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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Pal, R. Bhattacharya, S. |
| Copyright Year | 1991 |
| Abstract | Fine-scale models based on stochastic master equations can provide the most detailed description of the dynamics of gene expression and imbed, in principle, all the information about the biochemical reactions involved in gene interactions. However, there is limited time-series experimental data available for inference of such fine-scale models. Furthermore, the computational complexity involved in the design of optimal intervention strategies to favorably effect system dynamics for such detailed models is enormous. Thus, there is a need to design mappings from fine-scale models to coarse-scale models while maintaining sufficient structure for the problem at hand and to study the effect of intervention policies designed using coarse-scale models when applied to fine-scale models. In this paper, we propose a mapping from a fine-scale model represented by a stochastic master equation to a coarse-scale model represented by a probabilistic Boolean network that maintains the collapsed steady state probability distribution of the detailed model. We also derive bounds on the performance of the intervention strategy designed using the coarse-scale model when applied to the fine-scale model. |
| Sponsorship | IEEE Signal Processing Society |
| Starting Page | 3341 |
| Ending Page | 3351 |
| Page Count | 11 |
| File Size | 566426 |
| File Format | |
| ISSN | 1053587X |
| Volume Number | 58 |
| Issue Number | 6 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-06-01 |
| Publisher Place | U.S.A. |
| Access Restriction | One Nation One Subscription (ONOS) |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Genetics Stochastic processes Equations Biological system modeling Mathematical model Gene expression Neoplasms Systems biology Medical diagnostic imaging Computational complexity stochastic master equations Markov chain models probabilistic Boolean networks relationships between genetic regulatory network models |
| Content Type | Text |
| Resource Type | Article |
| Subject | Signal Processing Electrical and Electronic Engineering |
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