Identification of biochemical networks by S-tree based genetic programming
Dong-Yeon Cho1, Kwang-Hyun Cho, Byoung-Tak Zhang
1School of Computer Science and Engineering, Seoul National University Seoul 151-742, Korea.
Bioinformatics (Oxford, England)
|April 6, 2006
Summary
This study introduces S-trees and genetic programming to simultaneously model biochemical network structure and dynamics. The method accurately identifies network components and parameters, even with noisy data.
Area of Science:
- Systems Biology
- Computational Biology
- Bioinformatics
Background:
- Existing models often focus on network structure or parameter estimation separately, not simultaneously.
- System-level understanding requires examining both component interactions and dynamic behaviors.
- Limited data hinders simultaneous identification of structure and parameters, leading to many plausible but incorrect network models.
Purpose of the Study:
- To propose a unified framework (S-trees) for simultaneous structural and dynamical modeling of biochemical networks.
- To develop an S-tree based genetic programming approach for efficient identification and parameter estimation.
- To address the challenge of identifying sparse network structures automatically.
Main Methods:
- Introduced a novel representation called S-trees for unified structural and dynamical modeling.
- Developed S-tree based genetic programming for simultaneous network structure identification and parameter estimation.
- Evaluated the algorithm's efficiency in automatically assembling sparse network primitives.
Main Results:
- Achieved accurate identification of true network structures with <5% error on artificial genetic networks.
- Demonstrated robustness to +/-10% noise in dynamic profiles.
- Successfully estimated parameters for a yeast fermentation pathway, detecting weak connections.
- Validated performance on the SOS response network in E. coli, identifying most relations accurately.
Conclusions:
- S-trees provide an efficient, unified scheme for biochemical network modeling.
- S-tree based genetic programming effectively identifies network structure and parameters simultaneously.
- The approach is robust, accurate, and applicable to real biological data.
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