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Computation of Natural Logarithm Using Abstract Chemical Reaction Networks
Abstract:Recent researches has focused on nucleic acids as a substrate for designing biomolecular circuits for in situ monitoring and control. A common approach is to express them by a set of idealised abstract chemical reaction networks (ACRNs). Here, we present new results on how abstract chemical reactions, viz., catalysis, annihilation and degradation, can be used to implement circuit that accurately computes logarithm function using the method of Arithmetic-Geometric Mean (AGM), which has not been previously used in conjunction with ACRNs.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.3461866Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 325
 Seelig, G., Soloveichik, D., Zhang, D.Y., & Winfree, E. (2006). Enzyme-free nucleic acid logic circuits. Science, 314, 1585-1588.
 Zhang, D.Y., Turberfield, A.J., Yurke, B. & Winfree, E. (2007). Engineering entropy-driven reactions and networks catalyzed by DNA. Science, 318, 1121-1125.
 Padirac, A., Fujii, T., & Rondelez, Y. (2013). Nucleic acids for the rational design of reaction circuits. Current Opinion of Biotechnology, 24, 575-580.
 Yordanov, B., Kim, J., Petersen, R.L., Shudy, A., Kulkarni, V.V., & Philips, A. (2014). Computational design of nucleic acid feedback control circuits. ACS Synthetic Biology, 3, 600-616.
 Zhang, D.Y. (2011). Towards domain-based sequence design for DNA strand displacement reactions. DNA Computing and Molecular Programming, Springer Berlin Heidelberg, 162-175.
 Montagne, K., Plasson, R., Sakai, Y., Fujii, T., & Rondelez, Y. (2011). Programming an in vitro DNA oscillator using a molecular networking strategy. Molecular Systems Biology, 7, 466.
 Kim, J., & Winfree, E. (2011). Synthetic in vitro transcriptional oscillators. Molecular Systems Biology, 7, 465.
 Chen, Y.-J., Dalchau, N., Srinivas, N., Philips, A., Cardelli, L., Soloveichik, D., & Seelig, G. (2013). Programmable chemical controllers made from DNA. Nature Nanotechnology, 8, 755-762.
 Fujii, T., & Rondelez, Y. (2013). Predator-prey molecular ecosystems. ACS Nano, 7, 27-34.
 Soloveichik, D., Seelig, G., Winfree, E. (2010). DNA as a universal substrate for chemical kinetics. Proceedings of National Academy of Sciences, USA, 12, 5393-5398.
 Alberts, B. and Johnsosn, A. and Lewis, J. and Raff, M. and Roberts, K. and Walter, P. (2007). Molecular Biology of the Cell (5th Edition). Garland Science, New York, NY.
 Lim, W. and Mayer, B. and Pawson, T. (2014). Cell Signaling. Garland Science, New York, NY.
 Ma, K.C. and Perli, S.D. and Lu, T.K. (2016). Foundations and emerging paradigms for computing in living cells. Journal of Molecular Biology, 428, pp. 893-915.
 Chou, C. T. (2017) Chemical reaction networks for computing logarithm. Synthetic Biology, 2(1), ysx002.
 Oishi, K., & Klavins, E. (2011). Biomolecular implementation of linear I/O systems. IET Systems Biology, 5, 252-260.
 Brent, R. P. (2018). Fast Algorithms for High-Precision Computation of Elementary Functions, 5.
 Zarubiieva, I., Tseng, J.Y. and Kulkarni, V. (2018). Accurate Ratio Computation using Abstract Chemical Reaction Networks. IAENG WCE 2018: International Association of Engineers World Congress on Engineering.