Relating attractors and singular steady states in the logical analysis of bioregulatory networks
AB'07 Proceedings of the 2nd international conference on Algebraic biology
Decision diagrams for the representation and analysis of logical models of genetic networks
CMSB'07 Proceedings of the 2007 international conference on Computational methods in systems biology
On differentiation and homeostatic behaviours of Boolean dynamical systems
Transactions on computational systems biology VIII
| Hi-index | 0.00 |
A well-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. In this paper, we employ the concept of local interaction graphs to strengthen the results in [10]. Using the local interaction graph of a singular steady state, we are able to construct attractors of the regulatory network from attractors of certain subnetworks. As a comprehensive generalization of the framework introduced in [10], we drop constraints concerning the choice of parameter values to include so-called context sensitive networks.