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Sanassy, Daven; Fellermann, Harold; Krasnogor, Natalio; Konur, Savas; Mierla, Laurentiu M.; Gheorghe, Marian; Ladroue, Christophe; Kalvala, Sara (2014)
Publisher: IEEE
Languages: English
Types: Unknown
Subjects: QH301, QA76
Synthetic Biology aspires to design, compose and engineer biological systems that implement specified behaviour. When designing such systems, hypothesis testing via computational modelling and simulation is vital in order to reduce the need of costly wet lab experiments. As a case study, we discuss the use of computational modelling and stochastic simulation for engineered genetic circuits that implement Boolean AND and OR gates that have been reported in the literature. We present performance analysis results for nine different state-of-the-art stochastic simulation algorithms and analyse the dynamic behaviour of the proposed gates. Stochastic simulations verify the desired functioning of the proposed gate designs.\ud
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] E. Andrianantoandro, S. Basu, D. K. Karig, and R. Weiss, “Synthetic biology: new engineering rules for an emerging discipline,” Molecular Systems Biology, vol. 2, no. 1, Jan. 2006.
    • [2] D. T. Gillespie, “A general method for numerically simulating the stochastic time evolution of coupled chemical reactions,” Journal of Computational Physics, vol. 22, no. 4, pp. 403 - 434, 1976.
    • --, “Exact stochastic simulation of coupled chemical reactions,” The Journal of Physical Chemistry, vol. 81, no. 25, pp. 2340-2361, 1977.
    • [4] J. Beal, A. Phillips, D. Densmore, and Y. Cai, “High-level programming languages for biomolecular systems,” in Design and Analysis of Biomolecular Circuits. Springer New York, 2011, pp. 225-252.
    • [5] A. Tamsir, J. J. Tabor, and C. A. Voigt, “Robust multicellular computing using genetically encoded NOR gates and chemical 'wires',” Nature, vol. 469, no. 7329, pp. 212-215, 2011.
    • [6] S. Regot, J. Macia, N. Conde, K. Furukawa, J. Kjellen, T. Peeters, S. Hohmann, E. de Nadal, F. Posas, and R. Sole, “Distributed biological computation with multicellular engineered networks,” Nature, vol. 469, no. 7329, pp. 207-211, 2011.
    • [7] M. Hucka et al., “The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models,” Bioinformatics, vol. 19, no. 4, pp. 524-531, 2003.
    • [8] M. A. Gibson and J. Bruck, “Efficient exact stochastic simulation of chemical systems with many species and many channels,” The Journal of Physical Chemistry A, vol. 104, no. 9, pp. 1876-1889, 2000.
    • [9] Y. Cao, H. Li, and L. Petzold, “Efficient formulation of the stochastic simulation algorithm for chemically reacting systems,” The Journal of Chemical Physics, vol. 121, no. 9, pp. 4059-4067, 2004.
    • [10] J. M. McCollum, G. D. Peterson, C. D. Cox, M. L. Simpson, and N. F. Samatova, “The sorting direct method for stochastic simulation of biochemical systems with varying reaction execution behavior,” Computational Biology and Chemistry, vol. 30, no. 1, pp. 39 - 49, 2006.
    • [11] H. Li and L. Petzold, “Logarithmic direct method for discrete stochastic simulation of chemically reacting systems.” Department of Computer Science, University of California: Santa Barbara, Tech. Rep., 2006.
    • [12] R. Ramaswamy, N. Gonzalez-Segredo, and I. F. Sbalzarini, “A new class of highly efficient exact stochastic simulation algorithms for chemical reaction networks,” The Journal of Chemical Physics, vol. 130, no. 24, p. 244104, 2009.
    • [13] A. Slepoy, A. P. Thompson, and S. J. Plimpton, “A constant-time kinetic monte carlo algorithm for simulation of large biochemical reaction networks,” The Journal of Chemical Physics, vol. 128, no. 20, p. 205101, 2008.
    • [14] D. T. Gillespie, “Approximate accelerated stochastic simulation of chemically reacting systems,” The Journal of Chemical Physics, vol. 115, no. 4, pp. 1716-1733, 2001.
    • [15] J. Blakes, J. Twycross, F. J. Romero-Campero, and N. Krasnogor, “The Infobiotics Workbench: an integrated in silico modelling platform for systems and synthetic biology,” Bioinformatics, 2011.
    • [16] S. Konur, C. Ladroue, H. Fellermann, D. Sanassy, L. Mierla, F. Ipate, S. Kalvala, M. Gheorghe, and N. Krasnogor, “Modeling and analysis of genetic boolean gates using the Infobiotics Workbench,” in Proceedings of the 2014 Workshop on Verification of Engineered Molecular Devices and Programs, 2014, submitted.
    • [17] D. Sanassy, P. Widera, and N. Krasnogor, “Meta-stochastic Simulation of Biochemical Models for Systems & Synthetic Biology,” in Proceedings of The Synthetic Biology: Engineering, Evolution & Design (SEED) conference, 2014, submitted.
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