A model comparison study of the flowering time regulatory network in Arabidopsis

BMC Systems Biology

Jeffrey JP Tsai²  Phillip CY Sheu¹  Chun-Ming Chang⁴  Ka-Lok Ng³  Pei-Chun Chang³  Charles CN Wang³ 

[1] Department of Electrical Engineering & Computer Science, University of California at Irvine, Irvine, USA;Department of Computer Science, University of Illinois at Chicago, Chicago, USA;Department of Biomedical Informatics, Asia University, Taichung, Taiwan;Department of Applied Informatics and Multimedia, Asia University, Taichung, Taiwan

关键词: Floral transition process;    Arabidopsis;    S-system;    Mass-action model;    Michaelis-Menten model;    Dynamic model;   

Others  :  1141453
DOI  :  10.1186/1752-0509-8-15

 received in 2013-03-26, accepted in 2014-01-15,  发布年份 2014

【 摘 要 】

Background

Several dynamic models of a gene regulatory network of the light-induced floral transition process in Arabidopsis have been developed to capture the behavior of gene transcription and infer predictions based on experimental observations. It has been proven that the models can make accurate and novel predictions, which generate testable hypotheses.

Two major issues were addressed in this study. First, construction of dynamic models for gene regulatory networks requires the use of mathematic modeling that comprises equations of a large number of parameters. Second, the binding mechanism of the transcription factor with DNA is another factor that requires detailed modeling. The first issue was tackled by adopting an optimization algorithm, and the second was addressed by comparing the performance of three alternative modeling approaches, namely the S-system, the Michaelis-Menten model and the Mass-action model. The efficiencies of parameter estimation and modeling performance were calculated based on least square error (O(p)), mean relative error (MRE) and Akaike Information Criterion (AIC).

Results

We compared three models to describe gene regulation of the flowering transition process in Arabidopsis. The Mass-action model is the simplest and has the least parameters. It is therefore less computation-intensive with the smallest AIC value. The disadvantage, however, is that it assumes the system is simply a second order reaction which is not the case in our study. The Michaelis-Menten model also assumes the system is homogeneous and ignores the intracellular protein transport process. The S-system model has the best performance and it does describe the diffusion effects. A disadvantage of the S-system is that it involves the most parameters. The largest AIC value also implies an over-fitting may occur in parameter estimation.

Conclusions

Three dynamic models were adopted to describe the dynamics of the gene regulatory network of the flowering transition process in Arabidopsis. Based on MRE, the least square error and global sensitivity analysis, the S-system has the best performance. However, the fact that it has the highest AIC suggests an over-fitting may occur in parameter estimation. The result of this study may need to be applied carefully when modeling complex gene regulatory networks.

【 授权许可】

   
2014 Wang et al.; licensee BioMed Central Ltd.

【 图 表 】

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