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Related Experiment Video

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FIBS-enabled Noninvasive Metabolic Profiling

FIBS-enabled Noninvasive Metabolic Profiling

Published on: February 3, 2014

SBSI: an extensible distributed software infrastructure for parameter estimation in systems biology.

Richard Adams¹, Allan Clark, Azusa Yamaguchi

¹SynthSys Edinburgh, King's Buildings, University of Edinburgh, Edinburgh, UK.

Bioinformatics (Oxford, England)

|January 19, 2013

Summary

This summary is machine-generated.

We developed the Systems Biology Software Infrastructure (SBSI) to simplify complex computational experiments for systems biology researchers. This software suite enhances model parameter fitting and integrates experimental data, making advanced modeling more accessible.

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Area of Science:

Systems Biology
Computational Biology
Bioinformatics

Background:

Complex computational experiments, like model parameter fitting in Systems Biology, demand significant computational power and user-friendly software.
Scientists require easy access to up-to-date experimental data and tools adaptable to varying computational expertise.

Purpose of the Study:

To develop a software suite, the Systems Biology Software Infrastructure (SBSI), to streamline the parameter-fitting process in Systems Biology.
To provide a modular and extensible platform that simplifies complex computational experiments for a wider range of scientists.

Main Methods:

Developed a modular software suite (SBSI) comprising three components: SBSINumerics (parallelized algorithms for parameter fitting), SBSIDispatcher (job tracking and submission), and SBSIVisual (experiment configuration and results viewing).
Implemented a plugin infrastructure for easy installation of project-specific modules, leveraging existing UI and application frameworks.
Utilized standard data formats to facilitate customization and integration by plugin developers.

Main Results:

SBSI facilitates complex computational experiments, particularly model parameter fitting, by providing high-performance parallelized algorithms.
The modular design and plugin infrastructure allow for customization and extension of SBSI for specific research needs.
SBSI enhances accessibility for scientists with varying computational expertise by offering a user-friendly interface and platform independence.

Conclusions:

The Systems Biology Software Infrastructure (SBSI) effectively addresses the challenges of complex computational experiments in Systems Biology.
SBSI enhances the efficiency and accessibility of model parameter fitting and data integration, supporting broader scientific adoption.
The software's open-source availability and platform independence promote its widespread use across different research environments.