Search

Parkinsonian and essential tremor can often be effectively treated by deep brain stimulation. We propose a novel explanation for the mechanism by which this technique ameliorates tremor: a reduction of the delay in the relevant motor control loops via preferential antidromic blockade of slow axons. The antidromic blockade is preferential because the pulses more rapidly clear fast axons, and the distribution of axonal diameters, and therefore velocities, in the involved tracts, is sufficiently long-tailed to make this effect quite significant. The preferential blockade of slow axons, combined with gain adaptation, results in a reduction of the mean delay in the motor control loop, which serves to stabilize the feedback system, thus ameliorating tremor. This theory, without any tuning, accounts for several previously perplexing phenomena, and makes a variety of novel predictions.

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.

Probe photobleaching and a specimen’s sensitivity to phototoxicity severely limit the number of possible excitation cycles in time-lapse fluorescent microscopy experiments. Consequently, when a study of cellular processes requires measurements over hours or days, temporal resolution is limited, and spontaneous or rapid events may be missed, thus limiting conclusions about transduction events. We have developed ALISSA, a design framework and reference implementation for an automated live-cell imaging system for signal transduction analysis. It allows an adaptation of image modalities and laser resources tailored to the biological process, and thereby extends temporal resolution from minutes to seconds. The system employs online image analysis to detect cellular events that are then used to exercise microscope control. It consists of a reusable image analysis software for cell segmentation, tracking, and time series extraction, and a measurement-specific process control software that can be easily adapted to various biological settings. We have applied the ALISSA framework to the analysis of apoptosis as a demonstration case for slow onset and rapid execution signaling. The demonstration provides a clear proof-of-concept for ALISSA, and offers guidelines for its application in a broad spectrum of signal transduction studies.

The mathematical modelling of signal transduction pathways has become a valuable aid to understanding the complex interactions involved in intracellular signalling mechanisms. An important aspect of the mathematical modelling process is the selection of the model type and structure. Until recently, the convention has been to use a standard kinetic model, often with the Michaelis–Menten steady state assumption. However this model form, although valuable, is only one of a number of choices, and the aim of this article is to consider the mathematical structure and essential features of an alternative model form — the power-law model. Specifically, we analyse how power-law models can be applied to increase our understanding of signal transduction pathways when there may be limited prior information. We distinguish between two kinds of power law models: a) Detailed power-law models, as a tool for investigating pathways when the structure of protein–protein interactions is completely known, and; b) Simplified power-law models, for the analysis of systems with incomplete structural information or insufficient quantitative data for generating detailed models. If sufficient data of high quality are available, the advantage of detailed power-law models is that they are more realistic representations of non-homogenous or crowded cellular environments. The advantages of the simplified power-law model formulation are illustrated using some case studies in cell signalling. In particular, the investigation on the effects of signal inhibition and feedback loops and the validation of structural hypotheses are discussed.

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.