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

Advanced computing for systems biology.

Kevin Burrage1, Lindsay Hood, Mark A Ragan

  • 1ARC Centre in Bioinformatics and Institute for Molecular Bioscience, The University of Queensland, Brisbane 4072, Australia.

Briefings in Bioinformatics
|September 27, 2006
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

A mathematical perspective on hypothesis-driven model construction: A case study in pea.

Mathematical biosciences·2026
Same author

Optimal control of multiple myeloma assuming drug resistance and off-target effects.

PLoS computational biology·2025
Same author

An in-depth study of the dynamics of Thornley's mathematical model in plant biology with a view to an improved model.

Journal of theoretical biology·2025
Same author

Agent-based modeling for the tumor microenvironment (TME).

Mathematical biosciences and engineering : MBE·2024
Same author

Harnessing 12-lead ECG and MRI data to personalise repolarisation profiles in cardiac digital twin models for enhanced virtual drug testing.

Medical image analysis·2024
Same author

Perlin noise generation of physiologically realistic cardiac fibrosis.

Medical image analysis·2024
Same journal

STED: flexible cross-modal topic modeling infers cell-type-specific regulatory landscapes from bulk epigenomics.

Briefings in bioinformatics·2026
Same journal

A knowledge-guided deep learning framework for quantitative nucleic acid testing.

Briefings in bioinformatics·2026
Same journal

Optimal transport for label transfer in single-cell multi-omics integration.

Briefings in bioinformatics·2026
Same journal

Continuous multi-omics pathway enrichment analysis resolves hidden functional heterogeneity.

Briefings in bioinformatics·2026
Same journal

Evaluating completeness, coherence, and consistency of genome-scale function annotations.

Briefings in bioinformatics·2026
Same journal

Transformers for single-cell RNA sequencing: a survey.

Briefings in bioinformatics·2026
See all related articles

Systems biology leverages computational modeling and grid computing for large-scale simulations. Grid computing offers scalable solutions for distributed data and expertise, but challenges remain in standardization and security for wider adoption.

Area of Science:

  • Computational biology
  • Bioinformatics
  • Systems biology

Background:

  • Systems biology relies on computational modeling and simulation of complex biological networks.
  • Data in systems biology is often large, geographically dispersed, necessitating distributed computing solutions.
  • Interdisciplinary and cross-institutional collaboration is crucial for addressing complex system-level biological problems.

Purpose of the Study:

  • To explore the computational and data requirements in systems biology.
  • To illustrate the utility of grid computing for handling large-scale data and computations in systems biology.
  • To identify challenges and opportunities for grid computing in advancing systems biology research.

Main Methods:

  • Case studies illustrating diverse computational and data needs: orthologue mapping (computation-intensive, data-light), Visible Cell project (terabyte-scale data), and multi-scale simulations (computationally and data-intensive).

Related Experiment Videos

  • Evaluation of grid computing as a solution for distributed data, computation, and expertise.
  • Analysis of current limitations in authentication, authorization, and audit systems for collaborative research.

    • Main Results:

    • Grid computing provides robust and scalable solutions for distributed data and computational tasks in systems biology.
    • Different systems biology applications present varied demands, from high computation with low data to massive data handling.
    • Existing security and access control systems pose scalability challenges for distributed collaboration, especially in commercial contexts.

    Conclusions:

    • Grid computing is essential for addressing the computational and data challenges in modern systems biology.
    • Further development of lightweight standards and scalable security infrastructure is needed to facilitate broader adoption of grid-type computing in systems biology.
    • Overcoming these challenges will enable more effective collaboration and accelerate discoveries in systems biology.