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Description

A repository to host code and data regarding molecular biology-related analysis with a focus on yeast strains, including growth analysis and bioinformatic pipelines. Here you will currently find some R code to analyze the growth of yeast strains, as well as various figures and .drawio files regarding yeast biology-centered parts of the AcubeSAT mission.

Table of Contents

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Growth Analysis

Introduction

The conventional microbial population growth in bulk liquid medium is a very well-studied subject [1]. The generic four-phase pattern of a standard bacterial population growth is known as the growth curve. The lag phase is when microorganisms initially adjust to a new environment; for example when introduced into a test tube with new conditions regarding temperature, pH, sugar concentration, etc. The log or exponential phase is when cells start dividing regularly and the population rises rapidly, reaching maximum growth rate. As the time passes and cell density increases (in a closed system), nutrients diminish. Other changes, for example pH changes, occur due to the microbial high metabolic rates. Moreover, death rate starts increasing until it matches the growth rate. This phase, called stationary phase, is characterized by a constant living cell population. The following phase is denoted as death phase, which can be described as the situation whereon death rate surpasses growthrate and population declination initiates [2].

AcubeSAT example

The AcubeSAT mission will host a scientific payload, wherein a custom-made lab-on-a-chip will be situated, to allow for multiplexed cell culturing and analysis. This PDMS-based chip will host Saccharomyces cerevisiae yeast cells in spore formation. Before the first in-orbit experiment commences, the cells might remain stored inside the spacecraft for up to more than two years. To ensure the cells will still grow in a consistent and timely manner when the time comes, we probed the growth behaviour of the TAF10-GFP MATa strains by conducting a growth analysis on both cells and spores after 1 year of storage in RT.

Code

To perform the growth analysis, we can use the src/growth_analysis/growth_analysis.R file. From there, the growth_analysis() function takes as inputs:

  1. a path with a .csv file that has the respective timepoints (in the first column) and the OD measurements of the desired strains
  2. a number of replicates for each strain
  3. user-defined values/timepoints of OD measurements with cells in the exponential phase (straight line in the linear model) that will be used in the linear regression model (and to calculate the maximal growth rate)

Note: Input (3) should be decided after the plotting step, but for convencience in our case, it can be defined here retrospectively.

The outputs are:

  1. individual and cumulative growth curves (OD versus time),
  2. the linear model fit summary and
  3. maximal growth rate/doubling time for each strain.

Example

As an example, consider conducting a growth analysis of 4 yeast strains from the Yeast GFP library (GFP-tagged TAF10, TEF2, ALG9, UBC6) with 2 replicates each:

# path: /yeast-biology/src/growth_analysis/
source("growth_analysis.R") 
growth_analysis("example.csv", 2, 4:9)

An example .csv file containing the data to be parsed is example.csv, with contents:

Time_hr TAF10_replicate_1_OD TAF10_replicate_2_OD TEF2_replicate_1_OD TEF2_replicate_2_OD ALG9_replicate_1_OD ALG9_replicate_2_OD UBC6_replicate_1_OD UBC6_replicate_2_OD
0 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05
1 0.271 0.181 0.295 0.125 0.278 0.176 0.242 0.141
2 0.299 0.208 0.285 0.156 0.287 0.21 0.268 0.173
3 0.3 0.29 0.23 0.231 0.307 0.3 0.23 0.257
4 0.418 0.41 0.325 0.333 0.421 0.413 0.307 0.365
5.5 0.66 0.66 0.546 0.562 0.675 0.678 0.501 0.618
6.6 1.155 1.135 0.75 0.776 1.125 1.25 0.651 1.015
7.5 1.51 2 1.31 1.435 1.55 1.75 1.1 1.47
8.5 1.89 2.1 1.795 1.81 2.085 2.34 1.49 1.965

An example growth analysis curve (not using the above table data) is: growth curve

Resources

  • Protocols and workflows for the wet lab experiments can be found on our Benchling.

Bibliography

1: Buchanan, R. L., Whiting, R. C., & Damert, W. C. (1997). When is simple good enough: a comparison of the Gompertz, Baranyi, and three-phase linear models for fitting bacterial growth curves. Food microbiology, 14(4), 313-326.

2: Wang, L., Fan, D., Chen, W., & Terentjev, E. M. (2015). Bacterial growth, detachment and cell size control on polyethylene terephthalate surfaces. Scientific reports, 5(1), 1-11.