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 Experiment Videos

Uncovering signal transduction networks from high-throughput data by integer linear programming.

Xing-Ming Zhao1, Rui-Sheng Wang, Luonan Chen

  • 1ERATO Aihara Complexity Modelling Project, JST, Tokyo 151-0064, Japan.

Nucleic Acids Research
|April 16, 2008
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

CauFinder: Steering Cell-State and Phenotype Transitions by Causal Disentanglement Learning.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Identifying the optimal rapid antigen test for screening and determining the end of isolation: A modeling study.

PLoS computational biology·2026
Same author

Force Learning in Balanced Cortical E-I Networks.

Neural computation·2026
Same author

Psychological distress among Japanese high school students during the COVID-19 pandemic: An energy landscape analysis.

PLoS medicine·2026
Same author

Stratification of viral shedding patterns in saliva of COVID-19 patients.

eLife·2026
Same author

Brain-Inspired Chaotic Graph Backpropagation for Combinatorial Optimization.

IEEE transactions on neural networks and learning systems·2026
Same journal

Correction to 'scSuperAnnotator: A platform for benchmarking comparison and visualizing automated cellular annotation methods for scRNA-seq data'.

Nucleic acids research·2026
Same journal

Correction to 'Differentiable partition function calculation for RNA'.

Nucleic acids research·2026
Same journal

Deployment of non-canonical splicing in tunicate genomes is mediated by divergent U2AF function and changing m6A modification in U1 and U6 snRNA.

Nucleic acids research·2026
Same journal

Bacillus subtilis DnaB forms multiple protein-protein interactions essential for DNA replication initiation.

Nucleic acids research·2026
Same journal

Multiple forms of protein-protein and DNA binding are exhibited by BrxC from the BREX phage restriction system.

Nucleic acids research·2026
Same journal

Biosynthesis of glycosylated 5-hydroxycytosine in the DNA of diverse viruses.

Nucleic acids research·2026
See all related articles

This study introduces a new computational method to identify signal transduction networks (STNs) using protein interactions and gene expression data. The approach accurately maps cellular signaling pathways, aiding biological research.

Area of Science:

  • Systems Biology
  • Computational Biology
  • Genomics and Proteomics

Background:

  • Signal transduction is crucial for cellular responses, transmitting external signals internally.
  • Understanding signaling pathways requires advanced computational models integrating high-throughput data.
  • Current methods may lack flexibility in handling diverse prior information and network structures.

Purpose of the Study:

  • To develop a novel computational method for uncovering signal transduction networks (STNs).
  • To integrate protein-protein interaction and gene expression data for network identification.
  • To provide an efficient and accurate approach for mapping biological signaling pathways.

Main Methods:

  • Formulated signal transduction network identification as an integer linear programming (ILP) model.

Related Experiment Videos

  • Utilized a relaxed linear programming algorithm for solving the ILP model.
  • Integrated protein interaction and gene expression data for network reconstruction.

    • Main Results:

    • The proposed ILP model efficiently and accurately uncovered signal transduction networks in yeast.
    • Prediction results showed high agreement with existing biological knowledge and literature.
    • Demonstrated the model's flexibility in incorporating prior information without structural restrictions.

    Conclusions:

    • The developed ILP model offers an effective and accurate method for identifying signal transduction networks.
    • The approach is interpretable and scalable for large biological systems.
    • This method advances the understanding of essential mechanisms in cellular signaling pathways.