Control theory meets synthetic biology
Name
Paperddv_v5.pdf
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1.93 MB
Format
Adobe PDF
Checksum (MD5)
4734c639c4504669cdd0495b10ab0393
Author(s)
Del Vecchio, Domitilla
Dy, Aaron James
Qian, Yili
Date Issued
July 2016
Journal
Journal of The Royal Society Interface
Publisher
Royal Society Publishing
Citation
Del Vecchio, Domitilla, Aaron J. Dy, and Yili Qian. “Control Theory Meets Synthetic Biology.” Journal of The Royal Society Interface 13, no. 120 (July 2016): 20160380.
Version
Author's final manuscript
Abstract
The past several years have witnessed an increased presence of control theoretic concepts in synthetic biology. This review presents an organized summary of how these control design concepts have been applied to tackle a variety of problems faced when building synthetic biomolecular circuits in living cells. In particular, we describe success stories that demonstrate how simple or more elaborate control design methods can be used to make the behaviour of synthetic genetic circuits within a single cell or across a cell population more reliable, predictable and robust to perturbations. The description especially highlights technical challenges that uniquely arise from the need to implement control designs within a new hardware setting, along with implemented or proposed solutions. Some engineering solutions employing complex feedback control schemes are also described, which, however, still require a deeper theoretical analysis of stability, performance and robustness properties. Overall, this paper should help synthetic biologists become familiar with feedback control concepts as they can be used in their application area. At the same time, it should provide some domain knowledge to control theorists who wish to enter the rising and exciting field of synthetic biology.
MIT Department
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Department of Mechanical Engineering
Massachusetts Institute of Technology. Synthetic Biology Center
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1098/RSIF.2016.0380
