Name
Title | ||
|---|---|---|
An efficient method of exploring simulation models by assimilating literature and biological observational data. |
Abstract | ||
|---|---|---|
Recently, several biological simulation models of, e.g., gene regulatory networks and metabolic pathways, have been constructed based on existing knowledge of biomolecular reactions, e.g., DNA–protein and protein–protein interactions. However, since these do not always contain all necessary molecules and reactions, their simulation results can be inconsistent with observational data. Therefore, improvements in such simulation models are urgently required. A previously reported method created multiple candidate simulation models by partially modifying existing models. However, this approach was computationally costly and could not handle a large number of candidates that are required to find models whose simulation results are highly consistent with the data. In order to overcome the problem, we focused on the fact that the qualitative dynamics of simulation models are highly similar if they share a certain amount of regulatory structures. This indicates that better fitting candidates tend to share the basic regulatory structure of the best fitting candidate, which can best predict the data among candidates. Thus, instead of evaluating all candidates, we propose an efficient explorative method that can selectively and sequentially evaluate candidates based on the similarity of their regulatory structures. Furthermore, in estimating the parameter values of a candidate, e.g., synthesis and degradation rates of mRNA, for the data, those of the previously evaluated candidates can be utilized. The method is applied here to the pharmacogenomic pathways for corticosteroids in rats, using time-series microarray expression data. In the performance test, we succeeded in obtaining more than 80% of consistent solutions within 15% of the computational time as compared to the comprehensive evaluation. Then, we applied this approach to 142 literature-recorded simulation models of corticosteroid-induced genes, and consequently selected 134 newly constructed better models. The method described here was found to be capable of efficiently exploring candidate simulation models and obtaining better models within a short span of time. Furthermore, the results suggest that there may be room for improvement in literature recorded pathways and that they can be systematically updated using biological observational data. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.biosystems.2014.06.001 | Biosystems |
Keywords | Field | DocType |
Gene expression,Pharmacogenome,Simulation,Systems biology | Data mining,Observational study,Computer science,Systems biology,Simulation modeling,Artificial intelligence,Gene regulatory network,Biological simulation,Machine learning | Journal |
Volume | ISSN | Citations |
121 | 0303-2647 | 3 |
PageRank | References | Authors |
0.39 | 9 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Takanori Hasegawa | 1 | 8 | 3.20 |
| Masao Nagasaki | 2 | 368 | 26.22 |
| Rui Yamaguchi | 3 | 180 | 26.49 |
| Seiya Imoto | 4 | 975 | 84.16 |
| Satoru Miyano | 5 | 2406 | 250.71 |