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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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Predicting network modules of cell cycle regulators using relative protein abundance statistics
Cihan Oguz1, Layne T Watson2,3,4, William T Baumann5
1Department of Biological Sciences, Virginia Tech, Blacksburg VA, 24061, USA. cihanoguzvt@gmail.com.
BMC Systems Biology
|March 1, 2017
Summary
This study reveals that cell cycle network topology influences protein abundance predictions. Machine learning models accurately predict network modules based on these statistics, highlighting module-specific patterns.
Area of Science:
- Systems Biology
- Computational Biology
- Cell Cycle Regulation
Background:
- Parameter estimation in systems biology models involves fitting experimental data.
- Feasible parameter vectors can lead to differing predictions under new conditions.
- This study investigates the feasible region of a yeast cell cycle model.
Purpose of the Study:
- To characterize the feasible region of a yeast cell cycle model.
- To test if network topology influences relative protein abundance prediction statistics.
- To explore module-specific patterns in model predictions.
Main Methods:
- Differential evolution to generate an ensemble of feasible parameter vectors.
- Prediction of mutant strain phenotypes.
- Random forest modeling to predict network modules using abundance statistics.
Main Results:
- Identified 86 novel viable mutants.
- Ranked proteins by contribution to predictive variability.
- Random forest models achieved AUCs of 0.83-0.87, outperforming random models.
Conclusions:
- Model prediction statistics exhibit distinct network module-specific patterns.
- Network topology influences the statistics of protein abundance predictions.
- Machine learning effectively predicts network modules from abundance statistics.
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