Stochastic dynamics in olfactory signal transduction and development

The purpose of the senses of animals (and humans) is to translate information available in the external environment into internal information that can be processed by the brain. In the case of the olfactory sense – the sense of smell – this is information about the type and concentration of odourants. In the last 15 years major progress has been made in the experimental understanding of the first two stages of the olfactory sense: the signal transduction inside the cilia of the olfactory receptor neurons and the first ’relay station’ in the brain, the olfactory bulb, as well as the connection between these two. Theoretical studies that classify the experimentally achieved knowledge or help in testing different biological hypotheses are only starting to be developed. The present work aims to contribute to the theoretical understanding of the first two stages of the olfactory sense. The first processing of the olfactory information, the olfactory signal transduction, is accomplished by a complex chemical network in the sensory cells with the task of coding the available information reliably over a wide range of stimulus strength. In the present work, methods from nonlinear dynamics combined with network theory (namely stoichiometric network analysis) are used to identify a specific negative feedback mechanism that accounts for a number of recently measured experimental results, e.g. oscillations in calcium concentration or the adaptation of the cell towards strong stimuli. This feedback is an experimentally well-established inhibition of cationic channels by the calcium-loaded form of the protein calmodulin. The results of the set of coupled nonlinear deterministic differential equations describing these dynamics agree quantitatively with experimental data. A bifurcation analysis of the system considered shows the robustness of the oscillatory solution against changes in parameters used. It also gives predictions that could serve as an experimental test of the proposed mechanism. Further abstraction and simplification of this specific signal transduction unit leads to a stochastic two-level system with negative feedback, that can not only be found in signalling systems but also in other branches of cell biology, e.g. regulated enzyme activity or in transcription dynamics. Whereas the description outlined above is fully deterministic, here the model system is intrinsically noisy. The influence of the feedback on the intrinsic noise as well as on the signalling properties of the module are analysed in detail by computing mean values, correlation and response functions of the two dynamical system variables using different analytical approaches. Common to all of them is that the intrinsic noise of the system is calculated from its dynamics rather than being introduced by hand. A master equation is used to get generally valid expressions for the mean values. Correlation and response functions for weak feedback are calculated within a path-integral description, and an easier self-consistent method with restricted validity is developed for future extensions of the module such as, e.g., the inclusion of diffusion. The results of the analytical methods are compared to each other and to the results of extended numerical simulations. The considered quantities allow for statements regarding the quality of the signal transduction properties of this module and the positive and negative effects of feedback on it. Going one step up in the information processing in the olfactory sense, another system is found that shows interesting dynamics during development and is influenced by stochastic effects: the formation of the neural map on the surface of the olfactory bulb – stage two in the olfactory system. The dynamics of this very complex biological pattern formation process is studied mostly numerically focusing on three different aspects of axonal growth. Possible chemical guidance cues and the reaction of axonal growth cones to them are described using different levels of detail. There is strong experimental evidence for interactions among growing axons which is implemented in different ways into models. Finally, axon turnover is considered and used in the most promising simulation approach, where many axons grow as interacting directed random walkers. For each of these aspects, qualitative features of respective experiments are reproduced.