Campus Units

Electrical and Computer Engineering, Biomedical Sciences

Document Type

Article

Publication Version

Submitted Manuscript

Publication Date

2015

Journal or Book Title

IEEE/ACM Transactions on Computational Biology and Bioinformatics

Issue

99

First Page

1545

Last Page

5963

DOI

10.1109/TCBB.2015.2509992

Abstract

The reconstruction of gene regulatory networks from gene expression data has been the subject of intense research activity. A variety of models and methods have been developed to address different aspects of this important problem. However, these techniques are narrowly focused on particular biological and experimental platforms, and require experimental data that are typically unavailable and difficult to ascertain. The more recent availability of higher-throughput sequencing platforms, combined with more precise modes of genetic perturbation, presents an opportunity to formulate more robust and comprehensive approaches to gene network inference. Here, we propose a step-wise framework for identifying gene-gene regulatory interactions that expand from a known point of genetic or chemical perturbation using time series gene expression data. This novel approach sequentially identifies non-steady state genes post-perturbation and incorporates them into a growing series of low-complexity optimization problems. The governing ordinary differential equations of this model are rooted in the biophysics of stochastic molecular events that underlie gene regulation, delineating roles for both protein and RNA-mediated gene regulation. We show the successful application of our core algorithms for network inference using simulated and real datasets.

Comments

This article is published as Zamanighomi, Mahdi, Mostafa Zamanian, Michael Kimber, and Zhengdao Wang. "Gene regulatory network inference from perturbed time-series expression data via ordered dynamical expansion of non-steady state actors." IEEE/ACM transactions on computational biology and bioinformatics (2015). doi: 10.1109/TCBB.2015.2509992. Posted with permission.

Rights

© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Copyright Owner

IEEE

Language

en

File Format

application/pdf

Published Version

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