Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A ll-known discrete approach to modeling biological regulatory networks is the logical framework developed by R. Thomas. The network structure is captured in an interaction graph, which, together with a set of Boolean parameters, gives rise to a state transition graph describing the dynamical behavior. Together with E. H. Snoussi, Thomas later extended the framework by including singular values representing the threshold values of interactions. A systematic approach was taken in [10] to link circuits in the interaction graph with character and number of attractors in the state transition graph by using the information inherent in singular steady states. ,  employ the concept of local interaction graphs to strengthen the results in [10]. Using the local interaction graph of a singular steady state,  are able to construct attractors of the regulatory network from attractors of certain subnetworks. As a comprehensive generalization of the framework introduced in [10],  drop constraints concerning the choice of parameter values to include so-called context sensitive networks.
['Heike Siebert']
I am writing a research paper on a idea Local Structure and Behavior of Boolean Bioregulatory Networks
[{"from": "human", "value": "I am writing a research paper on a idea Local Structure and Behavior of Boolean Bioregulatory Networks"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A ll-known discrete approach to modeling biological regulatory networks is the logical framework developed by R. Thomas. The network structure is captured in an interaction graph, which, together with a set of Boolean parameters, gives rise to a state transition graph describing the dynamical behavior. Together with E. H. Snoussi, Thomas later extended the framework by including singular values representing the threshold values of interactions. A systematic approach was taken in [10] to link circuits in the interaction graph with character and number of attractors in the state transition graph by using the information inherent in singular steady states. ,  employ the concept of local interaction graphs to strengthen the results in [10]. Using the local interaction graph of a singular steady state,  are able to construct attractors of the regulatory network from attractors of certain subnetworks. As a comprehensive generalization of the framework introduced in [10],  drop constraints concerning the choice of parameter values to include so-called context sensitive networks."}]