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Proc Natl Acad Sci U S A. 2017 Feb 21;114(8):E1336-E1344. doi: 10.1073/pnas.1615351114. Epub 2017 Feb 6.

Reaction dynamics analysis of a reconstituted Escherichia coli protein translation system by computational modeling.

Author information

Department of Biotechnology, Graduate School of Engineering, Osaka University, Suita, Osaka 565-0871, Japan;
Science Solutions Division, Mizuho Information & Research Institute, Chiyoda-ku, Tokyo 101-8443, Japan.
Humanware Innovation Program, Institute for Academic Initiatives, Osaka University, Suita, Osaka 565-0871, Japan.
Department of Bioinformatic Engineering, Graduate School of Information and Science, Osaka University, Suita, Osaka 565-0871, Japan.
Laboratory for Cell-Free Protein Synthesis, Quantitative Biology Center, RIKEN, Suita, Osaka 565-0874, Japan


To elucidate the dynamic features of a biologically relevant large-scale reaction network, we constructed a computational model of minimal protein synthesis consisting of 241 components and 968 reactions that synthesize the Met-Gly-Gly (MGG) peptide based on an Escherichia coli-based reconstituted in vitro protein synthesis system. We performed a simulation using parameters collected primarily from the literature and found that the rate of MGG peptide synthesis becomes nearly constant in minutes, thus achieving a steady state similar to experimental observations. In addition, concentration changes to 70% of the components, including intermediates, reached a plateau in a few minutes. However, the concentration change of each component exhibits several temporal plateaus, or a quasi-stationary state (QSS), before reaching the final plateau. To understand these complex dynamics, we focused on whether the components reached a QSS, mapped the arrangement of components in a QSS in the entire reaction network structure, and investigated time-dependent changes. We found that components in a QSS form clusters that grow over time but not in a linear fashion, and that this process involves the collapse and regrowth of clusters before the formation of a final large single cluster. These observations might commonly occur in other large-scale biological reaction networks. This developed analysis might be useful for understanding large-scale biological reactions by visualizing complex dynamics, thereby extracting the characteristics of the reaction network, including phase transitions.


cell-free protein synthesis; computational modeling; network analysis; protein translation; quasi-stationary state

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