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Abstract: A biological regulatory network can be modeled as a discrete function thatcontains all available information on network component interactions. From thisfunction we can derive a graph representation of the network structure as wellas of the dynamics of the system. In this paper we introduce a method toidentify modules of the network that allow us to construct the behavior of thegiven function from the dynamics of the modules. Here, it proves useful todistinguish between dynamical and structural modules, and to define networkmodules combining aspects of both. As a key concept we establish the notion ofsymbolic steady state, which basically represents a set of states where thebehavior of the given function is in some sense predictable, and which givesrise to suitable network modules. We apply the method to a regulatory networkinvolved in T helper cell differentiation.



Author: Heike Siebert

Source: https://arxiv.org/







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