Lesson 5. Lesson 5: Genetic Drift

Sugat Dabholkar, Kevin Hall, Philip Woods, Connor Bain
Biology
45-60 min
Introductory High School Biology
v2

Overview

Students experiment with a population of bacteria growing in an environment with sugar as an energy source. The population of bacteria consist of different types represented with different colors. Different types of bacteria have different number of flagella; however, in this model there is no selective advantage of having more number of flagella. Students explore this model to investigate the phenomenon of genetic drift. They discover that even though there is no selective advantage of having more or less flagella, eventually only one type survives in the population. This happens because of statistical selection, also referred to as genetic drift.  

Standards

Next Generation Science Standards
  • Life Science
    • [HS-LS2] Ecosystems: Interactions, Energy, and Dynamics
    • [HS-LS4] Biological Evolution: Unity and Diversity
  • NGSS Crosscutting Concept
    • Patterns
    • Systems
    • Stability and Change
  • NGSS Practice
    • Analyzing Data
    • Using Models
    • Conducting Investigations
Computational Thinking in STEM
  • Data Practices
    • Analyzing Data
    • Manipulating Data
    • Visualizing Data
  • Modeling and Simulation Practices
    • Using Computational Models to Find and Test Solutions
    • Using Computational Models to Understand a Concept
  • Computational Problem Solving Practices
    • Troubleshooting and Debugging
  • Systems Thinking Practices
    • Investigating a Complex System as a Whole
    • Thinking in Levels
    • Understanding the Relationships within a System

Credits

Unit designed/developed by Dabholkar, S., Hall K., Woods P., & Bain C.

Acknowledgement

CODAP is developed and built by The Concord Consortium at https://codap.concord.org/  

Lesson 7 is based on the lesson Evolution in Action: The Galápagos Finches Authored by Paul Strode for Howard Hughes Medical Institute based on data collected by Peter and Rosemary Grant, Princeton University.

This work is supported by the National Science Foundation (grants CNS-1138461, CNS-1441041 and DRL-1020101) and the Spencer Foundation (grant 201600069). Any opinions, findings, conclusions, and/or recommendations are those of the investigators and do not necessarily reflect the views of the funding organizations.

Activities

  • 1. Get to know the model
  • 2. Exploration 1: Investigate carrying capacity
  • 3. Make a prediction
  • 4. Test your prediction
  • 5. Exploration 2: Investigate genetic drift
  • 6. Understanding Randomness In Genetic Drift
  • 7. Effect of carrying capacity on genetic drift
  • 8. Discoveries

Student Directions and Resources


In this lesson, you will experiment with a population of bacteria. In this model the environment has sugar that bacteria use as an energy source. There are different phenotypes of bacteria in this population. Different types of bacteria have different number of flagella. Flagella are the appendages that allow bacteria move in specific direction. It's important to note that in this model there is no advantage for a bacterium to have more number of flagella.

Let's explore this model to investigate the phenomenon of genetic drift. Genetic drift is a mechanism of evolution. You will observe and learn about how the population evolves over time because of genetic drift. When you finish working through this lesson, we expect that you learn about genetic drift works as a mechanism of evolution. Let's get started!

1. Get to know the model


The model on this page is of a population of bacteria in an environment where sugar is an energy source. Before we explore the phenomenon of genetic drift, let's get to know this model first. 

[* If the model is runs very slowly in your browser, use this GENETIC DRIFT MODEL for completing this lesson. You MUST have NetLogo installed on your computer to use the downloaded version of the model.]

Click 'SETUP' to initialize the model. Do NOT change any other parameters. 

Make sure that the parameter values are as following:

#-phenotypes = 5

initial-#-bacteria-per-phenotype = 6

left-resource -location right-resource-location
around a central point around a central point
left-resource-distribution right-resource-distribution
20% 20%

Run the model for 500 ticks for three times and answer the following questions.


Question 1.1

Note down the number of bacteria of the following types in the left region and in the right region in each simulation run. 



Question 1.2

Explain why there may be variation in population sizes in the three trials.



Question 1.3

Change the resource distribution to 10% in the left and 80% in the right regions. Run the model. Describe the differences in the population growth in each region.



Question 1.4

In the previous question, you were asked to compare regions with 10% and 80% resource distributions. Before you ran your experiment, did you make sure that the resource locations were the same for both the left and right regions? Why might this be an important step in order to talk about the differences between 10% and 80% resource distributions?



Question 1.5

If, in the third question, you forgot to set the left and right region resource locations to the same thing (like "anywhere" or "horizontal strip"), go ahead and set them to the same thing and re-run your experiment. Using you new results, describe the differences in population growth between the two regions.

2. Exploration 1: Investigate carrying capacity


Do you remember what 'carrying capacity' means from the previous lesson?

Let's investigate how resource distribution influences the carrying capacity in this model.


Question 2.1

Write your definition of carrying capacity.



Question 2.2

Set resource-distribution to 10 %. Run the model. What is the carrying capacity of this environment? 

  less than 50 bacteria
  50 to 100 bacteria
  100 to 200 bacteria
  more than 200 bacteria


Question 2.3

Set resource-distribution to 80%. What is the carrying capacity now?

  less than 50 bacteria
  50 to 100 bacteria
  100 to 200 bacteria
  more than 200 bacteria


Question 2.4

Explain how can you be certain that your carrying capacity numbers are correct.



Question 2.5

How does resource-distribution influence the carrying capacity in this model? Explain your reason.

3. Make a prediction


Imagine a bacterial population where there are only TWO TYPES to begin with. Let's make some predictions about how we expect such population will evolve.

Answer the following questions and then, on the next page, you'll test your predictions using the model.


Question 3.1

What do you expect to happen to the populations if you the model for a little while, like 5000 ticks?

  Both populations will survive
  Only one population will survive


Question 3.2

Which of the following do you expect to happen if you run the simulation for a really long time (for more than 100,000 ticks)? 

  Bacteria of both the phenotypes will survive
  Only one phenotype of bacteria will survive


4. Test your prediction


Set number of phenotypes to 2. 

Set %-resource-distribution to 20%. 

Run the model for 5000 ticks, using the speed slider at the top of the model to make it run faster if necessary. Note your observations in the space provided below the model. Then run the model for really long time (more than 100,000 ticks). Note your observations again.

Explain your observations by answering the questions below. Let's focus on the left region while you answer these questions.


Question 4.1

What were your observations when you ran the model for 5000 ticks? Explain those observations.



Question 4.2

What were your observations when you ran the model for really long time (more than 25,000 ticks)? Explain those observations.

A scientist has run this simulation for 25,000 ticks and has come up with the following plots. You could use these graphs below to answer this question.



Question 4.3

If you repeat the same computational experiment again, will you get the same results? Explain your answer.

5. Exploration 2: Investigate genetic drift


Genetic drift is the process of one type/color surviving without having any selective advantage. In this model, having higher number of flagella do not confer any selective advantage. We've observed that if we run the model long enough, we see only one type survive in the population.

So far we have explored the phenomenon of genetic drift, when there are only two types. Let's explore the phenomenon further when there are multiple types.

Let's focus on the left region while you answer these questions.

Answer question 14 below before you run the model to note down your predictions. Then run the model to test those predictions.


Question 5.1

Increase the number of types of bacteria to 6 or 7. How do you think the results will be different than when you had 2 types? Write your prediction. Do NOT run the model yet.



Question 5.2

Now run the model for at least 30,000 ticks. After you run the model, write your observations and compare those with your predictions. 

6. Understanding Randomness In Genetic Drift


Genetic drift is sometimes referred to as statistical selection, because it is probabilistic or random. Let's understand the randomness in the phenomenon of genetic drift. 


Question 6.1

Set % resource distribution to 20% to both the regions. Set the number of types to 5. Run the model three times until all  phenotype have become extinct except for one.

Note your results in the table below. [If you are working in groups, you could write results from simulation-runs on different computers.]  



Question 6.2

Describe your observations in the computational experiment you performed in the previous question. Explain your observations.



Question 6.3

What conclusions can be drawn from your observations?

7. Effect of carrying capacity on genetic drift


Let's investigate effect of carrying capacity on the process of genetic drift.

Set very low carrying capacity for the left region (10 % resource distribution) and very high carrying capacity for the right region ( 80 % resource distribution). In which region will you expect genetic drift to happen faster?


Question 7.1

What would be outcome of the simulation? Answer this question before you run the model.

  Genetic drift will happen slower in the environment with low carrying capacity.
  Genetic drift will happen faster in the environment with low carrying capacity.
  Genetic drift will happen at the same rate in the environment with low carrying capacity.


Question 7.2

Explain your reasons for the prediction.



Question 7.3

Write you observations after you run the model. Explain those observations.

8. Discoveries


The original idea of this lesson was to explore the phenomenon of genetic drift.


Question 8.1

Explain what genetic drift is in your own words.