Journal Article

An integer programming formulation to identify the sparse network architecture governing differentiation of embryonic stem cells

Ipsita Banerjee, Spandan Maiti, Natesh Parashurama and Martin Yarmush

in Bioinformatics

Volume 26, issue 10, pages 1332-1339
Published in print May 2010 | ISSN: 1367-4803
Published online March 2010 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btq139
An integer programming formulation to identify the sparse network architecture governing differentiation of embryonic stem cells

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Motivation: Primary purpose of modeling gene regulatory networks for developmental process is to reveal pathways governing the cellular differentiation to specific phenotypes. Knowledge of differentiation network will enable generation of desired cell fates by careful alteration of the governing network by adequate manipulation of cellular environment.

Results: We have developed a novel integer programming-based approach to reconstruct the underlying regulatory architecture of differentiating embryonic stem cells from discrete temporal gene expression data. The network reconstruction problem is formulated using inherent features of biological networks: (i) that of cascade architecture which enables treatment of the entire complex network as a set of interconnected modules and (ii) that of sparsity of interconnection between the transcription factors. The developed framework is applied to the system of embryonic stem cells differentiating towards pancreatic lineage. Experimentally determined expression profile dynamics of relevant transcription factors serve as the input to the network identification algorithm. The developed formulation accurately captures many of the known regulatory modes involved in pancreatic differentiation. The predictive capacity of the model is tested by simulating an in silico potential pathway of subsequent differentiation. The predicted pathway is experimentally verified by concurrent differentiation experiments. Experimental results agree well with model predictions, thereby illustrating the predictive accuracy of the proposed algorithm.

Contact: ipb1@pitt.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  6229 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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