Abstract
A common property of a living organism as a non-equilibrium dynamic system is the self-organization including the evolution of this self-organized system through distinct consecutive stages. In this article, the properties of dynamic self-organization is examined on a primitive model of life – the oscillating Belousov-Zhabotinsky (BZ) reaction. This system is sensitive to the changes of external conditions by dynamic reorganization of chemical waves. The generated patterns bring the information on history of the reaction evolution. We performed the pattern classification using calculation of the point information gain entropy density followed by multivariate statistical analysis. It was proved by numerous experiments that each obtained cluster is related to a unique reaction stage with characteristic concentrations of the reactants. The reliability makes this method promising for application to the recognition of stages in variety of complex systems. The results obtained via visual inspection of 6 parallel image series of the BZ reaction together with their statistical analysis approximate cell physiology during development and differentiation of tissues – a small change in the initial conditions leads to a different development of the cell population. This finding also explains a lower reproducibility of measurements of biological systems.
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Acknowledgments
This work was partly supported by the Ministry of Education, Youth and Sports of the Czech Republic – projects CENAKVA (No. CZ.1.05/2.1.00/01.0024) and CENAKVA II (No. LO1205 under the NPU I program).
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Zhyrova, A., Rychtáriková, R., Štys, D. (2017). Recognition of Stages in the Belousov-Zhabotinsky Reaction Using Information Entropy: Implications to Cell Biology. In: Rojas, I., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2017. Lecture Notes in Computer Science(), vol 10208. Springer, Cham. https://doi.org/10.1007/978-3-319-56148-6_29
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