Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A Kauffman network is an abstract model of gene regulatory networks. Each gene is represented by a vertex. An edge from one vertex to another implies that the former gene regulates the latter. Statistical features of Kauffman networks match the characteristics of living cells. The number of cycles in the network's state space, called attractors, corresponds to the number of different cell types. The attractor's length corresponds to the cell cycle time. The sensitivity of attractors to different kinds of disturbances, modeled by changing a network connection, the state of a vertex, or the associated function, reflects the stability of the cell to damage, mutations and virus attacks. In order to evaluate attractors, their number and lengths have to be computed. This problem is the major open problem related to Kauffman networks. Available algorithms can only handle networks with less than a hundred vertices. The number of genes in a cell is often larger. ,  present a set of efficient algorithms for computing attractors in large Kauffman networks. The resulting software package is hoped to be of assistance in understanding the principles of gene interactions and discovering a computing scheme operating on these principles.
['Elena Dubrova', 'Maxim Teslenko', 'Andrés Martinelli']
I am writing a research paper on a idea Kauffman networks: analysis and applications
[{"from": "human", "value": "I am writing a research paper on a idea Kauffman networks: analysis and applications"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A Kauffman network is an abstract model of gene regulatory networks. Each gene is represented by a vertex. An edge from one vertex to another implies that the former gene regulates the latter. Statistical features of Kauffman networks match the characteristics of living cells. The number of cycles in the network's state space, called attractors, corresponds to the number of different cell types. The attractor's length corresponds to the cell cycle time. The sensitivity of attractors to different kinds of disturbances, modeled by changing a network connection, the state of a vertex, or the associated function, reflects the stability of the cell to damage, mutations and virus attacks. In order to evaluate attractors, their number and lengths have to be computed. This problem is the major open problem related to Kauffman networks. Available algorithms can only handle networks with less than a hundred vertices. The number of genes in a cell is often larger. ,  present a set of efficient algorithms for computing attractors in large Kauffman networks. The resulting software package is hoped to be of assistance in understanding the principles of gene interactions and discovering a computing scheme operating on these principles."}]