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. 2013 Mar 14;9(3):e1002922. doi: 10.1371/journal.pcbi.1002922

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© 2013 Granot-Atedgi et al

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.

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a) Every response pattern from data is assigned to its corresponding meta-stable pattern by descending on the energy landscape defined by the S2 model of Eq (4) until the local minimum is reached (see text). Across all test repeats and at each point in time (horizontal axis), we find the metastable states that are visited more than 30 times, plot their energy (vertical axis), and the number of repeats on which that metastable state is visited (shade of red). b) Inset: for (blue rectangle in a), we plot the frequency of visit to each metastable state (dots) in the training set (horizontal) against the frequency in the test set (vertical). Main panel: the same analysis across all time bins (different colors) superposed, dashed line is equality.

Inline graphic — a) Every response pattern from data is assigned to its corresponding meta-stable pattern by descending on the energy landscape defined by the S2 model of Eq (4) until the local minimum is reached (see text). Across all test repeats and at each point in time (horizontal axis), we find the metastable states that are visited more than 30 times, plot their energy (vertical axis), and the number of repeats on which that metastable state is visited (shade of red). b) Inset: for (blue rectangle in a), we plot the frequency of visit to each metastable state (dots) in the training set (horizontal) against the frequency in the test set (vertical). Main panel: the same analysis across all time bins (different colors) superposed, dashed line is equality.