Engineering Models to Scale
Name
Engineering Models to Scale-Collins.pdf
Size
3.43 MB
Format
Adobe PDF
Checksum (MD5)
1b07af07bfc75ef65b5536d6b75c6e07
Author(s)
Dy, Aaron James
Collins, James J.
Date Issued
April 2016
Journal
Cell
Publisher
Elsevier B.V.
Citation
Dy, Aaron J., and James J. Collins. “Engineering Models to Scale.” Cell 165, no. 3 (April 2016): 516–517.
Version
Author's final manuscript
Abstract
Main Text
The physicist Richard Feynman famously wrote, “What I cannot create, I do not understand,” at the top of his final blackboard. This philosophy has inspired many in the emerging field of synthetic biology, which harnesses the power of biology to rationally engineer biomolecular systems for a variety of purposes, such as whole-cell biosensing and in vivo diagnostics (Slomovic et al., 2015). The “build-to-understand” approach (Elowitz and Lim, 2010) is complementary to top-down systems biology approaches and borrows concepts and techniques from engineering and computer science. By creating biological systems with desired architectures and functions, it aims to test design principles in relative isolation by exploring how biology’s building blocks, such as DNA-encoded genes, can be rearranged and altered to produce different phenotypes. In this issue, Cao et al. use this approach to tackle the question of how self-organizing systems maintain a constant ratio of physical pattern features with changing size, a property known as scale invariance (Cao et al., 2016).
MIT Department
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Massachusetts Institute of Technology. Synthetic Biology Center
Massachusetts Institute of Technology. Department of Biological Engineering
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.cell.2016.04.017
