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  Transforming Boolean models to continuous models: methodology and application to T-cell receptor signaling

Wittmann, D. M., Krumsiek, J., Saez, J., Lauffenburger, D. A., Klamt, S., & Theis, F. (2009). Transforming Boolean models to continuous models: methodology and application to T-cell receptor signaling. BMC Systems Biology, 3: 98. doi:10.1186/1752-0509-3-98.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-93E1-1 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-002C-8B6A-4
Genre: Journal Article

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Wittmann, D. M.1, 2, Author
Krumsiek, J.1, Author
Saez, J.3, 4, Author
Lauffenburger, D. A.3, Author
Klamt, S.5, Author              
Theis, Fabian1, 2, 6, Author              
Affiliations:
1Institute for Bioinformatics and Systems Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany , ou_persistent22              
2Technische Universität München, Garching, ou_persistent22              
3M.I.T., Cambridge MA, USA , ou_persistent22              
4Harvard Medical School, Boston MA, USA , ou_persistent22              
5Analysis and Redesign of Biological Networks, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society, ou_1738139              
6Department of Nonlinear Dynamics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society, ou_2063286              

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 Abstract: Background The understanding of regulatory and signaling networks has long been a core objective in Systems Biology. Knowledge about these networks is mainly of qualitative nature, which allows the construction of Boolean models, where the state of a component is either 'off' or 'on'. While often able to capture the essential behavior of a network, these models can never reproduce detailed time courses of concentration levels. Nowadays however, experiments yield more and more quantitative data. An obvious question therefore is how qualitative models can be used to explain and predict the outcome of these experiments. Results In this contribution we present a canonical way of transforming Boolean into continuous models, where the use of multivariate polynomial interpolation allows transformation of logic operations into a system of ordinary differential equations (ODE). The method is standardized and can readily be applied to large networks. Other, more limited approaches to this task are briefly reviewed and compared. Moreover, we discuss and generalize existing theoretical results on the relation between Boolean and continuous models. As a test case a logical model is transformed into an extensive continuous ODE model describing the activation of T-cells. We discuss how parameters for this model can be determined such that quantitative experimental results are explained and predicted, including time-courses for multiple ligand concentrations and binding affinities of different ligands. This shows that from the continuous model we may obtain biological insights not evident from the discrete one. Conclusion The presented approach will facilitate the interaction between modeling and experiments. Moreover, it provides a straightforward way to apply quantitative analysis methods to qualitatively described systems. © 2009 Wittmann et al; licensee BioMed Central Ltd. 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. [accessed February 5th, 2010]

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Language(s): eng - English
 Dates: 2009
 Publication Status: Published in print
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 Rev. Type: Peer
 Identifiers: eDoc: 439486
Other: 16/10
DOI: 10.1186/1752-0509-3-98
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Title: BMC Systems Biology
Source Genre: Journal
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Pages: - Volume / Issue: 3 Sequence Number: 98 Start / End Page: - Identifier: -