Reverse engineering of bacterial chemotaxis pathway via frequency domain analysis

• Published in: PloS one Vol. 5; no. 3; p. e9182
• Main Authors: Luo, Junjie, Wang, Jun, Ma, Ting Martin, Sun, Zhirong
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 09.03.2010; Public Library of Science (PLoS)
• Subjects: Adaptation; Adaptive control; Algorithms; Analysis; Attractants; Bacteria; Bacterial Physiological Phenomena; Bandpass filters; Bias; Bioinformatics; Biotechnology; Chemotaxis; Computational Biology; Computational Biology/Synthetic Biology; Computational Biology/Systems Biology; Computer simulation; Control equipment; Control theory; Cut-off; Design; Design engineering; E coli; Education; Engineering; Escherichia coli; Escherichia coli - metabolism; Evolution; Frequency dependence; Frequency domain analysis; Frequency response; Information processing; Laboratories; Ligands; Markov Chains; Mathematical models; Models, Biological; Models, Statistical; Models, Theoretical; Movement; Noise; Pathways; Phosphorylation; Protein Binding; Protein Engineering; Repellents; Reverse engineering; Signal transduction; Studies; Synthetic biology; Taxonomy; Beijing China; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0009182

Chemotaxis is defined as a behavior involving organisms sensing attractants or repellents and leading towards or away from them. Therefore, it is possible to reengineer chemotaxis network to control the movement of bacteria to our advantage. Understanding the design principles of chemotaxis pathway is a prerequisite and an important topic in synthetic biology. Here, we provide guidelines for chemotaxis pathway design by employing control theory and reverse engineering concept on pathway dynamic design. We first analyzed the mathematical models for two most important kinds of E. coli chemotaxis pathway-adaptive and non-adaptive pathways, and concluded that the control units of the pathway de facto function as a band-pass filter and a low-pass filter, respectively, by abstracting the frequency response properties of the pathways. The advantage of the band-pass filter is established, and we demonstrate how to tune the three key parameters of it-A (max amplification), omega(1) (down cut-off frequency) and omega(2) (up cut-off frequency) to optimize the chemotactic effect. Finally, we hypothesized a similar but simpler version of the dynamic pathway model based on the principles discovered and show that it leads to similar properties with native E. coli chemotactic behaviors. Our study provides an example of simulating and designing biological dynamics in silico and indicates how to make use of the native pathway's features in this process. Furthermore, the characteristics we discovered and tested through reverse engineering may help to understand the design principles of the pathway and promote the design of artificial chemotaxis pathways.