Number of attractors in the critical Kauffman model is exponential
The Kauffman model is the archetypal model of genetic computation. It highlights the importance of criticality, at which many biological systems seem poised. In a series of advances, researchers have honed in on how the number of attractors in the critical regime grows with network size. But a definitive answer has proved elusive. We prove that, for the critical Kauffman model with connectivity one, the number of attractors grows at least, and at most, as $(2/\!\sqrt{e})^N$. This is the first proof that the number of attractors in a critical Kauffman model grows exponentially.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Insights from number theory into the critical Kauffman model with connectivity one
The Kauffman model of genetic computation highlights the importance of criticality at the border of order and chaos. The model with connectivity one is of special interest because it is exactly solvable. But our understa…
An Algorithm to Effect Prompt Termination of Myopic Local Search on Kauffman-s NK Landscape
In Kauffman-s NK model, myopic local search involves flipping one randomly-chosen bit of an N-bit decision string in every time step and accepting the new configuration if that has higher fitness. One issue is that, this…
Kauffman's adjacent possible in word order evolution
Word order evolution has been hypothesized to be constrained by a word order permutation ring: transitions involving orders that are closer in the permutation ring are more likely. The hypothesis can be seen as a particu…
Model SelectionIdentifying the Attractors of Gene Regulatory Networks from Expression Data under Uncertainty: An Interpretable Approach
In systems biology, attractor landscape analysis of gene regulatory networks is recognized as a powerful computational tool for studying various cellular states from proliferation and differentiation to senescence and ap…
Encoder-Decoder Based Attractors for End-to-End Neural Diarization
This paper investigates an end-to-end neural diarization (EEND) method for an unknown number of speakers. In contrast to the conventional cascaded approach to speaker diarization, EEND methods are better in terms of spea…
Decoderspeaker-diarizationSpeaker Diarization