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Issue 11, 2012
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Logic-based models in systems biology: a predictive and parameter-free network analysis method

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Abstract

Highly complex molecular networks, which play fundamental roles in almost all cellular processes, are known to be dysregulated in a number of diseases, most notably in cancer. As a consequence, there is a critical need to develop practical methodologies for constructing and analysing molecular networks at a systems level. Mathematical models built with continuous differential equations are an ideal methodology because they can provide a detailed picture of a network's dynamics. To be predictive, however, differential equation models require that numerous parameters be known a priori and this information is almost never available. An alternative dynamical approach is the use of discrete logic-based models that can provide a good approximation of the qualitative behaviour of a biochemical system without the burden of a large parameter space. Despite their advantages, there remains significant resistance to the use of logic-based models in biology. Here, we address some common concerns and provide a brief tutorial on the use of logic-based models, which we motivate with biological examples.

Graphical abstract: Logic-based models in systems biology: a predictive and parameter-free network analysis method

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Publication details

The article was received on 13 Aug 2012, accepted on 27 Sep 2012 and first published on 03 Oct 2012


Article type: Critical Review
DOI: 10.1039/C2IB20193C
Citation: Integr. Biol., 2012,4, 1323-1337
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    Logic-based models in systems biology: a predictive and parameter-free network analysis method

    M. L. Wynn, N. Consul, S. D. Merajver and S. Schnell, Integr. Biol., 2012, 4, 1323
    DOI: 10.1039/C2IB20193C

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