Single-cell time-series analysis of metabolic rhythms in yeast

The yeast metabolic cycle (YMC) is a biological rhythm in budding yeast (Saccharomyces cerevisiae). It entails oscillations in the concentrations and redox states of intracellular metabolites, oscillations in transcript levels, temporal partitioning of biosynthesis, and, in chemostats, oscillations in oxygen consumption. Most studies on the YMC have been based on chemostat experiments, and it is unclear whether YMCs arise from interactions between cells or are generated independently by each cell. This thesis aims at characterising the YMC in single cells and its response to nutrient and genetic perturbations. Specifically, I use microfluidics to trap and separate yeast cells, then record the time-dependent intensity of flavin autofluorescence, which is a component of the YMC. Single-cell microfluidics produces a large amount of time series data. Noisy and short time series produced from biological experiments restrict the computational tools that are useful for analysis. I developed a method to filter time series, a machine learning model to classify whether time series are oscillatory, and an autocorrelation method to examine the periodicity of time series data. My experimental results show that yeast cells show oscillations in the fluorescence of flavins. Specifically, I show that in high glucose conditions, cells generate flavin oscillations asynchronously within a population, and these flavin oscillations couple with the cell division cycle. I show that cells can individually reset the phase of their flavin oscillations in response to abrupt nutrient changes, independently of the cell division cycle. I also show that deletion strains generate flavin oscillations that exhibit different behaviour from dissolved oxygen oscillations from chemostat conditions. Finally, I use flux balance analysis to address whether proteomic constraints in cellular metabolism mean that temporal partitioning of biosynthesis is advantageous for the yeast cell, and whether such partitioning explains the timing of the metabolic cycle. My results show that under proteomic constraints, it is advantageous for the cell to sequentially synthesise biomass components because doing so shortens the timescale of biomass synthesis. However, the degree of advantage of sequential over parallel biosynthesis is lower when both carbon and nitrogen sources are limiting. This thesis thus confirms autonomous generation of flavin oscillations, and suggests a model in which the YMC responds to nutrient conditions and subsequently entrains the cell division cycle. It also emphasises the possibility that subpopulations in the culture explain chemostat-based observations of the YMC. Furthermore, this thesis paves the way for using computational methods to analyse large datasets of oscillatory time series, which is useful for various fields of study beyond the YMC

Accuracy and data efficiency in deep learning models of protein expression

Synthetic biology often involves engineering microbial strains to express high-value proteins. Thanks to progress in rapid DNA synthesis and sequencing, deep learning has emerged as a promising approach to build sequence-to-expression models for strain optimization. But such models need large and costly training data that create steep entry barriers for many laboratories. Here we study the relation between accuracy and data efficiency in an atlas of machine learning models trained on datasets of varied size and sequence diversity. We show that deep learning can achieve good prediction accuracy with much smaller datasets than previously thought. We demonstrate that controlled sequence diversity leads to substantial gains in data efficiency and employed Explainable AI to show that convolutional neural networks can finely discriminate between input DNA sequences. Our results provide guidelines for designing genotype-phenotype screens that balance cost and quality of training data, thus helping promote the wider adoption of deep learning in the biotechnology sector