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Updated: May 30, 2026
08:38
Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay (PCA) in Living Cells
Published on: March 3, 2015
Cellular automata and its applications in protein bioinformatics
Xuan Xiao1, Pu Wang, Kuo-Chen Chou
1Computer Department, Jing-De-Zhen Ceramic Institute, China. xxiao@gordonlifescience.org
Current Protein & Peptide Science
|July 27, 2011
Summary
Cellular automata (CA) offer a powerful and intuitive approach for analyzing protein sequences in bioinformatics. This review highlights CA
Area of Science:
- Bioinformatics
- Computational Biology
- Systems Biology
Background:
- The postgenomic era yields vast protein sequence data, necessitating efficient analysis tools.
- Current bioinformatics tools often rely on machine learning for attribute prediction.
- Understanding uncharacterized proteins is crucial for research and drug discovery.
Purpose of the Study:
- To review the application of cellular automata (CA) in protein bioinformatics.
- To highlight the potential of CA for analyzing protein sequence data.
- To explore CA's utility in predicting protein attributes.
Main Methods:
- Review of existing literature on cellular automata in protein bioinformatics.
- Discussion of CA as a discrete dynamic model for complex systems.
- Exploration of CA's application in sequence visualization and evolutionary analysis.
Main Results:
- Cellular automata demonstrate potential for visualizing protein sequences.
- CA can be used to investigate protein evolution.
- CA facilitates the prediction of various protein attributes.
Conclusions:
- Cellular automata offer an intuitive and powerful method for protein bioinformatics.
- The simplicity and flexibility of CA make them suitable for complex biological systems.
- CA present a promising tool for high-throughput analysis of protein sequences.
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