Open Access Highly Accessed Research article

Understanding system dynamics of an adaptive enzyme network from globally profiled kinetic parameters

Austin WT Chiang123, Wei-Chung Liu4, Pep Charusanti5 and Ming-Jing Hwang123*

Author Affiliations

1 Institute of BioMedical Informatics, National Yang-Ming University, Taipei, Taiwan

2 Bioinformatics Program, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica, Taipei, Taiwan

3 Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan

4 Institute of Statistical Science, Academia Sinica, Taipei, Taiwan

5 Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA

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BMC Systems Biology 2014, 8:4  doi:10.1186/1752-0509-8-4

Published: 15 January 2014



A major challenge in mathematical modeling of biological systems is to determine how model parameters contribute to systems dynamics. As biological processes are often complex in nature, it is desirable to address this issue using a systematic approach. Here, we propose a simple methodology that first performs an enrichment test to find patterns in the values of globally profiled kinetic parameters with which a model can produce the required system dynamics; this is then followed by a statistical test to elucidate the association between individual parameters and different parts of the system’s dynamics.


We demonstrate our methodology on a prototype biological system of perfect adaptation dynamics, namely the chemotaxis model for Escherichia coli. Our results agreed well with those derived from experimental data and theoretical studies in the literature. Using this model system, we showed that there are motifs in kinetic parameters and that these motifs are governed by constraints of the specified system dynamics.


A systematic approach based on enrichment statistical tests has been developed to elucidate the relationships between model parameters and the roles they play in affecting system dynamics of a prototype biological network. The proposed approach is generally applicable and therefore can find wide use in systems biology modeling research.

Kinetic motif; Parameter profile; Biological network; Systems biology