Anomaly detection in gene expression via stochastic models of gene regulatory networks

BMC Genomics
Haseong Kim, Erol Gelenbe

Abstract

The steady-state behaviour of gene regulatory networks (GRNs) can provide crucial evidence for detecting disease-causing genes. However, monitoring the dynamics of GRNs is particularly difficult because biological data only reflects a snapshot of the dynamical behaviour of the living organism. Also most GRN data and methods are used to provide limited structural inferences. In this study, the theory of stochastic GRNs, derived from G-Networks, is applied to GRNs in order to monitor their steady-state behaviours. This approach is applied to a simulation dataset which is generated by using the stochastic gene expression model, and observe that the G-Network properly detects the abnormally expressed genes in the simulation study. In the analysis of real data concerning the cell cycle microarray of budding yeast, our approach finds that the steady-state probability of CLB2 is lower than that of other agents, while most of the genes have similar steady-state probabilities. These results lead to the conclusion that the key regulatory genes of the cell cycle can be expressed in the absence of CLB type cyclines, which was also the conclusion of the original microarray experiment study. G-networks provide an efficient way to monitor ste...Continue Reading

References

Aug 1, 1977·Proceedings of the National Academy of Sciences of the United States of America·D V GoeddelM H Caruthers
Feb 4, 1997·Proceedings of the National Academy of Sciences of the United States of America·H H McAdams, A Arkin
Dec 7, 2000·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·N FriedmanD Pe'er
Jul 5, 2001·Proceedings of the National Academy of Sciences of the United States of America·M Thattai, A van Oudenaarden
Oct 14, 2004·Bioinformatics·Juliane Schäfer, Korbinian Strimmer
Jun 24, 2005·Proceedings of the National Academy of Sciences of the United States of America·Nicolas E BuchlerTerence Hwa
Oct 4, 2005·Proceedings of the National Academy of Sciences of the United States of America·Dmitri BratsunJeff Hasty
Dec 20, 2005·Cell·Ido GoldingEdward C Cox
Dec 7, 2006·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·Andre RibeiroStuart A Kauffman
Jan 25, 2007·Nature Reviews. Molecular Cell Biology·Joanna Bloom, Frederick R Cross
Oct 13, 2007·Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics·Erol Gelenbe
Apr 5, 2008·Neural Computation·Erol Gelenbe, Stelios Timotheou
Jul 17, 2008·Comparative and Functional Genomics·Ilya ShmulevichWei Zhang
Jul 28, 2009·Journal of Bioinformatics and Computational Biology·Haseong KimTaesung Park

Citations

Jan 9, 2010·BMC Genomics·Shoba RanganathanTin Wee Tan
Dec 7, 2011·IEEE/ACM Transactions on Computational Biology and Bioinformatics·Haseong Kim, Erol Gelenbe
Feb 5, 2015·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·Keith NotoDonna K Slonim
Oct 6, 2017·Nucleic Acids Research·Xiangtian YuLuonan Chen

Related Concepts

Biometry
Cell Cycle
Endomycopsis
Probability
Stochastic Processes
Gene Expression
Gene Expression Regulation, Fungal
Gene Modules
Congenital Abnormality
Cell Cycle

Related Feeds

Cell Cycle Pathways

Cell cycle is a complex process regulated by several signal transduction pathways and enzymes. Here is the latest research on regulation of cell cycle and cell cycle pathways.

Cell Checkpoints & Regulators

Cell cycle checkpoints are a series of complex checkpoint mechanisms that detect DNA abnormalities and ensure that DNA replication and repair are complete before cell division. They are primarily regulated by cyclins, cyclin-dependent kinases, and the anaphase-promoting complex/cyclosome. Here is the latest research.