7-4-7-growth-rate-of-microorganisms

Investigating growth rate using turbidity measurements

• The population growth rate of microorganisms, such as bacteria or yeast, can be investigated by growing the microorganisms in a broth culture

• The turbidity of the suspension can then be used as a way of estimating the number of cells (i.e. the population size) of the microorganisms in the broth culture

  • Turbidity is simply a measure of the cloudiness of a suspension (i.e. how much light can pass through it)

• As the microorganisms in the broth culture reproduce and their population grows, the suspension becomes progressively more turbid (cloudy)

• This changing turbidity can be monitored by measuring how much light can pass through the suspension at fixed time intervals after the initial inoculation of the nutrient broth with the microorganisms

  • A turbidity meter, a light sensor or a colorimeter (connected to a datalogger) can be used to take these measurements

• The results can then be used to plot a population growth curve to show how the population of microorganism grew over time

Using logarithms when investigating bacteria

• Bacterial colonies can grow at rapid rates when in culture, with very large numbers of bacteria produced within hours

• Dealing with the experimental data relating to large numbers of bacteria can be difficult when using traditional linear scales

  • There is a wide range of very small and very large numbers

  • This makes it hard to work out a suitable scale for the axes of graphs

• Logarithmic scales can be very useful when investigating bacteria

What is a logarithmic scale?

• A log scale doesn’t increase by equal amounts like 100, 200, 300

• Instead, it increases by powers of 10:

  • 10²=100

  • 10³=1000

• This allows large changes in data (e.g. population size) to be shown on a compressed axis

• You can recognise a log scale because the intervals on the y-axis are not evenly spaced

Reading a log scale

• Logarithmic scales allow a wide range of values to be shown on one graph

• To read from the logarithmic scale, you should consider the following:

  • Identify the powers of 10 on the log scale

    • For example: 10¹ = 10, 10² = 100, 10³ = 1000

  • Check the spacing

    • On a log scale, the spacing between powers of 10 is even, but the actual numbers between them are not linear

    • This means 100 to 1000 is broken into log-based steps, not 200, 300, etc.

  • Estimate values between powers of 10

    • Use known log₁₀ values:

      • 100 → log₁₀ = 2

      • 320 → log₁₀ ≈ 2.5

      • 1000 → log₁₀ = 3

    • For a value like 320, find the point halfway between 100 and 1000 on the log axis

  • Use a calculator for precision

    • Type log(value) into your calculator to find its position on the scale

    • E.g. log(320) ≈ 2.5, so it’s slightly over halfway between 100 and 1000

  • Read carefully

    • Never assume even spacing = even number gaps

    • Always estimate based on logarithmic spacing

Example: reading a logarithmic scale

• In this example, two graphs show population growth for a microbial population

  • The first uses a linear scale

  • The second uses a logarithmic scale

• At 5 hours, the population size is 320

  • Annotations on each graph show how this reading was taken

    

  

pH scales

• The pH scale is logarithmic

  • The concentration of hydrogen ions varies massively between each pH level