sclerosis severity score: A study based on sequential data modeling.

International journal of medical informatics·2026

Related Experiment Videos

A Boolean approach to linear prediction for signaling network modeling.

Federica Eduati1, Alberto Corradin, Barbara Di Camillo

  • 1Department of Information Engineering, University of Padova, Padova, Italy.

Plos One
|September 24, 2010
PubMed
Summary

This study presents a novel computational method for reconstructing biological signaling networks. The approach accurately predicts protein activity under various conditions, aiding in understanding disease mechanisms.

Related Experiment Videos

Area of Science:

  • Systems Biology
  • Computational Biology
  • Network Medicine

Background:

  • Reconstructing biological signaling networks is crucial for understanding cellular responses to stimuli and inhibitors.
  • Predicting protein activity levels in complex biological systems remains a significant challenge.

Purpose of the Study:

  • To develop and evaluate a computational method for reconstructing cause-effect signaling networks from limited experimental data.
  • To predict protein activity levels in multi-stimulus/inhibitor conditions based on the reconstructed network.

Main Methods:

  • Inference of Boolean tables from single-stimulus/inhibitor data to identify protein-affecting conditions.
  • Reconstruction of a cause-effect network based on the inferred Boolean tables.
  • Linear combination of training data guided by rules derived from the reconstructed network.

Main Results:

  • The proposed method achieved high performance in the DREAM4 challenge for predictive signaling network modeling.
  • The reconstructed networks were consistent with existing biological literature.
  • The method provided reasonable predictions for protein activity levels.

Conclusions:

  • The developed method offers a robust approach for signaling network reconstruction and prediction.
  • This approach can be applied to predict the impact of ligands on signaling pathways and disease-altered responses.
  • The method's simplicity and effectiveness make it a valuable tool for systems biology research.