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Mathematical models of signal transduction pathways are characterized by a large number of proteins and uncertain parameters.
Incorporation of experimental microscope images within full 3-dimensional spatial models of signal transduction networks is a key feature of the Virtual Cell.
The gain value is typically obtained by measurements and/or prediction models of signal propagation.
Another possibility is to manipulate lower-level, sample-based operators, for instance, by using data flow models of signal processing functions [97].
Models of signal transduction networks often exhibit complex relationships between model performance and parameter values [38].
Mathematical models of signal transduction rarely consider the contributions of spatial organization due to high computational costs.
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Reconstructing Boolean Models of Signaling.
This work demonstrates the capability of automated analysis of dynamic models of signaling networks to discover novel phenotypes and predictively identify specific manipulations that can reach them.
Broad strategies to measure quantitative, dynamic parameters in signaling networks, together with perturbation approaches, are needed to develop comprehensive models of signaling networks*.
Knepper's lab builds models of signaling networks by identifying specific protein kinases and phosphatases.
http://code.google.com/p/netflux/ CellNOpt Free software for creating logic-based models of signaling networks.
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