Back to Search
Start Over
Inference of Biochemical S-Systems via Mixed-Variable Multiobjective Evolutionary Optimization.
- Source :
-
Computational & Mathematical Methods in Medicine . 5/21/2017, p1-9. 9p. - Publication Year :
- 2017
-
Abstract
- Inference of the biochemical systems (BSs) via experimental data is important for understanding how biochemical components in vivo interact with each other. However, it is not a trivial task because BSs usually function with complex and nonlinear dynamics. As a popular ordinary equation (ODE) model, the S-System describes the dynamical properties of BSs by incorporating the power rule of biochemical reactions but behaves as a challenge because it has a lot of parameters to be confirmed. This work is dedicated to proposing a general method for inference of S-Systems by experimental data, using a biobjective optimization (BOO) model and a specially mixed-variable multiobjective evolutionary algorithm (mv-MOEA). Regarding that BSs are sparse in common sense, we introduce binary variables indicating network connections to eliminate the difficulty of threshold presetting and take data fitting error and the L0-norm as two objectives to be minimized in the BOO model. Then, a selection procedure that automatically runs tradeoff between two objectives is employed to choose final inference results from the obtained nondominated solutions of the mv-MOEA. Inference results of the investigated networks demonstrate that our method can identify their dynamical properties well, although the automatic selection procedure sometimes ignores some weak connections in BSs. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 1748670X
- Database :
- Academic Search Index
- Journal :
- Computational & Mathematical Methods in Medicine
- Publication Type :
- Academic Journal
- Accession number :
- 123147815
- Full Text :
- https://doi.org/10.1155/2017/3020326