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The current version of ChemChains contains the 'Patterns' extension used in our recent study [ 14].
The available Patterns extension categorizes each simulation and a specified group of output nodes into discrete groups, representing global responses (patterns) based on their activity level.
Figure 5 illustrates how the output of the Patterns extension is created using partial data generated from a ChemChains experiment on a large-scale model used in our recent paper [ 14].
As an example of how ChemChains, in combination with the Patterns extension, can be used for the above mentioned mutation studies is illustrated on the signal transduction model (mentioned above) in Table 6.
Table 5 shows output produced by the Patterns extension after a sample simulation experiment performed on a Boolean model representing a signal transduction network in a generic fibroblast cell.
The patterns extension allows users to view the system's dynamics in a more organized and intuitive manner by classifying the activity levels of user-defined nodes into groups based on a pre-defined activity level range, called patterns.
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Pattern extension is analyzed as a phonologically-neutral process driven by analogy.
A random pattern extension to the stochastic network pore model has been achieved by random relocation of the regularly spaced nodes of a square network.
The pattern extension approach suggests the most likely pattern for a given sequence, while ignoring less probable extensions.
SeAMotE is based on the generation of a pool of nucleotide seeds followed by "zero or one occurrence per sequence" (ZOOPs) model testing [ 17] coupled with pattern extension and refinement [ 8].
(3) Since the experiment data about hybrid lineages has not been taken into account as we have pointed out, we interpret these uninterpretable lineages to three fate pattern extensions as listed in the column of STA which includes more extensive possible fate patterns than ST.
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