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An algorithmic framework for network reconstruction
- Source :
- Theoretical Computer Science. (26):2800-2815
- Publisher :
- Elsevier B.V.
-
Abstract
- Models of biological systems and phenomena are of high scientific interest and practical relevance, but not always easy to obtain due to their inherent complexity. To gain the required insight, experimental data are provided and need to be interpreted in terms of models that explain the observed phenomena. In systems biology the framework of Petri nets is often used to describe models for the regulatory mechanisms of biological systems. The aim of this paper is to provide, based on results in Marwan et al. (2008) [1] and Durzinsky et al. (2008) [2], an algorithmic framework for the challenging task of generating all possible Petri nets fitting the given experimental data.
- Subjects :
- Theoretical computer science
General Computer Science
Computer science
business.industry
Systems biology
Petri nets
Petri net
Process architecture
Theoretical Computer Science
Task (project management)
Computational biology
Relevance (information retrieval)
Integer decomposition
Artificial intelligence
business
Reverse engineering
Computer Science(all)
Subjects
Details
- Language :
- English
- ISSN :
- 03043975
- Issue :
- 26
- Database :
- OpenAIRE
- Journal :
- Theoretical Computer Science
- Accession number :
- edsair.doi.dedup.....517014ba50fa17c636aecebd9d0ebdbf
- Full Text :
- https://doi.org/10.1016/j.tcs.2010.08.016