English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Modeling approaches for qualitative and semi-quantitative analysis of cellular signaling networks

Samaga, R., & Klamt, S. (2013). Modeling approaches for qualitative and semi-quantitative analysis of cellular signaling networks. Cell Communication and Signaling, 11(1), 43. doi:10.1186/1478-811X-11-43.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-FA19-F Version Permalink: http://hdl.handle.net/11858/00-001M-0000-002C-9054-D
Genre: Journal Article

Files

show Files
hide Files
:
1801317_samaga.pdf (Publisher version), 2MB
Name:
1801317_samaga.pdf
Description:
-
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Locators

show

Creators

show
hide
 Creators:
Samaga, Regina1, Author              
Klamt, Steffen1, Author              
Affiliations:
1Analysis and Redesign of Biological Networks, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society, ou_1738139              

Content

show
hide
Free keywords: -
 Abstract: A central goal of systems biology is the construction of predictive models of bio-molecular networks. Cellular networks of moderate size have been modeled successfully in a quantitative way based on differential equations. However, in large-scale networks, knowledge of mechanistic details and kinetic parameters is often too limited to allow for the set-up of predictive quantitative models. Here, we review methodologies for qualitative and semi-quantitative modeling of cellular signal transduction networks. In particular, we focus on three different but related formalisms facilitating modeling of signaling processes with different levels of detail: interaction graphs, logical/Boolean networks, and logic-based ordinary differential equations (ODEs). Albeit the simplest models possible, interaction graphs allow the identification of important network properties such as signaling paths, feedback loops, or global interdependencies. Logical or Boolean models can be derived from interaction graphs by constraining the logical combination of edges. Logical models can be used to study the basic input–output behavior of the system under investigation and to analyze its qualitative dynamic properties by discrete simulations. They also provide a suitable framework to identify proper intervention strategies enforcing or repressing certain behaviors. Finally, as a third formalism, Boolean networks can be transformed into logic-based ODEs enabling studies on essential quantitative and dynamic features of a signaling network, where time and states are continuous. We describe and illustrate key methods and applications of the different modeling formalisms and discuss their relationships. In particular, as one important aspect for model reuse, we will show how these three modeling approaches can be combined to a modeling pipeline (or model hierarchy) allowing one to start with the simplest representation of a signaling network (interaction graph), which can later be refined to logical and eventually to logic-based ODE models. Importantly, systems and network properties determined in the rougher representation are conserved during these transformations. © 2013 Samaga and Klamt; licensee BioMed Central Ltd. [accessed July 18th]

Details

show
hide
Language(s): eng - English
 Dates: 2013
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1186/1478-811X-11-43
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Cell Communication and Signaling
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 11 (1) Sequence Number: - Start / End Page: 43 Identifier: ISSN: 1478-811X
CoNE: /journals/resource/111069362535004