Abstract
Ordinary Differential Equations (ODEs) represent a deterministic approach to model gene regulatory networks. ODEs can be used to model changes in gene transcription induced by an external perturbation, such as gene overexpression/downregulation or treatment with a drug. Reverse-engineering algorithms based on ODEs require a choice of a functional form describing the effect of a regulator on its target genes. Here, we focused on an ODE-based reverse engineering algorithm named Network Identification by multiple Regression (NIR) which is rooted on the hypothesis that the regulation exerted by one gene (i.e., a TF) on a target gene can be approximated by a linear function, i.e., the transcription rate of the target gene is proportional to the amount of TF. NIR uses steady-state gene expression measurements and requires knowledge of the genes perturbed in each experiment. We showed that even if originally NIR was created for a different purpose, it can be successfully used to infer gene regulation from an integrated genotype and phenotype dataset. Our results provide evidence of the feasibility of applying reverse-engineering algorithms, such as NIR, to infer gene regulatory networks by integrated analysis of genotype and phenotype.
Original language | English |
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Title of host publication | Gene Network Inference: Verification of Methods for Systems Genetics Data |
Publisher | Springer-Verlag Berlin Heidelberg |
Pages | 49-61 |
Number of pages | 13 |
ISBN (Print) | 9783642451614, 3642451608, 9783642451607 |
DOIs | |
Publication status | Published - Dec 1 2013 |
Keywords
- Gene networks
- ODE
- Reverse engineering
ASJC Scopus subject areas
- Agricultural and Biological Sciences(all)
- Biochemistry, Genetics and Molecular Biology(all)
- Medicine(all)