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The analysis is limited to two dimensions and assumes quasi-stationary distributions in the long time limit.
Therefore under this model, the long time limit is exact.
Previous studies on competition with a finite interaction range have shown that spatial aggregation is possible in the long time limit [ 37- 43].
In the long time limit, this should outweigh all time-linear dynamics that have been assumed in PPI network evolution models so far [ 36, 41- 45] (see, however, Discussion).
A possible way out was proposed by Ribeiro and Kauffman [31] who observed that there exist sets of attractors, which they called ergodic sets, which entrap the system in the long time limit, so the system continues to jump between attractors which belong to the set.
Then, the cumulant generating function in the long time limit is given by F ( χ ; t ) = λ 0 t, where λ0 denotes the minimum eigenvalue of W that develops adiabatically from 0 with χ.
Similar(45)
The thermal fluctuations are still oscillating functions even in the long-time limit.
In the long-time limit all systems obey xf∼t 0.49±0.02) and differ only in the timespan needed to enter this diffusion-controlled state.
As expected, similar results are obtained using the long-time limit distribution of ΔS (Table 2).
Once again, we can obtain a good approximation to this distribution using the long-time limit.
Although these results are formally exact, they can be further simplified in the long-time limit.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com