Multiscale models for synthetic biology

Yiannis N. Kaznessis, Vassilios Sotiropoulos, Anthony Hill, Jonathan Tomshine, Katherine Volzing, John S Barrett, Emma Weeding

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Reacting systems away from the thermodynamic limit cannot be accurately modeled with ordinary differential equations. These continuous-deterministic modeling formalisms, traditionally developed and used by chemical engineers can be distinctly false if the number of molecules of reacting chemical species is very small, or if reaction events are very rare. Then stochastic-discrete representations are appropriate. Importantly, in cases where in a network of reactions there are some parts that must be modeled discretely and stochastically, yet others can be modeled continuously and deterministically, the need for development of multiscale models emerges naturally. In computational synthetic biology, such cases arise often. In this work we present the development of multiscae models for synthetic biology applications, demonstrating accuracy, computational efficiency and utility.

Original languageEnglish (US)
Title of host publicationProceedings of the 2008 International Conference on Bioinformatics and Computational Biology, BIOCOMP 2008
Pages835-837
Number of pages3
StatePublished - Dec 1 2008
Event2008 International Conference on Bioinformatics and Computational Biology, BIOCOMP 2008 - Las Vegas, NV, United States
Duration: Jul 14 2008Jul 17 2008

Other

Other2008 International Conference on Bioinformatics and Computational Biology, BIOCOMP 2008
CountryUnited States
CityLas Vegas, NV
Period7/14/087/17/08

Keywords

  • Chemical Langevin Equations
  • Kinetic Monte Carlo
  • Stochastic simulations
  • Synthetic biology

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  • Cite this

    Kaznessis, Y. N., Sotiropoulos, V., Hill, A., Tomshine, J., Volzing, K., Barrett, J. S., & Weeding, E. (2008). Multiscale models for synthetic biology. In Proceedings of the 2008 International Conference on Bioinformatics and Computational Biology, BIOCOMP 2008 (pp. 835-837)