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Published Online: 6 April 2015

A Scalable Method for Molecular Network Reconstruction Identifies Properties of Targets and Mutations in Acute Myeloid Leukemia

Publication: Journal of Computational Biology
Volume 22, Issue Number 4


A key aim of systems biology is the reconstruction of molecular networks. We do not yet, however, have networks that integrate information from all datasets available for a particular clinical condition. This is in part due to the limited scalability, in terms of required computational time and power, of existing algorithms. Network reconstruction methods should also be scalable in the sense of allowing scientists from different backgrounds to efficiently integrate additional data. We present a network model of acute myeloid leukemia (AML). In the current version (AML 2.1), we have used gene expression data (both microarray and RNA-seq) from 5 different studies comprising a total of 771 AML samples and a protein–protein interactions dataset. Our scalable network reconstruction method is in part based on the well-known property of gene expression correlation among interacting molecules. The difficulty of distinguishing between direct and indirect interactions is addressed by optimizing the coefficient of variation of gene expression, using a validated gold-standard dataset of direct interactions. Computational time is much reduced compared to other network reconstruction methods. A key feature is the study of the reproducibility of interactions found in independent clinical datasets. An analysis of the most significant clusters, and of the network properties (intraset efficiency, degree, betweenness centrality, and PageRank) of common AML mutations demonstrated the biological significance of the network. A statistical analysis of the response of blast cells from 11 AML patients to a library of kinase inhibitors provided an experimental validation of the network. A combination of network and experimental data identified CDK1, CDK2, CDK4, and CDK6 and other kinases as potential therapeutic targets in AML.

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Published In

cover image Journal of Computational Biology
Journal of Computational Biology
Volume 22Issue Number 4April 2015
Pages: 266 - 288
PubMed: 25844667


Published online: 6 April 2015
Published in print: April 2015


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Edison Ong
Salgomed Inc., Del Mar, California.
Anthony Szedlak
Department of Physics and Astronomy, Michigan State University, East Lansing, Michigan.
Yunyi Kang
Sanford-Burnham Medical Research Institute, La Jolla, California.
Peyton Smith
Salgomed Inc., Del Mar, California.
Nicholas Smith
Salgomed Inc., Del Mar, California.
Madison McBride
Sanford-Burnham Medical Research Institute, La Jolla, California.
Darren Finlay
Sanford-Burnham Medical Research Institute, La Jolla, California.
Kristiina Vuori
Sanford-Burnham Medical Research Institute, La Jolla, California.
James Mason
Scripps Health, San Diego, California.
Edward D. Ball
Moores Cancer Center and Department of Medicine, University of California–San Diego, La Jolla, California.
Carlo Piermarocchi
Department of Physics and Astronomy, Michigan State University, East Lansing, Michigan.
Giovanni Paternostro
Sanford-Burnham Medical Research Institute, La Jolla, California.


Address correspondence to:Dr. Carlo PiermarocchiDepartment of Physics and AstronomyMichigan State University220 Trowbridge Rd.East Lansing, MI 48824E-mail: [email protected]
Dr. Giovanni PaternostroSanford-Burnham Medical Research Institute10901 North Torrey Pines RoadLa Jolla, CA 92037E-mail: [email protected]

Author Disclosure Statement

C.P. and G.P. own equity in Salgomed Inc. The remaining coauthors have no competing financial interests.

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