Name
Abstract | ||
|---|---|---|
Modern experimental techniques for time course measurement of gene expression enable the identification of dynamical models of genetic regulatory networks. In general, identification involves fitting appropriate network structures and parameters to the data. For a given set of genes, exploring all possible network structures is clearly prohibitive. Modelling and identification methods for the a priori selection of network structures compatible with biological knowledge and experimental data are necessary to make the identification problem tractable.We propose a differential equation modelling framework where the regulatory interactions among genes are expressed in terms of unate functions, a class of gene activation rules commonly encountered in Boolean network modelling. We establish analytical properties of the models in the class and exploit them to devise a two-step procedure for gene network reconstruction from product concentration and synthesis rate time series. The first step isolates a family of model structures compatible with the data from a set of most relevant biological hypotheses. The second step explores this family and returns a pool of best fitting models along with estimates of their parameters. The method is tested on a simulated network and compared with state-of-the-art network inference methods on the benchmark synthetic network IRMA. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1093/bioinformatics/btq120 | Bioinformatics |
Keywords | Field | DocType |
boolean network modelling,state-of-the-art network inference method,network structure,simulated network,unate structure,benchmark synthetic network,genetic regulatory network,experimental data,gene network reconstruction,possible network structure,genetic network dynamic,fitting appropriate network structure | Boolean network,Data mining,Computer science,Inference,A priori and a posteriori,Network architecture,Bioinformatics,Gene regulatory network,System identification,Parameter identification problem,Biological network inference | Journal |
Volume | Issue | ISSN |
26 | 9 | 1367-4811 |
Citations | PageRank | References |
7 | 0.62 | 13 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Riccardo Porreca | 1 | 44 | 4.67 |
| Eugenio Cinquemani | 2 | 107 | 11.96 |
| John Lygeros | 3 | 2742 | 319.22 |
| Giancarlo Ferrari-Trecate | 4 | 831 | 77.29 |