Abstract
The inability to rationally design and construct circuits that robustly enable complex behaviors is perhaps the most fundamental challenge in synthetic biology. While systems modeling can aid this process and help reduce the space of design strategies, the unavailability and dynamic variability of kinetic parameters limits the utility of such models. Here, we present a general approach that employs an exhaustive enumeration of network architectures to suggest topologies that robustly enable a desired behavior.
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References
Kwok R (2010) Five hard truths for synthetic biology. Nature 463:288–290
Purnick PEM, Weiss R (2009) The second wave of synthetic biology: from modules to systems. Nat Rev Mol Cell Biol 10:410–422
Ma W, Trusina A, El-Samad H et al (2009) Defining network topologies that can achieve biochemical adaptation. Cell 138:760–773
Shah NA, Sarkar CA (2011) Robust network topologies for generating switch-like cellular responses. PLOS Comput Biol 7:e1002085
Goldbeter A, Koshland DE (1981) An amplified sensitivity arising from covalent modification in biological systems. Proc Natl Acad Sci U S A 78:6840–6844
Iman RL, Davenport JM, Zeigler DK (1980) Latin Hypercube Sampling (Program User's Guide). Technical Report SAND79-1473, Sandia National Laboratories, Albuquerque, NM
Hastie T, Tibshirani R, Friedman J (2013) The elements of statistical learning. Springer, New York
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Shah, N.A., Sarkar, C.A. (2015). Computationally Guided Design of Robust Gene Circuits. 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_8
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DOI: https://doi.org/10.1007/978-1-4939-1878-2_8
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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
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