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Efficient Analysis Methods in Synthetic Biology

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Computational Methods in Synthetic Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1244))

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Abstract

This chapter describes new analysis and verification techniques for synthetic genetic circuits. In particular, it applies stochastic model checking techniques to models of genetic circuits in order to ensure that they behave correctly and are as robust as possible for a variety of different inputs and parameter settings. In addition to stochastic model checking, this chapter proposes new variants to the incremental stochastic simulation algorithm (iSSA) that are capable of presenting a researcher with a simulation trace of the typical behavior of the system. Before the development of this algorithm, discerning this information was extremely error-prone as it involved performing many simulations and attempting to wade through the massive amounts of data. This algorithm greatly aids researchers in designing genetic circuits as it efficiently shows the researcher the most likely behavior of the circuit. Both the iSSA and stochastic model checking can be used in concert to give a researcher the likelihood that the system exhibits its most typical behavior, as well as, non-typical behaviors. This methodology is applied to several genetic circuits leading to new understanding of the effects of various parameters on the behavior of these circuits.

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Acknowledgments

The authors would like to thank Eduardo Monzon and Abiezer Tejeda for their work on improvements to the iSSA in its early stages of development.

This material is based upon work supported by the National Science Foundation under Grant Numbers 0331270, CCF-07377655, CCF-0916042, and CCF-1218095. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Author information

Authors and Affiliations

  1. School of Computing, University of Utah, Salt Lake City, UT, USA

    Curtis Madsen

  2. Dept. of Electrical and Computer Engineering, University of Utah, Salt Lake City, UT, USA

    Chris Myers & Zhen Zhang

  3. Dept. of Bioengineering, University of Utah, SLC, UT, USA

    Nicholas Roehner

  4. Dept. of Electrical and Computer Engineering, Utah State University, Logan, UT, USA

    Chris Winstead

Authors
  1. Curtis Madsen
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  2. Chris Myers
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  3. Nicholas Roehner
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  4. Chris Winstead
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  5. Zhen Zhang
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Corresponding author

Correspondence to Curtis Madsen .

Editor information

Editors and Affiliations

  1. School of Life Science and Technology, Harbin Institute of Technology, Harbin, China

    Mario Andrea Marchisio

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Madsen, C., Myers, C., Roehner, N., Winstead, C., Zhang, Z. (2015). Efficient Analysis Methods in Synthetic Biology. In: Marchisio, M. (eds) Computational Methods in Synthetic Biology. Methods in Molecular Biology, vol 1244. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-1878-2_11

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  • DOI: https://doi.org/10.1007/978-1-4939-1878-2_11

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-1877-5

  • Online ISBN: 978-1-4939-1878-2

  • eBook Packages: Springer Protocols

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