Case Study 2 Part 2 Experiments Worksheet PDF

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F21 BIOL*2400 DE

Case Study 2 Part 2: Avida-ED Experiments Worksheet *Keep this sheet handy when you do the Case Study 2: Part 2 quiz, you may want to refer to it.*

Experiment 1 Table 1. Updates (time) until the first occurrence of an Avidian performing NOT, with and without notose present (a selective advantage or reward) in the environment. Environmental Update number at first occurrence of NOT Treatment Trial

1

2

3

4

5

All resources absent (No reward for NOT) Notose present (Reward for NOT) What is your conclusion from this experiment, based on the above data? Would your answer change if you ran 100 trials instead of 5?

Experiment 2 Stage 1 Selected NOT+ avidian info Environment: Notose present Avidian name: @Notose_NOT+ Fitness: Energy acquisition rate: Offspring cost: Note: the above values will also go in the first row of Table 2.

Pause and think: what do you think will happen to the fitness of @Notose_NOT+ when placed in an environment with no resources? (In our analogy, an E. coli with antibiotic resistance when placed in an environment with no antibiotics.) Explain your reasoning.

Environment: No resources Avidian name: @Notose_NOT+ Fitness: Energy acquisition rate: Offspring cost:

Table 2. Fitness attributes of organisms chosen for use as ancestors in two different selective environments. “@Notose_NOT+” Selective Environment

Fitness (absolute)

Energy Acquisition Rate

Offspring Cost

Notose present No resources Are the fitness values for the Notose_NOT+ ancestor the same, or different, in each of the two environments in Table 2?

If the fitness of the Notose_NOT+ ancestor has changed, what specifically changed and why? Do you think these data make sense?

What conclusions can you draw from this stage of the experiment?

Stage 2 Table 3. Frequency of individuals performing NOT. @Notose_NOT+ after 1,000 updates Selective Number performing Total number of Environment NOT organisms

Frequency Percent of organisms performing NOT

Notose present No resources Screenshot of graph showing avidians performing NOT over time when resources are absent:

Screenshot of graph showing avidians performing NOT over time when notose is present:

How does the average frequency of individuals performing NOT compare in these two treatments?

Describe a mechanism that provides an explanation for the pattern you observed.

Suppose you repeated Experiment 2 Stage 2, but added the @ancestor avidian to the dish alongside @Notose_NOT+. What do you think would happen in both environments?

Return to our analogy of the NOT function being the trait of antibiotic resistance. Does anything surprise you about the results in Table 3? Suppose you had to explain the data (in this analogy) to a colleague. What would you say?

Experiment 3 Stage 1 Table 4. Diversity of descendant types (number of colours) across experimental replicates for various population sizes. Number of ancestor types present after 300 updates Replicate Size

1

2

3

4

5

Average

9 (3x3) 81 (9x9) 361 (19x19) Think back to the content on genetic drift from Unit 04. What conclusions can you draw about genetic drift and population size from the data in Table 4?

Stage 2 The population always begins with 9 different individual Avidians, and hence the initial frequency of any single individual (i.e. flrx_ancestor) is 1/9 = 0.11 or 11%. If you tracked the frequency of this single ancestor type over time, would you expect the frequency to stay the same, increase, or decrease?

Do you think this would be the same or different across the different population sizes? Why?

Use the graph below to draw your predictions for expected frequency of different ancestral strains through time. You can graph your prediction for each population size on the same graph using different colours or types of lines.

Figure 1. Graph of the frequency of a chosen ancestor organism at each of the time updates (every 50 updates). Create three different lines on the graph, one for each of the population sizes. Note: Initial Percentage of any single ancestor at the beginning = 1/9 = 11%; Extinction = 0%; Fixation = 100%.

Table 5. Number and frequency of one particular descendant type every 50 updates. Number of Number of Number of single single single ancestor Freq. = ancestor Freq. = ancestor Freq. = Updates type No./Total type No./Total type Total present (9) present (81) present (361) for 3 x 3 for 9 x 9 for 19 x 19 dish dish dish e.g. e.g. e.g. Ancestor rxfl_ancesto rxfl_ancesto rxfl_ancesto r r r 0 1 0.11 1 0.11 1 0.11 50 100 150 200 250 300 350 400 450 500

Graph the frequency of the ancestor for each population size in Figure 2 using the data from Table 5.

Figure 2. Graph of the frequency of a chosen ancestor organism at each of the time updates (every 50 updates). Create three different lines on the graph, one for each of the population sizes. Note: Initial Percentage of any single ancestor at the beginning = 1/9 = 11%; Extinction = 0%; Fixation = 100%.

Were your predictions correct? If not, how did they differ? What assumptions lead to the different predictions?

Think on our analogy to pathogenic E. coli. If you were a medical scientist and Figure 2 used data from populations with apparent antibiotic resistance, what would your conclusions be?...