Boolean Network Dynamic Behavior Research

Systems biology is a new subject after the genomics, protein study,representing the21st century the future of biology. With various systemsmodeling technical means development, especially complex systemmodeling method has been established which shows that scholars havegradually ability to create molecular control network closely related todisease. Thus, finally we can explain complex disease causes andtreatments, and reduce the difficulties of information analysis about thecomplex disease, and reduce the sample size limit of the actualobservation to provide the theory basis. Gene regulation network ispotential benefits in scientific and practical, so it has attracted a lot ofattention to countries and businesses. Biology is occurring frommolecular level to the system level of change. Believe that in the nearfuture, gene regulation network, as the main content of systems biologyresearch, will greatly promote medicine and biology research direction of the research results, becoming the key drivers of the development ofmedicine.In this paper, the key is relationship between the topology of thenetwork and Boolean function classes and long-range correlation. Wefound the sensitivity and Boolean network connectivity have influence onBoolean network long-rang correlation. First of all, construct Booleannetwork model, and add the noise to network, and synchronous recordnetwork evolution. Then, the distance from different state to its attractoras the output signal of quantitative network. Finally, the DFA method isused to calculate the correlation structure of networks. After the dataanalysis, we think that nearly half the simulation Boolean networksappear1/f fluctuation, along with the change of the noise, and thefluctuating range is different, which is related to and the Boolean functionclasses constructed the networks, because of channelizing function andPost class function with chemotaxis and closure respectively. Thecomposition of the Boolean network long-rang correlation also reflectsthe function nature itself, and the1/f fluctuation range are wider thannetworks structured by random functions. In addition, according toBoolean rule, we put eight kinds of actual network into Boolean networkmodel, and use the same DFA method to calculate the long-rangecorrelation. We found that all networks all can produce1/f fluctuation.Therefore, we guess that the actual biology network which is same as other network may be in1/f fluctuation, which will play an important rolein promoting the study of network dynamic.