Inferring gene regulatory networks from raw data--a molecular epistemics approach.
Biopathways play an important role in the functional understanding and interpretation of gene function. In this paper we present the results of an iterative algorithm for automatically generating gene regulatory networks from raw data. The algorithm is based on an epistemics approach of conjecture (hypothesis formation) and refutation (hypothesis testing). These operations are performed on a matrix representation of the gene network. Our approach also provides a way of incorporating external biological knowledge into the model. This is done by pre-assigning portions of the matrix--which represent previously known background knowledge. This background knowledge helps make the results closer to a human's rendition of such networks. We illustrate our approach by having the computer replicate a gene regulatory network generated by human scientists at an academic lab.
Kightley
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Genstruct Inc., 125 Cambridgepark Drive, Cambridge, MA 01702, USA.
Chandra
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Elliston
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D000465:Algorithms; D000818:Animals; D019295:Computational Biology; D003627:Data Interpretation, Statistical; D005786:Gene Expression Regulation; D008957:Models, Genetic; D012617:Sea Urchins / Q000196:embryology / Q000235:genetics