In this paper, we define a robustness measure for gene regulation networks, which allows to quantify how well a given model structure can reproduce a desired steady-state pattern in the absence of detailed knowledge about the kinetic mechanisms and parameters. To develop this measure, a modeling framework is introduced, which is able to represent the qualitative knowledge typically available for gene regulation networks. With this framework, the robustness measure as well as tools for its efficient computation are developed. The benefit of our method is twofold: On the one hand, it allows to compare the robustness properties of different model structures and thus may help modelers to decide which model is biologically more plausible. On the other hand, the most fragile interconnections within a network can be detected. To demonstrate its use, the new method is applied to various models of a gene regulation network, which is responsible for the maintenance of the mid-hindbrain boundary. We find that for this example system, weaker connected networks are more robust.     «

In this paper, we define a robustness measure for gene regulation networks, which allows to quantify how well a given model structure can reproduce a desired steady-state pattern in the absence of detailed knowledge about the kinetic mechanisms and parameters. To develop this measure, a modeling framework is introduced, which is able to represent the qualitative knowledge typically available for gene regulation networks. With this framework, the robustness measure as well as tools for its effici...     »