Adaptive models for gene networks.
Publication
, Journal Article
Shin, Y-J; Sayed, AH; Shen, X
Published in: PLoS One
2012
Biological systems are often treated as time-invariant by computational models that use fixed parameter values. In this study, we demonstrate that the behavior of the p53-MDM2 gene network in individual cells can be tracked using adaptive filtering algorithms and the resulting time-variant models can approximate experimental measurements more accurately than time-invariant models. Adaptive models with time-variant parameters can help reduce modeling complexity and can more realistically represent biological systems.
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Published In
PLoS One
DOI
EISSN
1932-6203
Publication Date
2012
Volume
7
Issue
2
Start / End Page
e31657
Location
United States
Related Subject Headings
- Tumor Suppressor Protein p53
- Time Factors
- Systems Biology
- Proto-Oncogene Proteins c-mdm2
- Models, Genetic
- Humans
- General Science & Technology
- Gene Regulatory Networks
- Algorithms
Citation
APA
Chicago
ICMJE
MLA
NLM
Shin, Y.-J., Sayed, A. H., & Shen, X. (2012). Adaptive models for gene networks. PLoS One, 7(2), e31657. https://doi.org/10.1371/journal.pone.0031657
Shin, Yong-Jun, Ali H. Sayed, and Xiling Shen. “Adaptive models for gene networks.” PLoS One 7, no. 2 (2012): e31657. https://doi.org/10.1371/journal.pone.0031657.
Shin Y-J, Sayed AH, Shen X. Adaptive models for gene networks. PLoS One. 2012;7(2):e31657.
Shin, Yong-Jun, et al. “Adaptive models for gene networks.” PLoS One, vol. 7, no. 2, 2012, p. e31657. Pubmed, doi:10.1371/journal.pone.0031657.
Shin Y-J, Sayed AH, Shen X. Adaptive models for gene networks. PLoS One. 2012;7(2):e31657.
Published In
PLoS One
DOI
EISSN
1932-6203
Publication Date
2012
Volume
7
Issue
2
Start / End Page
e31657
Location
United States
Related Subject Headings
- Tumor Suppressor Protein p53
- Time Factors
- Systems Biology
- Proto-Oncogene Proteins c-mdm2
- Models, Genetic
- Humans
- General Science & Technology
- Gene Regulatory Networks
- Algorithms