System identification of the Arabidopsis plant circadian system
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The circadian system generates an endogenous oscillatory rhythm that governs the daily activities of organisms in nature. It offers adaptive advantages to organisms through a coordination of their biological functions with the optimal time of day. In this paper, a model of the circadian system in the plant Arabidopsis (species thaliana) is built by using system identification techniques. Prior knowledge about the physical interactions of the genes and the proteins in the plant circadian system is incorporated in the model building exercise. The model is built by using primarily experimentally-verified direct interactions between the genes and the proteins with the available data on mRNA and protein abundances from the circadian system. Our analysis reveals a great performance of the model in predicting the dynamics of the plant circadian system through the effect of diverse internal and external perturbations (gene knockouts and day-length changes). Furthermore, we found that the circadian oscillatory rhythm is robust and does not vary much with the biochemical parameters except those of a light-sensitive protein P and a transcription factor TOC1. In other words, the circadian rhythmic profile is largely a consequence of the network’s architecture rather than its particular parameters. Our work suggests that the current experimental knowledge of the gene-to-protein interactions in the plant Arabidopsis, without considering any additional hypothetical interactions, seems to suffice for system-level modeling of the circadian system of this plant and to present an exemplary platform for the control of network dynamics in complex living organisms.
KeywordsComplex systems Biological networks Nonlinear dynamics Circadian systems
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- J. C. Dunlap, J. L. Loros and P. J. DeCoursey, Chronobiology: Biological time keeping (Sinauer Associate, Sunderland, 2003).Google Scholar
- N. Budjoso and S. J. Davis, Frontiers in Plant Science 4, 1 (2013).Google Scholar
- L. Ljung, System Identification: Theory for the User, 2nd Edition (Prentice Hall, Englewood Cliffs, 1999).Google Scholar
- M. N. Zeilinger, E. M. Farre, S. R. Taylor, S. A. Kay and F. J. Doyle III, Molecular Systems Biology 58, 1 (2006).Google Scholar
- U. Alon, M. G. Surette, N.Barkai and S. Leibler, Nature 397, 168 (1999).Google Scholar
-  G. von Dassow
- https://www.apctp.org/upload/jrg/jrgid 11 Supplementary Material MF DES PJK.pdf.Google Scholar
- S. H. Strogatz, Nonlinear Dynamics and Chaos with Applications to Physics, Biology, Chemistry and Engineering (Addison-Wesley Publishing Company, Massachusetts, 1994).Google Scholar
- D. E. Somers, W.-Y. Kim and R. Geng, Plant Cell 101, 319 (2004).Google Scholar