A systems' biology approach to study microrna-mediated gene regulatory networks
Lai, X., Bhattacharya, A., Schmitz, U., Kunz, M., Vera, J., and Wolkenhauer, O. (2013) A systems' biology approach to study microrna-mediated gene regulatory networks. BioMed Research International, 2013. 703849.
|
PDF (Published Version)
- Published Version
Available under License Creative Commons Attribution. Download (2MB) | Preview |
Abstract
MicroRNAs (miRNAs) are potent effectors in gene regulatory networks where aberrant miRNA expression can contribute to human diseases such as cancer. For a better understanding of the regulatory role of miRNAs in coordinating gene expression, we here present a systems biology approach combining data-driven modeling and model-driven experiments. Such an approach is characterized by an iterative process, including biological data acquisition and integration, network construction, mathematical modeling and experimental validation. To demonstrate the application of this approach, we adopt it to investigate mechanisms of collective repression on p21 by multiple miRNAs. We first construct a p21 regulatory network based on data from the literature and further expand it using algorithms that predict molecular interactions. Based on the network structure, a detailed mechanistic model is established and its parameter values are determined using data. Finally, the calibrated model is used to study the effect of different miRNA expression profiles and cooperative target regulation on p21 expression levels in different biological contexts.
Item ID: | 69004 |
---|---|
Item Type: | Article (Research - C1) |
ISSN: | 2314-6141 |
Copyright Information: | © 2013 Xin Lai et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Date Deposited: | 25 Jun 2024 02:57 |
FoR Codes: | 31 BIOLOGICAL SCIENCES > 3101 Biochemistry and cell biology > 310114 Systems biology @ 100% |
SEO Codes: | 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280102 Expanding knowledge in the biological sciences @ 100% |
Downloads: |
Total: 14 Last 12 Months: 7 |
More Statistics |