Documenting the extent of cellular diversity is a critical step in defining the functional organization of the nervous system. In this context, we sought to develop statistical methods capable of revealing underlying cellular diversity given incomplete data sampling - a common problem in biological systems, where complete descriptions of cellular characteristics are rarely available. We devised a sparse Bayesian framework that infers cell type diversity from partial or incomplete transcription factor expression data. This framework appropriately handles estimation uncertainty, can incorporate multiple cellular characteristics, and can be used to optimize experimental design. We applied this framework to characterize a cardinal inhibitory population in the spinal cord. Animals generate movement by engaging spinal circuits that direct precise sequences of muscle contraction, but the identity and organizational logic of local interneurons that lie at the core of these circuits remain unresolved. By using our Sparse Bayesian approach, we showed that V1 interneurons, a major inhibitory population that controls motor output, fractionate into diverse subsets on the basis of the expression of nineteen transcription factors. Transcriptionally defined subsets exhibit highly structured spatial distributions with mediolateral and dorsoventral positional biases. These distinctions in settling position are largely predictive of patterns of input from sensory and motor neurons, arguing that settling position is a determinant of inhibitory microcircuit organization. Finally, we extensively validated inferred cell types by direct experimental measurement and then, extend our Bayesian framework to full transcriptome technologies. Together, these findings provide insight into the diversity and organizational logic through which inhibitory microcircuits shape motor output.

Biomolecular machines are responsible for carrying out a host of essential cellular processes. In accordance to the wide range of functions they execute, the architectures of these also vary greatly. Yet, despite this diversity in both structure and function, they have some common characteristics. They are all large macromolecular complexes that enact multiple steps during the course of their functions. They are also ’Brownian’ in nature, i.e., they rectify the thermal motions of their surroundings into work. Yet how these machines can utilise their surrounding thermal energy in a directional manner, and do so in a cycle over and over again, is still not well understood. The work I present in this thesis spans the development, evaluation and use of biophysical, in particular single-molecule, tools in the study of the functional mechanisms of biomolecular machines. In Chapter 2, I describe a mathematical framework which utilises both the framework of Bayesian inference to relate any experimental data to an ideal template irrespective of the scale, background and noise in the data. This framework may be used for the analysis of data generated by multiple experimental techniques in an accurate, fast, and human-independent manner. One such application is described in Chapter 3, where this framework is used to evaluate the extent of spatial information present in experimental data generated using cryogenic electron microscopy (cryoEM). This application will not only aid the study of biomolecular structure using cryoEM by structural biologists, but also enable biophysicists and biochemists who use structural models to interpret and design their experiments to evaluate the cryoEM data they need to use for their investigations. In Chapter 4, I describe an investigation into the use of one class of analytical models, hidden Markov models (HMMs) to accurately extract kinetic information from single-molecule experimental data, such as the data generated by single-molecule fluorescence resonance energy transfer (smFRET) experiments. Finally in Chapter 5, I describe how single-molecule experiments have led to the discovery of a mechanism by which ligands can modulate and drive the conformational dynamics of the ribosome in a manner that facilitates ribosome-catalysed protein synthesis. This mechanism has implications to our understanding of the functional mechanisms of the ribosome in particular, and of biomolecular machines in general.