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Integrative biology : quantitative biosciences from nano to macro
Apr 18, 2016;8 (4): 456-64.

Addressing biological uncertainties in engineering gene circuits.

Carolyn Zhang, Ryan Tsoi, Lingchong You
Author Information
  1. Carolyn Zhang: Department of Biomedical Engineering, Duke University, Durham, North Carolina 27708, USA.
PMID: 26674800 DOI: 10.1039/c5ib00275c

Abstract

Synthetic biology has grown tremendously over the past fifteen years. It represents a new strategy to develop biological understanding and holds great promise for diverse practical applications. Engineering of a gene circuit typically involves computational design of the circuit, selection of circuit components, and test and optimization of circuit functions. A fundamental challenge in this process is the predictable control of circuit function due to multiple layers of biological uncertainties. These uncertainties can arise from different sources. We categorize these uncertainties into incomplete quantification of parts, interactions between heterologous components and the host, or stochastic dynamics of chemical reactions and outline potential design strategies to minimize or exploit them.

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Grants

  1. R01 GM110494/NIGMS NIH HHS
  2. R01 GM106107/NIGMS NIH HHS
  3. R01GM110494/NIGMS NIH HHS
  4. R01GM098642/NIGMS NIH HHS
  5. R01 GM098642/NIGMS NIH HHS

MeSH Term

Animals
Escherichia coli
Gene Regulatory Networks
Genetic Engineering
Hot Temperature
Humans
Hydrogen-Ion Concentration
Kinetics
Light
Models, Chemical
Software
Stochastic Processes
Synthetic Biology
Transcription, Genetic
Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. Review
Article has an altmetric score of 5

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