based largely on the effects on later dynamics as opposed to the initial activation, Lapatinib as is the case for all of the feedback parameters, then trying to modify these parameters would be much less effective. Thus, results from our dynamic sensitivity analysis can be of particular importance when trying to identify how to modify a model to correct discre pancies between model simulations and data, as it pro vides valuable information. It is important to note that our particular model, which is developed to reproduce population average measurements of IKK and NF B activity in microglia, is not unique and other models are capable of produ cing the same dynamics. It may be desirable in different contexts to extend or otherwise modify this model to explore aspects not considered here.

For instance, delayed negative feedback from the I B�� isoform may also contribute substantially to later phase NF B sig naling dynamics, but is omitted from the present model. It may be useful to extend the model to include interactions from I B�� in future studies. Using data from bulk population level averages Inhibitors,Modulators,Libraries also masks asyn chronous NF B oscillations at the single cell level. Thus a different approach, such as simulat ing the deterministic model with random parameter dis tributions or using stochastic deterministic hybrid models, may be more appropriate when specifi cally considering individual cell responses.

The analysis from this model for microglial NF B acti vation clearly portrays the canonical NF B response on one hand as very robust, cells are able to parse extracellu lar signals into transient IKK activation to produce Inhibitors,Modulators,Libraries a quick and dynamic rise in NF B activity, even in the face of uncertainty in many of the reaction rates in both the upstream and downstream pathways. This finding is consistent Inhibitors,Modulators,Libraries with sensitivity analysis of related models, in which the response was found to be largely insensitive to the majority of the rate parameters. On the other hand, this analysis reveals the highly responsive nature of the network, evident from the high sensitivity and low robustness of the NF B response to changes in the feed back parameters. We note that although pre vious analyses have identified Inhibitors,Modulators,Libraries the sensitivity of the NF B response to many of the same parameters identified here, none appear to have interpreted the importance of such parameters in the context of feedback control systems.

The behavior of the NF B regulatory network is not unlike that commonly encountered in feedback systems in the engineering world. Consider, for instance, the operation of an amplifier designed to amplify signals in an electronic Batimastat system. High gain amplifiers with nega tive feedback amplify signals robustly even when sub jected to relatively large changes in feedforward system parameters. But the response is sensitive to feedback parameters, which both permits the system to be finely tuned by selecting proper feedback components, and makes Vandetanib molecular weight the system vulnerable to failure if the feedback par