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Cryo-electron tomography (CET) is a unique technique to visualize biological objects under near-to-native conditions at near-atomic resolution. CET provides three-dimensional (3D) snapshots of the cellular proteome, in which the spatial relations between macromolecular complexes in their near native cellular context can be explored. Due to the limitation of the electron dose applicable on biological samples, the achievable resolution of a tomogram is restricted to a few nanometers, higher resolution can be achieved by averaging of structures occurring in multiples. For this purpose, computational techniques such as template matching, sub-tomogram averaging and classification are essential for a meaningful processing of CET data.
This thesis introduces the techniques of template matching and sub-tomogram averaging and their applications on real biological data sets. Subsequently, the problem of reference bias, which restricts the applicability of those techniques, is addressed. Two methods that estimate the reference bias in Fourier and real space are demonstrated. The real space method, which we have named the “M-free” score, provides a reliable estimation of the reference bias, which gives access to the reliability of the template matching or sub-tomogram averaging process. Thus, the “M-free” score makes those approaches more applicable to structural biology. Furthermore, a classification algorithm based on Neural Networks (NN) called “KerDenSOM3D” is introduced, which is implemented in 3D and compensates for the missing-wedge. This approach helps extracting different structural states of macromolecular complexes or increasing the class purity of data sets by eliminating outliers. A comprehensive comparison with other classification methods shows superior performance of KerDenSOM3D.
The nature of spontaneous brain activity during wakefulness and sleep: a complex systems approach
(2014)
In this thesis we study the organization of spontaneous brain activity during wakefulness and all stages of human non-rapid eye movement sleep using an approach based on developments and tools from the theory of complex systems. After a brief introduction to sleep physiology and different theoretical models of consciousness, we study how the organization of cortical and sub-cortical interactions is modified during the sleep cycle. Our results, obtained by modeling global brain activity as a complex functional interaction network, show that the capacity of the human brain to integrate different segregated functional modules is diminished during deep sleep, in line with an informationintegration account of consciousness. We then show that integration is impaired not only across space but also in the temporal domain, by assesing the emergence of long-range temporal correlations in brain activity and how they are modified during sleep. We propose an encompassing explanation for this observation, namely, that the brain operatsat different dynamical regimes during different states of consciousness. Finally, we gather massive amounts of data from different collaborative projects and apply machine learning techniques to reveal that the \resting state" cannot be considered as a pure brain state and is in fact a mixture containing different levels of conscious awareness. This last result has deep implications for future attempts to develop a discovery science of brain function both in health and disease.