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Understanding complexity in the HIF signaling pathway using systems biology and mathematical modeling
Alternative Title
Modeling the HIF pathway
Author(s)
Date Issued
2016-04
Date Available
2017-12-19T17:20:01Z
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.
Sponsorship
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
Language
English
Status of Item
Peer reviewed
This item is made available under a Creative Commons License
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Understanding Complexity in the HIF signaling pathway using systems biology and mathematical modeling.pdf
Size
473.34 KB
Format
Adobe PDF
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