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Deep brain stimulation (DBS) has been used to ameliorate essential and Parkinsonian tremor, however the detailed mechanism by which tremor reduction is achieved remains unclear. We hypothesize that DBS works by reducing time delays in the feedback paths of the motor control loops. In particular, we suggest that antidromic activation of axonal pathways induced by stimulation will preferentially block axons with longer propagation times, reducing time delays in neuronal motor circuits in a stabilising manner. We demonstrate the plausibility of this hypothesis using two simple computational models which account for a variety of experimental results, and allow us to makes a number of testable predictions.

In this paper we consider the question of parameter identifiability for biochemical reaction networks, as typically encountered in systems biology. Specifically, we are interested in deriving conditions on the biochemical reaction network and on the measured outputs that guarantee identifiability of the parameters. Taking the specific system structure of biochemical reaction networks into account, we derive sufficient conditions for local parameter identifiability based on a suitable system expansion which does not anymore directly depend on the parameters. Rather, as shown, the problem of identifiability can be recast as the question of observability of the (parameter free) expanded system. The conditions derived are exemplified considering a simple example.