Finding Instability in Biological Models
- Submitting institution
-
University College London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 13940
- Type
- E - Conference contribution
- DOI
-
10.1007/978-3-319-08867-9_24
- Title of conference / published proceedings
- COMPUTER AIDED VERIFICATION, CAV 2014
- First page
- 358
- Volume
- 8559
- Issue
- -
- ISSN
- 0302-9743
- Open access status
- Out of scope for open access requirements
- Month of publication
- January
- Year of publication
- 2014
- URL
-
-
- Supplementary information
-
https://link.springer.com/chapter/10.1007%2F978-3-319-08867-9_24
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
5
- Research group(s)
-
-
- Citation count
- 2
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The stability of biological models (a.k.a. homeostatis) is key to building computer-aided tools for finding cancer drugs targets. This paper developed algorithm for proving stability of industrial-sized problems. The paper proves instability/stability in realistic biological models, including adaptation in bacterial chemotaxis, the lambda phage lysogeny/lysis switch, voltage gated channel opening and cAMP oscillations in the slime mold Dictyostelium discoideum. This approach went on to support the development of new clinically relevant tools for industrial biomedicine, leading ultimately to the biological results reported in the Nature scientific report “Drug Target Optimization in Chronic Myeloid Leukemia Using Innovative Computational Platform”.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -