Prediction of a Gene Regulatory Network in Cancer Cells
Date
2017-11-15
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Department of Computer Science and Engineering (CSE), Islamic University of Technology (IUT), Board Bazar, Gazipur-1704, Bangladesh
Abstract
The invention of high throughput technology like microarrays has enabled us to better understand how different cellular components interact. Thus created great interest in the field of Gene Regulatory Network(GRN) in particular. The interplay of interactions between DNA, RNA and proteins leads to genetic regulatory networks (GRN) and in turn controls the gene regulation. Directly or indirectly in a cell such molecules either interact in a positive or in
repressive manner therefore it is hard to obtain the accurate computational models through which the final state of a cell can be predicted with certain accuracy. A variety of models and methods have been developed to address different aspects of GRN. Using the Time series data and applying it to these models researchers generate meaningful results i.e. how genes interact with one another. However results found are not of much accuracy due to presence of intrinsic noise of the expression measurements. In order to produce more accurate GRNs using one of the many models available, a new technique is proposed here.
Description
Supervised by
Tareque Mohmud Chowdhury,
Assistant Professor,
Department of Computer Science and Engineering (CSE),
Islamic University of Technology (IUT),
Board Bazar, Gazipur-1704, Bangladesh.
Keywords
Citation
Bandres E, et al. ,microRNA-451 regulates macrophage migration inhibitory factor production and proliferation of gastrointestinal cancer cells, Clin. Cancer Res., 2009 Barabási A, Oltvai Z. Network biology: understanding the cell's functional organization, Nat. Rev. Genet. , 2004 Bonnet E, et al. Module network inference from a cancer gene expression data set identifies microRNA regulated modules, PLoS ONE , 2010 Michoel T, et al. Validating module network learning algorithms using simulated data, BMC Bioinformatics, 2011 Michoel T, et al. Comparative analysis of module-based versus direct methods for reverse-engineering transcriptional regulatory networks, BMC Syst. Biol. , 2009 PollardJ. , Tumour-educated macrophages promote tumour progression and metastasis, Nat. Rev. Cancer , 2008 XiY et al. ,Differentially regulated micro-RNAs and actively translated messenger RNA transcripts by tumor suppressor p53 in colon cancer, Clin. Cancer Res. , 2006 Joshi A, et al. Analysis of a Gibbs sampler method for model-based clustering of gene expression data, Bioinformatics,2008