Name
Title | ||
|---|---|---|
Eigen-genomic system dynamic-pattern analysis (ESDA): modeling mRNA degradation and self-regulation. |
Abstract | ||
|---|---|---|
High-throughput methods systematically measure the internal state of the entire cell, but powerful computational tools are needed to infer dynamics from their raw data. Therefore, we have developed a new computational method, Eigen-genomic System Dynamic-pattern Analysis (ESDA), which uses systems theory to infer dynamic parameters from a time series of gene expression measurements. As many genes are measured at a modest number of time points, estimation of the system matrix is underdetermined and traditional approaches for estimating dynamic parameters are ineffective; thus, ESDA uses the principle of dimensionality reduction to overcome the data imbalance. Since degradation rates are naturally confounded by self-regulation, our model estimates an effective degradation rate that is the difference between self-regulation and degradation. We demonstrate that ESDA is able to recover effective degradation rates with reasonable accuracy in simulation. We also apply ESDA to a budding yeast dataset, and find that effective degradation rates are normally slower than experimentally measured degradation rates. Our results suggest that either self-regulation is widespread in budding yeast and that self-promotion dominates self-inhibition, or that self-regulation may be rare and that experimental methods for measuring degradation rates based on transcription arrest may severely overestimate true degradation rates in healthy cells. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/TCBB.2011.150 | IEEE/ACM Trans. Comput. Biology Bioinform. |
Keywords | Field | DocType |
degradation rate,degradation,cellular biophysics,genome-wide gene expression,transcription arrest,raw data,mrna,new computational method,esda,genetics,eigen-genomic system dynamic-pattern analysis,singular value decomposition.,microorganisms,genomics,self-regulation,gene expression,dynamic parameter,rna,budding yeast data set,biology computing,molecular biophysics,effective degradation rate,eigengenomic system dynamic-pattern analysis,data imbalance,eigenvalues and eigenvectors,powerful computational tool,overestimate true degradation rate,time point,budding yeast data,mrna degradation,eigenvalues and eigenfunctions,time series,systems theory,singular value decomposition,bioinformatics,system theory,point estimation,time series analysis,high throughput,pattern analysis,oscillators,system dynamics | MRNA degradation,Singular value decomposition,Time series,Dimensionality reduction,Underdetermined system,Computer science,Pattern analysis,Degradation (geology),Molecular biophysics,Bioinformatics | Journal |
Volume | Issue | ISSN |
9 | 2 | 1557-9964 |
Citations | PageRank | References |
1 | 0.40 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daifeng Wang | 1 | 5 | 1.83 |
| Mia K. Markey | 2 | 353 | 33.66 |
| Claus O. Wilke | 3 | 191 | 36.57 |
| Ari Arapostathis | 4 | 192 | 34.43 |