Efficient experimental design for uncertainty reduction in gene regulatory networks

BMC Bioinformatics

Proceedings

Roozbeh Dehghannasiri¹  Edward R Dougherty¹  Byung-Jun Yoon² 

[1] Department of Electrical and Computer Engineering, Texas A&M University, 77843, College Station, TX, USA;Center for Bioinformatics and Genomic Systems Engineering, Texas A&M University, 77845, College Station, TX, USA;Department of Electrical and Computer Engineering, Texas A&M University, 77843, College Station, TX, USA;Center for Bioinformatics and Genomic Systems Engineering, Texas A&M University, 77845, College Station, TX, USA;College of Science and Engineering, Hamad bin Khalifa University (HBKU), Doha, Qatar;

关键词: Experimental design;    gene regulatory networks;    mean objective cost of uncertainty;    objective-based network reduction;    Boolean networks;    structural intervention;   

DOI  :  10.1186/1471-2105-16-S13-S2

来源: Springer

【 摘 要 】

BackgroundAn accurate understanding of interactions among genes plays a major role in developing therapeutic intervention methods. Gene regulatory networks often contain a significant amount of uncertainty. The process of prioritizing biological experiments to reduce the uncertainty of gene regulatory networks is called experimental design. Under such a strategy, the experiments with high priority are suggested to be conducted first.ResultsThe authors have already proposed an optimal experimental design method based upon the objective for modeling gene regulatory networks, such as deriving therapeutic interventions. The experimental design method utilizes the concept of mean objective cost of uncertainty (MOCU). MOCU quantifies the expected increase of cost resulting from uncertainty. The optimal experiment to be conducted first is the one which leads to the minimum expected remaining MOCU subsequent to the experiment. In the process, one must find the optimal intervention for every gene regulatory network compatible with the prior knowledge, which can be prohibitively expensive when the size of the network is large. In this paper, we propose a computationally efficient experimental design method. This method incorporates a network reduction scheme by introducing a novel cost function that takes into account the disruption in the ranking of potential experiments. We then estimate the approximate expected remaining MOCU at a lower computational cost using the reduced networks.ConclusionsSimulation results based on synthetic and real gene regulatory networks show that the proposed approximate method has close performance to that of the optimal method but at lower computational cost. The proposed approximate method also outperforms the random selection policy significantly. A MATLAB software implementing the proposed experimental design method is available at http://gsp.tamu.edu/Publications/supplementary/roozbeh15a/.

【 授权许可】

Unknown   
© Dehghannasiri et al. 2015. 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 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.