Predecessor unsatisfied attractor algorithm of period control Boolean network

Abstract

The invention discloses a predecessor unsatisfied attractor algorithm of a period control Boolean network. The predecessor unsatisfied attractor algorithm comprises the steps of calculating an obtained attractor state set through sampling, and then determining whether partial state of a Boolean control network is unstable according to the attractor state set; on the condition whether the partial state is stable cannot be determined through the attractor state set, determining whether the partial state in unstable through predecessor iterative calculation, thereby establishing a partial state database; according to a partial state database, constructing a relatively small scale network state set; and then performing transversal on the Boolean control network for obtaining all attractors. Through the attractor calculating method, the calculation amount in attractor calculation is greatly reduced, the algorithm can quickly process attractor calculation of a relatively large-scale large-average-period control Boolean control network. The algorithm improves further understanding of internal essential characteristic and property of the Boolean control network, and generates tool examination for designing a controller of the Boolean control network.

Description

technical field [0001] The invention relates to the field of computer technology and gene regulation networks, in particular to an attractor calculation method for periodic control Boolean networks. Background technique [0002] The gene regulatory network is a complex regulatory network composed of various genes, proteins, and small molecules interacting with each other. Biology has also confirmed the existence of on-off phenomena in gene regulation. These features make Boolean control networks an important and effective tool for systems biology research. The Boolean control network is a Boolean network with a controller. In the Boolean network, each node has only two states "0" and "1", and each node can only be in one of these two states at a certain moment. A sort of. For a synchronous Boolean network, the state of each node at the next moment is determined by the state of the network node at the current moment. [0003] Boolean control network is an important and eff...

AI Technical Summary

Problems solved by technology

However, computing network attractors using the half-tensor product method involves a 2 64 ×2 64 structure matrix, even if the genes are compressed, it still requires at least 2 64 = 17,179,869,184Gb storage space, modern computers cannot bear this kind of storage and calculation

In addition, polynomial optimization methods and integer programming methods are also used in the research of Boolean network control problems; but these methods also face a problem, that is, it is difficult to deal with the control problems of larger-scale Boolean networks

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