- Title
- Simultaneous learning of instantaneous and time-delayed genetic interactions using novel information theoretic scoring technique
- Creator
- Morshed, Nizamul; Chetty, Madhu; Nguyen, Vinh
- Date
- 2012
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/157258
- Identifier
- vital:11547
- Identifier
- ISSN:1752-0509
- Identifier
-
https://doi.org/10.1186/1752-0509-6-62
- Abstract
- Background: Understanding gene interactions is a fundamental question in systems biology. Currently, modeling of gene regulations using the Bayesian Network (BN) formalism assumes that genes interact either instantaneously or with a certain amount of time delay. However in reality, biological regulations, both instantaneous and time-delayed, occur simultaneously. A framework that can detect and model both these two types of interactions simultaneously would represent gene regulatory networks more accurately. Results: In this paper, we introduce a framework based on the Bayesian Network (BN) formalism that can represent both instantaneous and time-delayed interactions between genes simultaneously. A novel scoring metric having firm mathematical underpinnings is also proposed that, unlike other recent methods, can score both interactions concurrently and takes into account the reality that multiple regulators can regulate a gene jointly, rather than in an isolated pair-wise manner. Further, a gene regulatory network (GRN) inference method employing an evolutionary search that makes use of the framework and the scoring metric is also presented. Conclusion: By taking into consideration the biological fact that both instantaneous and time-delayed regulations can occur among genes, our approach models gene interactions with greater accuracy. The proposed framework is efficient and can be used to infer gene networks having multiple orders of instantaneous and time-delayed regulations simultaneously. Experiments are carried out using three different synthetic networks (with three different mechanisms for generating synthetic data) as well as real life networks of Saccharomyces cerevisiae, E. coli and cyanobacteria gene expression data. The results show the effectiveness of our approach.
- Relation
- BMC systems biology Vol. 6, no. 62 (2012), p. 62
- Rights
- Copyright BMC
- Rights
- http://creativecommons.org/licenses/by/2.0
- Rights
- This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- Rights
- This metadata is freely available under a CCO license
- Subject
- System biology; Genetic regulation; Gene regulatory networks; Saccharomyces cerevisiae; Time delay systems
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