A statistical approach reveals designs for the most robust stochastic gene oscillators
- Submitting institution
-
University College London
(joint submission with Birkbeck College and Institute of Zoology)
- Unit of assessment
- 5 - Biological Sciences
- Output identifier
- 3748
- Type
- D - Journal article
- DOI
-
10.1021/acssynbio.5b00179
- Title of journal
- ACS Synthetic Biology
- Article number
- -
- First page
- 459
- Volume
- 5
- Issue
- 6
- ISSN
- 2161-5063
- Open access status
- Out of scope for open access requirements
- Month of publication
- February
- Year of publication
- 2016
- URL
-
-
- Supplementary information
-
https://pubs.acs.org/doi/suppl/10.1021/acssynbio.5b00179/suppl_file/sb5b00179_si_001.pdf
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
-
-
- Citation count
- 29
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- -
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
