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The Motifs of Kinetic Parameters That Facilitate Robust Functions of a Synthetic Gene
Network
Austin W.T. Chiang1,2,3,4, Bernhard Ø Palsson4 and Ming-Jing Hwang1,2,3
1Bioinformatics
Program, Taiwan International Graduate Program, Academia Sinica,
Taipei,
2Institute of BioMedical
Informatics, National Yang-Ming University, Taipei, Taiwan
3Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
4Department of Bioengineering, University of
California, San Diego, La Jolla, California, USA.
Abstract
Synthetic Biology is advancing rapidly with promises of great benefits to help us understand biology, improve public health and invent new technologies to sustain our biosphere. However, most of the artificially designed systems are still short of applicable in real biological settings, in part because they are not as robust as natural systems, which in turn underlines our limited knowledge of the system parameters at the design stage to achieve system robustness. Here, we described a systematic approach to computationally define and characterize the kinetic parameters of a synthetic gene network capable of performing user-desired system dynamics under noisy conditions. In this approach, we devised a Robustness Map to characterize and display functional performance of specific kinetic parameter combinations. Our results showed that there was a limited set of combinations, among a vast amount of many, of the kinetic parameters that could achieve a user-desired system dynamics, such as a steady-state dynamics illustrated here, under perturbations. Furthermore, these parameter combinations formed just few clusters, resulting in representative parameter motifs and yielding insight into the interplay of the biochemical reactions involved. The approach developed here is general in that it can be applied to study other types of dynamics; it is therefore a useful tool in synthetic biology design and in research to uncover the design principles of biological systems.