Method for Predicting Gene Expression by Modeling Transcription Factor Activity
Method for Predicting Gene Expression by Modeling Transcription Factor Activity
Method and software used to estimate regulatory connectivities between transcription factor activity and their target genes
New York, NY, United States
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This novel software elucidates the functional connectivity between signaling networks and transcriptional networks, providing new insights into gene regulatory pathways and the mechanisms that control gene expression. Variation in the expression of genes can be traced back to alterations in transcription factor activity, which results from mRNA expression levels. The primary application of this software is to identify chromosomal loci whose genotype affects the activity of a transcription factor (TF). By understanding the variations in transcription factors and their relation to individual gene expression, novel targets for therapeutic intervention can be established.

Quantitative use of transcription factor activity reveals connection between protein variations and gene expression 

The software presents a novel method to map molecular networks between transcription factors and their target genes, thereby explaining regulatory mechanisms of transcription factor activity. By incorporating an initial step where prior information about the connectivity between transcription factors and target genes is used to infer individual transcription factor activity, transcription factor activity itself can be used as a quantitative measurement. Therefore, this software can use existing linkage analysis methods to identify genetic loci and predict a large number of highly specific regulatory interactions between genes and transcription factor activity.

– Provides way to predict clinically relevant phenotypes from genotype information obtained by transcription factor activity predictions
– Identifies regulators of transcription factors. Once identified, the regulators can be drug targets, allowing for the perturbation of network activity with small molecules.

– Prior information about the connectivity between target genes and transcription factors is used to infer transcription factor activity.
– Transcription factor activity can be used as a quantitative trait.
– Existing linkage analysis (QTL) methods can be used to identify genetic loci that modulate transcription factor activity.
– Requires only a single PC for complete analysis, yet he quality of data obtained can be compared to that obtained from the use of a supercomputer.
– Running time for the algorithm is extremely fast, requiring only a few minutes to complete.

Lead Inventor

Harmen J. Bussemaker, Ph.D.

Related Publications:
–  Harmen J. Bussemaker, Barrett C. Foat, and Lucas D. Ward (2007) Predictive modeling of genome-wide mRNA expression: from modules to molecules Annu Rev Biophys Biomol Struct. 36: 329-47

–  E. Lee, HJ Bussemaker (2010) Identifying the genetic determinants of transcription factor activity. Mol Syst Biol 6:412.

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