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| Content Provider | ACM Digital Library |
|---|---|
| Author | Müller, Stefan Flamm, Christoph Hofacker, Ivo Kügler, Philipp Hsiao, Yu-Ting Lee, Wei-Po Yang, Wei |
| Copyright Year | 2016 |
| Abstract | Modeling gene regulatory networks (GRNs) is essential for conceptualizing how genes are expressed and how they influence each other. Typically, a reverse engineering approach is employed; this strategy is effective in reproducing possible fitting models of GRNs. To use this strategy, however, two daunting tasks must be undertaken: one task is to optimize the accuracy of inferred network behaviors; and the other task is to designate valid biological topologies for target networks. Although existing studies have addressed these two tasks for years, few of the studies can satisfy both of the requirements simultaneously. To address these difficulties, we propose an integrative modeling framework that combines knowledge-based and data-driven input sources to construct biological topologies with their corresponding network behaviors. To validate the proposed approach, a real dataset collected from the cell cycle of the yeast S. cerevisiae is used. The results show that the proposed framework can successfully infer solutions that meet the requirements of both the network behaviors and biological structures. Therefore, the outcomes are exploitable for future in vivo experimental design. |
| Starting Page | 64 |
| Ending Page | 75 |
| Page Count | 12 |
| File Format | |
| ISSN | 15455963 |
| DOI | 10.1109/TCBB.2015.2465954 |
| Volume Number | 13 |
| Issue Number | 1 |
| Journal | IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2016-01-01 |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | S-system Evolutionary algorithms Gene regulatory network modeling Prior knowledge-mapping |
| Content Type | Text |
| Resource Type | Article |
| Subject | Genetics Biotechnology Applied Mathematics |
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