Understanding complexity in the HIF signaling pathway using systems biology and mathematical modeling
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|Title:||Understanding complexity in the HIF signaling pathway using systems biology and mathematical modeling||Authors:||Fábián, Zsolt
Taylor, Cormac T.
Nguyen, Lan K.
|Permanent link:||http://hdl.handle.net/10197/9126||Date:||Apr-2016||Abstract:||Hypoxia is a common micro-environmental stress which is experienced by cells during a range of physiologic and pathophysiologic processes. The identification of the hypoxia-inducible factor (HIF) as the master regulator of the transcriptional response to hypoxia transformed our understanding of the mechanism underpinning the hypoxic response at the molecular level and identified HIF as a potentially important new therapeutic target. It has recently become clear that multiple levels of regulatory control exert influence on the HIF pathway giving the response a complex and dynamic activity profile. These include positive and negative feedback loops within the HIF pathway as well as multiple levels of crosstalk with other signaling pathways. The emerging model reflects a multi-level regulatory network that affects multiple aspects of the physiologic response to hypoxia including proliferation, apoptosis, and differentiation. Understanding the interplay between the molecular mechanisms involved in the dynamic regulation of the HIF pathway at a systems level is critically important in defining new appropriate therapeutic targets for human diseases including ischemia, cancer, and chronic inflammation. Here, we review our current knowledge of the regulatory circuits which exert influence over the HIF response and give examples of in silico model-based predictions of the dynamic behaviour of this system.||Funding Details:||Science Foundation Ireland||Type of material:||Journal Article||Publisher:||Springer||Journal:||Journal of Molecular Medicine||Volume:||94||Issue:||4||Start page:||377||End page:||390||Copyright (published version):||2016 Springer-Verlag Berlin Heidelberg||Keywords:||Hypoxia; Inflammation; Mathematical model; Signal transduction||DOI:||10.1007/s00109-016-1383-6||Language:||en||Status of Item:||Peer reviewed|
|Appears in Collections:||Conway Institute Research Collection|
SBI Research Collection
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