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  5. Reconstructing static and dynamic models of signaling pathways using Modular Response Analysis
 
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Reconstructing static and dynamic models of signaling pathways using Modular Response Analysis

Author(s)
Santra, Tapesh  
Rukhlenko, Oleksii S.  
Zhernovkov, Vadim  
Kholodenko, Boris N.  
Uri
http://hdl.handle.net/10197/10854
Date Issued
2018-06-01
Date Available
2019-07-08T09:06:36Z
Abstract
In this review we discuss the origination and evolution of Modular Response Analysis (MRA), which is a physics-based method for reconstructing quantitative topological models of biochemical pathways. We first focus on the core theory of MRA, demonstrating how both the direction and the strength of local, causal connections between network modules can be precisely inferred from the global responses of the entire network to a sufficient number of perturbations, under certain conditions. Subsequently, we analyze statistical reformulations of MRA and show how MRA is used to build and calibrate mechanistic models of biological networks. We further discuss what sets MRA apart from other network reconstruction methods and outline future directions for MRA-based methods of network reconstruction.
Sponsorship
European Commission Horizon 2020
Irish Cancer Society
Type of Material
Journal Article
Publisher
Elsevier BV
Journal
Current Opinion in Systems Biology
Volume
9
Issue
Nat Biotechnol 33 2015
Start Page
11
End Page
21
Copyright (Published Version)
2018 Elsevier
Subjects

Modular Response Anal...

Biochemical pathways

Network modules

Biological networks

Network reconstructio...

MRA constructed netwo...

DOI
10.1016/j.coisb.2018.02.003
Language
English
Status of Item
Peer reviewed
ISSN
2452-3100
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
File(s)
No Thumbnail Available
Name

Santra_et_al.docx

Size

591.73 KB

Format

Unknown

Checksum (MD5)

a1c2ade355788fe4569a760d2cc3a82f

Owning collection
Medicine Research Collection
Mapped collections
Conway Institute Research Collection•
SBI Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
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