Responding to change is a fundamental property of life, making time-series data invaluable in biology. For microbes, plate readers are a popular, convenient means to measure growth and also gene expression using fluorescent reporters. Nevertheless, the difficulties of analysing the resulting data can be a bottleneck, particularly when combining measurements from different wells and plates. Here we present omniplate, a Python module that corrects and normalises plate-reader data, estimates growth rates and fluorescence per cell as function of time, calculates errors, exports in different formats, and enables meta-analysis of multiple plates. The software corrects for autofluorescence, the optical density's non-linear dependence on the number of cells, and the effects of the media. We use omniplate to measure the Monod relationship for growth of budding yeast in raffinose, showing that raffinose is a convenient carbon source for controlling growth rates. Using fluorescent tagging, we study yeast's glucose transport. Our results are consistent with the regulation of the hexose transporter (HXT) genes being approximately bipartite: the medium and high affinity transporters are regulated by both the high affinity glucose sensor Snf3 and the kinase complex SNF1 via the repressors Mth1, Mig1, and Mig2; the low affinity transporters are predominately regulated by the low affinity sensor Rgt2 via the co-repressor Std1. We thus demonstrate that omniplate is a powerful tool for exploiting the advantages offered by time-series data in revealing biological regulation.Time-series data for strains of budding yeast growing in Tecan microplate readers in low fluorescence SC media with either glucose or raffinose as a carbon source. Pre-growth was in 2% pyruvate. Measurements are taken every five minutes and experiments run for approximately 20 hours. More information is in README.txt.