The Dry Lab Stuff

In a growth curve of a bacteria, we will have the optical density measurements against time. For each time point, we will have a few technical replicates. Let’s take the following data which has time, and replicates A, B, and C. We have saved this table in a excel file. The file is named as growth_curve.xlsx and is stored in a folder/directory called datasets , relative to my current working directory. First thing we would do is load the tidyverse library, which will have the ggplot2 library we will use for plotting the growth curve. library(tidyverse) We will first read the data from excel into tibble and see how it looks like. data <- readxl::read_excel('datasets/growth_curve.xlsx') data Then, we will see the whole code to process and plot the data and later explain it step by step. # calculate mean and standard deviations data <- data %>% rowwise() %>% mutate( means = mean(c(A, B, C)), stdev = sd(c(A, B, C)) ) # plot the means and standar...

Metabolic steady state is condition of cell in which concentrations of the metabolites inside the cell do not change over time. In cells that are growing exponentially, such as microbes in their log phase of growth, the cells are in metabolic pseudo-steady state as the concentration of metabolites inside the cells in bulk, remain constant on average. Although it might be difficult for you to accept this but we can actually do experiments to study metabolism assuming this metabolic steady state and generate data that would be observed only when this condition was true.

  Data distribution plots help visualize how quantitative data points are spread over the range of their values. Distribution of quantitative data can be shown in various ways such as box-plots, violin-plots, histograms, and scatter-plot with artificially introduced deviations to depicts density of the data points. However, I think the relatively simple looking histogram is the best way we can visualize the data distribution, because it visualizes exactly how many data points are present in a given range of numbers. Figure: Data distribution of MSRP of a category of car. (Data taken from Kaggle)