Using the message passing algorithm on discrete data to detect faults in boolean regulatory networks

Algorithms for Molecular Biology

Anwoy Kumar Mohanty¹  Aniruddha Datta¹  Vijayanagaram Venkatraj² 

[1] Department of Electrical and Computer Engineering, Texas A&M University, College Station 77843, USA

[2] Department of Veterinary Integrated Biosciences, College of Veterinary Medicine, Texas A&M University, College Station 77845, USA

关键词: Markov chain Monte-Carlo;    Sum-product;    Message passing;   

Others  :  1082121
DOI  :  10.1186/s13015-014-0020-6

 received in 2014-04-16, accepted in 2014-07-09,  发布年份 2014

【 摘 要 】

Background

An important problem in systems biology is to model gene regulatory networks which can then be utilized to develop novel therapeutic methods for cancer treatment. Knowledge about which proteins/genes are dysregulated in a regulatory network, such as in the Mitogen Activated Protein Kinase (MAPK) Network, can be used not only to decide upon which therapy to use for a particular case of cancer, but also help in discovering effective targets for new drugs.

Results

In this work we demonstrate how one can start from a model signal transduction network derived from prior knowledge, and infer from gene expression data the probable locations of dysregulations in the network. Our model is based on Boolean networks, and the inference problem is solved using a version of the message passing algorithm. We have done simulation experiments on synthetic data to verify the efficacy of the algorithm as compared to the results from the much more computationally intensive Markov Chain Monte-Carlo methods. We also applied the model to analyze data collected from fibroblasts, thereby demonstrating how this model can be used on real world data.

【 授权许可】

   
2014 Mohanty et al.; licensee BioMed Central

【 图 表 】

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