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Using bioconductor package BiGGR for metabolic flux estimation based on gene expression changes in brain.
Anand K Gavai1, Farahaniza Supandi2, Hannes Hettling1
1Centre for Integrative Bioinformatics, VU University, Amsterdam, The Netherlands; Netherlands Consortium for Systems Biology (NCSB), Amsterdam, The Netherlands.
Plos One
|March 26, 2015
Summary
The R package BiGGR now offers metabolic flux analysis, enabling researchers to predict metabolic changes from gene expression data. This tool aids in understanding diseases like Alzheimer's by analyzing metabolic flux patterns.
Area of Science:
- Systems biology
- Metabolic flux analysis
- Computational biology
Background:
- Predicting metabolic flux distribution is crucial in systems biology.
- Existing flux analysis tools are often proprietary (e.g., MATLAB).
- A need exists for accessible flux analysis tools within the R environment.
Purpose of the Study:
- To extend the R software package BiGGR for metabolic flux analysis.
- To facilitate the integration of metabolic analysis with gene expression data in R.
- To introduce a new algorithm for predicting flux changes from gene expression data.
Main Methods:
- Utilized public metabolic reconstruction databases and Systems Biology Markup Language (SBML) objects.
- Implemented linear inverse modeling algorithms for flux estimation.
- Developed a novel algorithm, Least-squares with equalities and inequalities Flux Balance Analysis (Lsei-FBA), to link gene expression to flux changes.
- Incorporated uncertainty quantification through sampling of the constrained flux space and hypergraph visualization.
Main Results:
- BiGGR now supports model assembly, flux estimation, and uncertainty quantification.
- The Lsei-FBA algorithm accurately predicted metabolic flux patterns in human brain, including changes associated with Alzheimer's disease.
- Model interoperability was enhanced through SBML import/export functionality.
Conclusions:
- The enhanced BiGGR package provides a powerful, accessible platform for metabolic flux analysis in R.
- BiGGR, particularly with the Lsei-FBA algorithm, can predict disease-associated metabolic alterations.
- This tool will advance systems biology research by integrating metabolic modeling with transcriptomic data.
