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Lab 1 The Role of Mutation in Evolution.docx

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Lab 1-The Role of Mutation in Evolution

Biol& 211 Edmonds College Winter 2020

Objectives

·       Use data from a simulation to explain what it means to say that mutations occur at random.

·       Use data to explore the following statement “Mutations arise based on need/ Evolution is goal-directed”

Introduction:

The basic components of the Darwinian evolutionary mechanism are variation (V), inheritance (I), natural selection (S) and time (T). This exercise focuses on variation and one basic way it can arise.

Natural selection acts on phenotypic variations in a population of organisms. Variations can arise in a population in several different ways. Here we will look only at variations that may be introduced by genetic mutations—random changes in an organism’s genome—and not at other processes such as recombination, horizontal transfer, etc. Genetic mutations may be substitutions (changes from one instruction to a different one), insertions (additions of an instruction into the genome), or deletions (deletions of an instruction from a genome).

As in nature, a population of Avidians—the model organisms in Avida-ED—can vary by what can be seen (for example eye color in humans) as well as those things that can’t be seen. In both cases the variation arises from changes in the DNA sequence. Avidians have a circular genome composed of simple genetic instructions. Different genetic sequences can produce different functions that are visible.

In this simulation, each instruction in the sequence is represented by a colored dot (see the below figure). During genome replication, point mutations may occur at random in the sequence. [Note: For simplicity, this version of Avida-ED allows only substitutions, not insertions or deletions.]

Making Mutant Avidians

Your goal in this exercise is to understand how mutations produce variation and, specifically, what it means to say that mutations in Avidians occur at “random”. You will then explore how these random mutations play a role in evolution by natural selection.

EXERCISE A PROCEDURES

This introductory exercise is a modification of the Introduction to Digital Evolution Handout & Tutorial by Wendy Johnson, Robert T. Pennock and Louise Mead contained in the Hands-on Activity: Studying Evolution with Digital Organisms available via the TeachEngineering.Org website at https://www.teachengineering.org/view_activity.php?url=collection/mis_/activities/mis_avid a/mis_avida_lesson01_activity1.xml.

1.    The page for the Avida-Ed Application that you will be using for this lab can be found here: https://avida-ed.msu.edu/avida-ed-application/ (Links to an external site.)

You can either choose to launch the App within the browser that you have (see that page for which browsers work best), or you can download the App to your computer and work on it outside of the browser.

1.    After opening the application, the starting window that you should see can be seen below.

Pilot Study: Use the Organism Viewer to see how substitution mutations change the genomes of organisms.

1.    In the top left hand corner, you should see a circle with a button titled “organism”. Press this button so that the screen turns from a black grid to an open white box.

2.    In the Organism viewer, click on the “Settings” button found on the top right. Set the per site mutation rate to 10%. Keep repeatability mode set to Experimental.

3.    Flip back to the viewer by pressing “Done”

4.    Drag in the @ancestor from the freezer and click Run to watch it replicate.

5.    Record the position and total # of any changes in the offspring’s genome after replication. (Do this in clockwise order starting from the 3 o’clock position on the circular genome, which is the origin of replication).

6.    Before you record your results, look at the results that were recorded from a sample simulation and familiarize yourself how to record the mutation shown on screen to this lab worksheet.

An example simulation was run with the following results below:

Example Run: _10_ % Mutation Rate. Mark differences from the ancestral genome. They will be highlighted in the organism viewer by a black outline around the letter.

Plot your offspring genome in the line marked “mutations”. A “-“ means the sequence is the same as the ancestor. A capital letter indicates a mutation in the offspring sequence that was not in the ancestor. bolded letters in the ancestor sequence mark site positions 1, 10, 20,30, 40 and 50 respectively. 

Ancestor wzcagcccccccccccccccccccccccccccccccccccczvfcaxgab

Mutations------------------S-------------D-P---E-----------

Total # of mutated sites ___4 sites___

Location(s) of mutated sites  site 19, site 33, site 35 and site 39

Now return to the results that you got from your screen and record your results on “Run #1”.

Run #1: ______ % Mutation Rate. Mark differences from the ancestral genome. They will be highlighted in the organism viewer by a black outline.

Ancestor wzcagcccccccccccccccccccccccccccccccccccczvfcaxgab

Mutations

Total # of mutated sites:

Location(s) of mutated sites:

Compare the results from the Example Run (replicated with a 10% mutation rate) to your Run #1 (also replicated at a 10% mutation rate).

Answer the below Questions.

1. Did each run have the same number and/or type of mutations?

2. If different, what is your explanation for the differences?

3. What do you expect to see if you repeat this using the same ancestor with the same mutation rate?

4. Do you think the specific mutations will be the same? Explain your reasoning.

5. Do you think the number of mutations always be the same? Explain your reasoning

Testing your prediction.

AFTER you have written down your predictions above, repeat the process of replicating the ancestor five more times using the same settings. For each run, record your results below.

Run #2: ______ % Mutation Rate. Mark differences from the ancestral genome. They will be highlighted in the organism viewer by a black outline.

Ancestor wzcagcccccccccccccccccccccccccccccccccccczvfcaxgab

Mutations

Total # of mutated sites:

Location(s) of mutated sites:

Run #3: ______ % Mutation Rate. Mark differences from the ancestral genome. They will be highlighted in the organism viewer by a black outline.

Ancestor wzcagcccccccccccccccccccccccccccccccccccczvfcaxgab

Mutations

Total # of mutated sites:

Location(s) of mutated sites:

Run #4: ______ % Mutation Rate. Mark differences from the ancestral genome. They will be highlighted in the organism viewer by a black outline.

Ancestor wzcagcccccccccccccccccccccccccccccccccccczvfcaxgab

Mutations

Total # of mutated sites:

Location(s) of mutated sites:

Run #5: ______ % Mutation Rate. Mark differences from the ancestral genome. They will be highlighted in the organism viewer by a black outline.

Ancestor wzcagcccccccccccccccccccccccccccccccccccczvfcaxgab

Mutations

Total # of mutated sites:

Location(s) of mutated sites:

Run #6: ______ % Mutation Rate. Mark differences from the ancestral genome. They will be highlighted in the organism viewer by a black outline.

Ancestor wzcagcccccccccccccccccccccccccccccccccccczvfcaxgab

Mutations

Total # of mutated sites:

Location(s) of mutated sites:

After completing the five runs answer the below Questions:

6. Did you get the same number and type of mutations each time?

7. What does the distribution of your mutations look like- can you say one location in the genome is more susceptible to mutation then another part of the genome?

 8. Given your observations, what can you say about the role of mutation in creating variation in a population?

EXERCISE B PROCEDURES

In this exercise you will perform an experiment with two runs to explore the relationship between the time of appearance of a mutation in a population and the presence or absence of a selective agent (advantage). In the first run, you will monitor your run to determine at what update the first individual to perform NOT appears in the population. Next, you’ll do the same thing in a run where NOT is rewarded. Do you think the individual performing NOT will appear earlier or later in the second run? Why?

If you are still in the organism viewer from the previous exercise, click on the population button to get back to the black grid that was present when the App first opened.

Run 1 – First appearance of NOT with all resources turned off.

1.    Click on “setup” found in the upper right hand corner and set the following parameters:

1.    Dish size 30x30

2.    2% Mutation Rate

3.    Drag “@ancestor” from the “Freezer” (on the left side of the screen) into the Ancestral Organism box

4.    Turn off all resources.

5.    Place offspring near parent

6.    Repeatability Mode: Experimental

7.    Pause manually (don’t check the box that says “Pause Run at Update ____ when checked”)

2.    Return to Map view and click on Run.

3.    Pause your Run at the first occurrence of an organism that can perform the function, NOT.

1.    Note 1: “NOT” is one of a few “functions” that are listed on the right side of the screen when you are looking at the Map view.

2.    Note 2: It may take a little while for NOT to occur - it may appear in a couple minutes, not seconds...

3.    Note 3: ...But, once it does occur - things will happen quickly! So, you’ll need to watch closely and be very quick on the Pause button!!

4.    Record the “Update number” of this occurrence (found below the 30x30 grid where everything is happening) in Table 3a under “Run 1”.

5.    Under the control menu choose “Start New Experiment”, and repeat steps 1-4 five more times, recording the Update Number in Table 3a for each run.

Table 3a. Update number for first appearance of an Avidian performing NOT, without a reward (notose) in the environment.

Environmental Condition

Update Number of Run 1

Update Number of Run 2

Update Number of Run 3

Update Number of Run 4

Update Number of Run 5

Update Number of Run 6

No reward for NOT

Run 2 - First appearance of NOT with notose (NOT) turned on.

1.    Under the Control Menu, choose “start a new experiment”.

2.    In the population viewer, click on the “set up” button found in the viewer’s upper right corner.

3.    Set the following parameters (all should be the same as before, except for one change):

1.    World size 30x30

2.    2% Mutation Rate

3.    Drag “@ancestor” into the Ancestral Organism box

4.    Turn off all resources, like before - except check the box for “notose” to turn it on.

5.    Place offspring near parent

6.    Repeatability Mode Experimental

7.    Pause manually.

4.    Return to Map view and click on Run.

5.    Pause your Run at the first occurrence of an organism that can perform the function, NOT. (Note: you’ll need to watch closely and be very quick on the Pause button!!)

6.    Record the “Update number” of this occurrence in Table 3b under “Run 1”.

7.    Under the control menu choose “start a new experiment” and repeat steps 1-4 five more times, recording the Update Number in Table 3b (Runs 2- 6) before starting a new run.

Table 3b. Update number for first appearance of an Avidian performing NOT, with a reward (notose) in the environment.

Environmental Condition

Update Number of Run 1

Update Number of Run 2

Update Number of Run 3

Update Number of Run 4

Update Number of Run 5

Update Number of Run 6

Reward for NOT

1.    Once you completed both sets, examine the numbers in your update runs and answer Questions A-H below. For your convenience, the table has been recreated on that page so that you can combine the two tables into one table.

Questions  Based on Exercise B

Table 4. Update number for first appearance of an Avidian performing NOT, with or without a reward (notose) in the environment. (Fill this in with the data from Tables 3a and 3b)

Environmental Condition

Update Number of Run 1

Update Number of Run 2

Update Number of Run 3

Update Number of Run 4

Update Number of Run 5

Update Number of Run 2

No reward for NOT

Reward for NOT

9. When there was no reward for NOT, did the Avidian performing NOT arise at a certain time (and thus could be predicted for a future run in the same conditions) or does it seem random?

10. When there was a reward for NOT, did the Avidian performing NOT arise at a certain time (and thus could be predicted for a future run in the same conditions) or does it seem random?

11. Did the first occurrence of the mutation happen earlier, later, or at the same time when there was no reward for NOT versus when there was a reward for NOT?

12. Go to the discussion board and post your answer to the previous question. You will get a point on this week’s lab quiz for doing so. Once you have posted your results, you will see the results from other people’s simulations.  Was the answer you got for the previous question “earlier, later, or the same time” the same as everyone else?

13. What would you have expected to see happen in the class data if mutations occur in response to the presence of a selective environment?

14. Did the first occurrence of your mutation happen earlier, later, or at the same time in the class data that you used?

15. Given the data you’ve seen, when do you think mutations arise that can confer a selective advantage relative to the presence of a selective agent?

16. Is the rise of mutation based on need? Explain your answer using the data you collected from this simulation