Question 1
This will be the video. No need to write anything about it here, except to specify the name of the file, which should ideally be …
yourname-431lab01.mp4
Question 2
library(patchwork); library(tidyverse)
plotA <- ggplot(DNase, aes(x = conc, y = density)) + geom_point() + geom_smooth(method = “loess”, formula = y ~ x) + theme_bw() + labs(title = “Plot A”)
plotB <- ggplot(DNase, aes(x = factor(conc), y = density)) + geom_boxplot() + theme_bw() + labs(title = “Plot B”)
plotA / plotB + plot_annotation( title = “Question 2. Association of density and conc in the DNase data”)
Plot A shows each observation as a scatter point with a fitted smooth curve, revealing a nonlinear, saturating relationship between protein concentration and ELISA florescent density. At low concentrations, density and proetin concentation has a linear relationship, while at higher concentrations the increase slows, consistent with enzyme kinetics where the reaction approaches a maximum rate. Plot B using boxplots shows the relationship between florescent density and factor of concentration, which not show different conc as group instead of numerical value.So from plot B, it didn’t show the numerical relationship betwwen density and conc. # Question 3
The PPDAC cycle helps me to think about my research on developing an engineered probiotic that produces itaconic acid for treating Crohn’s disease. The “Problem” is that current therapies often fail or cause side effects, so there is a need for safer gut-focused treatments. In the “Plan” stage, I design experiments to test whether engineered E. coli can produce ITA at stable levels and improve gut immune balance. The “Data” come from growth assays, metabolite measurements, and animal models. Through “Analysis,” I look for patterns: does ITA production correlate with reduced inflammation markers? Finally, the “Conclusion” ties findings back to the central question of whether this strategy is viable.
Question 4
Problem: Convert optical density to DNA concentration reliably by establishing a linear, testable range rather than the saturating curve seen earlier. Plan: Identify concentrations spanning low–mid levels where response is approximately linear; include blanks and several replicate points at each level to assess precision. Data: Use existing DNase measurements, adding extra points around the suspected linear region and appropriate dilutions for high densities. Analysis: Fit data with linear models in different data sections to get high R^2 value fittings. Conclusion: A validated standard curve enables straightforward concentration estimates; samples above range should be diluted and remeasured.