Modeling microRNA-Transcription Factor Networks in Cancer

  • Baltazar D. Aguda
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 774)


An increasing number of transcription factors (TFs) and microRNAs (miRNAs) is known to form feedback loops (FBLs) of interactions where a TF positively or negatively regulates the expression of a miRNA, and the miRNA suppresses the translation of the TF messenger RNA. FBLs are potential sources of instability in a gene regulatory network. Positive FBLs can give rise to switching behaviors while negative FBLs can generate periodic oscillations. This chapter presents documented examples of FBLs and their relevance to stem cell renewal and differentiation in gliomas. Feed-forward loops (FFLs) are only discussed briefly because they do not affect network stability unless they are members of cycles. A primer on qualitative network stability analysis is given and then used to demonstrate the network destabilizing role of FBLs. Steps in model formulation and computer simulations are illustrated using the miR-17-92/Myc/E2F network as an example. This example possesses both negative and positive FBLs.


Mathematical modeling Feedback loops Feedforward loops miR-17-92 E2F Myc p53 Cancer zone Qualitative network 



cancer zone


differentiation transcription factor module


feed-forward loop


feedback loop


apoptosis factors


cell cycle factors


qualitative network


stem cell transcription factor module


transcription factor


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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Neuro-Oncology Branch, Center for Cancer ResearchNational Cancer Institute, National Institutes of HealthBethesdaUSA

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