Random Boolean network models and the yeast transcriptional network
我們最近通過用一個簡化的布爾網(wǎng)絡(luò)模型來分析酵母的轉(zhuǎn)錄網(wǎng)絡(luò),。期望在生成的布爾網(wǎng)絡(luò)的穩(wěn)定條件下，找出結(jié)構(gòu)中可行的規(guī)則,。我們發(fā)現(xiàn)在所有生成的模型中,，那些canalyzing布爾規(guī)則的模型特別穩(wěn)定,，而那些隨機布爾規(guī)則的模型只在邊緣穩(wěn)定,。另外,，生成的網(wǎng)絡(luò)的的實質(zhì)性部分是不變的,，也就是說不論他們的初始狀態(tài)怎樣,，最終它們總是到達同樣的狀態(tài),。所以，我們總的方法提示酵母的轉(zhuǎn)錄網(wǎng)絡(luò)顯示了高度的動力學規(guī)則性,。
Stuart Kauffman*, Carsten Peterson唶, Bjo?rn Samuelsson? and Carl Troein?
*Department of Cell Biology and Physiology, University of New Mexico Health Sciences Center, Albuquerque, NM 87131; and 咰omplex Systems Division,
Department of Theoretical Physics, Lund University, So?lvegatan 14A, S-223 62 Lund, Sweden
Communicated by Philip W. Anderson, Princeton University, Princeton, NJ, October 6, 2003 (received for review June 30, 2003)
The recently measured yeast transcriptional network is analyzed in terms of simplified Boolean network models, with the aim of determining feasible rule structures, given the requirement of stable solutions of the generated Boolean networks. We find that, for ensembles of generated models, those with canalyzing Boolean rules are remarkably stable, whereas those with random Boolean rules are only marginally stable. Furthermore, substantial parts of the generated networks are frozen, in the sense that they reach the same state, regardless of initial state. Thus, our ensemble approach suggests that the yeast network shows highly ordered dynamics.
