| Popis: |
In single-cell genomics, we can simultaneously assay hundreds of thousands of cells, their molecular contents, and how they respond to perturbation, from genetic knockouts to environmental changes. This thesis focuses on how to merge experimental and computational techniques to generate and analyze large-scale perturbation data for high-resolution systems biology. Beginning at the bench, we demonstrate how combining large-scale cell atlas surveys with multi-condition experimentation can illuminate the diversity of cell types across whole organisms and cellular strategies in response to environmental changes and perturbations. We then investigate the limitations of current practice in exploratory analysis, and strategies for determining preservation or distortion of biological insight by these data transformation and dimensionality reduction techniques. To address these limitations, we demonstrate how stochastic biophysical models can rewrite the way we interpret complex perturbation data, taking greater advantage of the diverse molecular measurements to develop biological hypotheses about DNA and RNA regulation in cellular function, development, and disease. |
