SynTReN 1.2 – Generator of Synthetic Gene Expression data for Design and Analysis of Structure Learning algorithms

SynTReN 1.2


Syntren  (Synthetic Transcriptional Regulatory Networks) is a network generator that creates synthetic transcriptional regulatory networks and produces simulated gene expression data that approximates experimental data. Network topologies are generated by selecting subnetworks from previously described regulatory networks. Interaction kinetics are modeled by equations based on Michaelis-Menten and Hill kinetics. Our results show that the statistical properties of these topologies more closely approximate those of genuine biological networks than do those of different types of random graph models. Several user-definable parameters adjust the complexity of the resulting data set with respect to the structure learning algorithms.



Kathleen Marchal 



  • Linux /  Windows / MacOsX
  • Java




BMC Bioinformatics. 2006 Jan 26;7:43.
SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms.
Van den Bulcke T, Van Leemput K, Naudts B, van Remortel P, Ma H, Verschoren A, De Moor B, Marchal K.

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