Growth curve experiments are used to study the physiology of bacteria, yeast, or other micro-organisms. You inoculate cells in a nutrient medium, let them grow, and record the optical density of the culture over time with a spectrophotometer. Automated plate readers can do thousands of growth curves in a single experiment, giving a detailed view of how environmental conditions affect cells.

I’ve spent many hours analyzing growth curves during my PhD, and almost as many hours teaching others to do the same, so I am going to describe a basic growth curve analysis here and try to highlight some quantitative principles and programming techniques along the way. Hopefully this can save you some time if you are new to growth curves and/or programming.

Demo code and some links for further reading are provided at the bottom of this post.

### Simple exponential growth

To guide our analysis, let’s write down some equations. Say that a culture has an initial cell concentration of . Let be the division time, so that after has elapsed, the cell concentration is . After another , the culture has cells per volume, and so on. At this rate, the cell concentration at time is . Defining the “growth rate” , which has units of “doublings per time”, we can write

Real data is measured as optical density , which is linear in cell concentration: . The constant represents the “background” signal on the spectrophotometer when only growth medium and cuvette (or plastic plate) are present. Usually, we measure empirically and calculate the “background-subtracted” optical density . Because this is proportional to cell concentration, it will grow exponentially at the same rate:

where is the (background-subtracted) initial optical density. We can use this to calculate a growth rate given OD measurements at 2 timepoints. For example, if and , then

Take the log2 of both equations and solving for growth rate, we get:

This formula defines over a specific time window, which means you can use it to get the “local exponential growth rate” even when growth rate is changing over time in a complicated way. Basically, the RHS above is a discrete version of the derivative and so the growth rate at any moment is the slope of the tangent line to the log-transformed growth curve. This is a helpful intuition to have in the analysis below.

## Analyzing real data

Now let’s look at real data. I grew some yeast cells in a nutrient medium with glucose as the carbon source and measured the optical density at 600nm every 15 minutes for 65 hours (using a robot). I load the data into MATLAB arrays `odraw`

and `t`

, representing optical density and time, respectively, and then plot `odraw`

versus `t`

. This is the “raw” growth curve, before background subtraction or log-transform.

%% plot raw growth curve % load data load('single_growth_curve.mat'); % plot growth curve figure plot(t,odraw,'-'); xlabel('Time (hours)'); ylabel('OD_{raw} (a.u.)','interpreter','tex');

The plot has an upward-curving exponential phase at the beginning and a “kink” in the curve around . This is a diauxic shift–more on this later. For now, let’s find the growth rate in the initial exponential phase. I measured blank samples to get the background as , and subtract this from `odraw`

. I also take the log2, and store the results in `logod`

. Some measurements may be less than due to noise. To avoid taking the log2 of negative numbers, I enforce a minimum value of , which is about the limit of instrument resolution (3 decimal digits = 1/1000 ). I plot `logod`

versus `t`

, bounding the x-axis at 35 hours.

%% plot log-transformed, background-subtracted growth curve logod = log2(max(odraw – 0.028,2^-10)); figure plot(t,logod,'-'); xlabel('Time (hours)'); ylabel('log_2 OD','interpreter','tex'); xlim([0 35]);

After the log-transform, most of the middle portion of the growth curve is a straight line—this is the exponential phase, and its slope is the growth rate. I choose two points to bracket this range: (5.18,-7.97) and (12.05,-3.04), and calculate a growth rate of doublings / hour, or a doubling time of 83 minutes.

The 2-point method is useful for quickly estimating growth rates, but it doesn’t take advantage of the high time-resolution of the data. Nor is it automated. A better method is to fit a line to the data between 5 and 12 hours. Here this gives a growth rate (i.e. slope) of 0.725, almost identical to the 2-point estimate. However, the line fit is likely to be more robust generally, especially when data is noisy.

% ...plot logod vs t... % fit line to a time window idx = t>5 & t<12; brob = robustfit(t(idx),logod(idx)); % brob(2) is the slope % overlay fitted line hold all x = t(idx); y = brob(1)+brob(2).*t(idx); plot(x([1 end]), y([1 end]),'ro-'); ylim([-10 0]);

A problem with fitting over a defined time window is that cultures can vary in the timing of exponential phase. This is because initial inoculation density varies due to pipetting error, and less dense initial cultures will reach a given OD at a later time. For example, applying the above code to a different strain causes the line fit to “miss” the range of exponential growth, even though this strain doesn’t necessary grow slower. A simple solution is not to choose a time window, but a window of instead. For example, fitting from to is robust to translations of the curve on the time axis. (I’ll skip the code here for conciseness, but you can download the full working script + data below.)

### Scaling up

To analyze many growth curves, I load OD data from a 96-well plate into a MATLAB array `od_all`

with dimensions 8 rows x 12 columns x 262 timepoints. Another array `t`

holds the sampling times, which are common to all wells on this plate. In this experiment, each well contains a different natural isolate yeast strain, which I expect to have different growth behaviors. I plot `logod`

versus `t`

for a 2×3 subset of wells and fit a line to each curve over the same time window as above. The growth rate calculated from this fit is shown in the bottom right for each strain.

%% plot multiple growth curves load('plate_data.mat'); strainNames = readtable('strain_names.csv','readvariablenames',false); [hf ha] = gridplot(2,3,200,200); % custom function for making subplots for r = 3:4 for c = 2:4 axes(ha(3*(r-3)+c-1)); odraw = squeeze(od_all(r,c,:)); logod = log2(max(odraw - 0.028,2^-10)); plot(t,logod,'.-'); xlim([0 35]); ylim([-10 0]); xlabel('Time (hours)'); ylabel('log_2 OD','interpreter','tex'); hold all idx = logod>-8 & logod<-4; brob = robustfit(t(idx),logod(idx)); % brob(2) is the slope x = t(idx); y = brob(1)+brob(2).*t(idx); plot(x([1 end]), y([1 end]),'ro-','linewidth',1.5); adjustaxeslabels([2 3],[r-2 c-1]); labelplot(strainNames{r,c}); labelplot(num2str(brob(2),2),'location','southeast'); end end

### Code & Further Reading

This GitHub repository contains the code and data used to generate the plots above. The main script is called `gc_analysis_code.m`

and contains each section of code in its own cell. A few utility functions (from my utility MATLAB package) are also included in order for the main code to run.

If you want to learn more about growth curve analysis, this review and this paper can get you started on modern methods. For a more conceptual (and historical) perspective, see this 1949 review by Jacques Monod. For examples of growth rate analysis (and more complex growth parameters) being used to answer biological questions, you can probably just close your eyes and point randomly, but my favorite recent examples are this paper on antibiotic drug interactions or this paper on natural phenotypic variation in yeast. At some point in the future, I will also describe how I analyzed diauxic growth curves for my paper on fitness tradeoffs in mixed-nutrient environments.

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