Avida ED Exercises

8/2/2019 Avida ED Exercises

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Avida-ED Sample Model LessonsNote: These sample model lessons are provided temporarily for illustrative purposes.

These will be replaced by edited versions after peer-review is completed.

Avida-ED Sample Model Lessons.............................................1Model In-Class Exercise 1.................................................................................3

Objectives:...................................................................................................3Reading (before class):.................................................................................3Zimmer (2005). Testing Darwin. Discover Magazine, 02-05-2005.................3(Only an excerpt should be sufficient: the article is long, and the first part

describes most of what is also relevant to Avida-ED).....................................3Engagement in the classroom:.....................................................................3Possible questions:.......................................................................................3A. How are digital organisms in the Avida environment compare to living

organisms in the natural world? ...................................................................3B. Can we see evolution? Can we experiment with evolution? (elaborate)....3

Mini-lecture, Avida-ED demonstration:.........................................................3Set up conditions for a basic test run, and run the software as the studentsobserve/run it on their own computers..........................................................3Questions and activity:.................................................................................3- What do you expect to see if the test run is repeated multiple times, using

the same parameters? Why?.........................................................................3- Record and plot the results of an evolution experiment as in the following

example:.......................................................................................................3....................................................................................................................4Observe the population composition at two different time points (e.g., 200

updates and 600 updates) - look at both tabular data and graph - :..............5- what is similar and different among the two populations? (same size,

different phenotype frequencies, new phenotype - Nan - is present at 600updates)........................................................................................................5- how do you explain the differences?..........................................................5- label the box-and-arrow model (next page) using the following concepts:

Variation, Mutation, Inheritance, Fitness, Change in Population.....................5...................................................................................................................5

Population at 50 updates Population at100 updates .................................................................................................5

Population at 200 updates Population at600 updates..................................................................................................6

Testing Darwin.................................................................................................6Digital organisms that breed thousands of times faster than common

bacteria are beginning to shed light on some of the biggest unansweredquestions of evolution...................................................................................6Model In-Class Exercises 2...............................................................................9Model Homework Exercise 1..........................................................................11Model Homework Exercise 2..........................................................................13Model Open-Ended Experiment Project..........................................................15Model Lab Exercise 1.....................................................................................16Model Lab Exercise 2.....................................................................................20


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MODEL IN-CLASS EXERCISE 1

Objectives:

1. Use Avida-ED as a model of evolution by natural selection.

2. Apply the principles of random genetic mutation, phenotypic variation, heredity and fitness to explain howAvidian populations change over time.

Reading (before class):

Zimmer (2005). Testing Darwin. Discover Magazine, 02-05-2005

(Only an excerpt should be sufficient: the article is long, and the first part describes most of what isalso relevant to Avida-ED)

Engagement in the classroom:

Possible questions:

A. How are digital organisms in the Avida environment compare to living organisms in the natural

world?

B. Can we see evolution? Can we experiment with evolution? (elaborate)

Mini-lecture, Avida-ED demonstration:

Set up conditions for a basic test run, and run the software as the students observe/run it on theirown computers.

Questions and activity:

- What do you expect to see if the test run is repeated multiple times, using the same parameters?Why?

- Record and plot the results of an evolution experiment as in the following example:

Mutation rate: 0.1%

Available resources: ALL

Time intervals: 100 updates

World size: 30x30 (max. population size 900 Avidians)

Updates Population size Not Nan Orn Ant And

50 56 0

100 322 29 0 0 0

200 895 269 7 0 114 7

300 896 414 38 15 338 1

400 895 495 61 17 498 0

500 896 518 137 63 557 4

600 891 553 289 75 562 11


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Avida-ED test run

0

100

200

300

400

500

600

0 100 200 300 400 500 600

Updates

Numberofindividuals

performinggivenfunctions

Not

Nan

Orn

AntAnd


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Observe the population composition at two different time points (e.g., 200 updates and 600updates) - look at both tabular data and graph - :- what is similar and different among the two populations? (same size, different phenotypefrequencies, new phenotype - Nan - is present at 600 updates)- how do you explain the differences?- label the box-and-arrow model (next page) using the following concepts: Variation,Mutation, Inheritance, Fitness, Change in Population.

Population at 50 updates Population at 100updates


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Population at 200 updates Population at 600updates

From Discover Magazine (02.05.2005)

TESTING DARWIN

Digital organisms that breed thousands of times faster than common bacteria are beginning to shed

light on some of the biggest unanswered questions of evolutionby Carl Zimmer

If you want to find alien life-forms, hold off on booking that trip to the moons of Saturn. You may only

need to catch a plane to East Lansing, Michigan.

The aliens of East Lansing are not made of carbon and water. They have no DNA. Billions of them are

quietly colonizing a cluster of 200 computers in the basement of the Plant and Soil Sciences building at

Michigan State University. To peer into their world, however, you have to walk a few blocks west onWilson Road to the engineering department and visit the Digital Evolution Laboratory. Here youll find a

crew of computer scientists, biologists, and even a philosopher or two gazing at computer monitors,

watching the evolution of bizarre new life-forms.

These are digital organismsstrings of commandsakin to computer viruses. Each organism can producetens of thousands of copies of itself within a matter of minutes. Unlike computer viruses, however, they are

made up of digital bits that can mutate in much the same way DNA mutates. A software program calledAvida allows researchers to track the birth, life, and death of generation after generation of the digital

organisms by scanning columns of numbers that pour down a computer screen like waterfalls.

After more than a decade of development, Avidas digital organisms are now getting close to fulfilling the

definition of biological life. More and more of the features that biologists have said were necessary for life

we can check off, says Robert Pennock, a philosopher at Michigan State and a member of the Avida team.


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Does this, does that, does this. Metabolism? Maybe not quite yet, but getting pretty close.

One thing the digital organisms do particularly well is evolve. Avida is not a simulation of evolution; it is

an instance of it, Pennock says. All the core parts of the Darwinian process are there. These thingsreplicate, they mutate, they are competing with one another. The very process of natural selection is

happening there. If thats central to the definition of life, then these things count.

It may seem strange to talk about a chunk of computer code in the same way you talk about a cherry tree or

a dolphin. But the more biologists think about life, the more compelling the equation becomes. Computer

programs and DNA are both sets of instructions. Computer programs tell a computer how to process

information, while DNA instructs a cell how to assemble proteins.

The ultimate goal of the instructions in DNA is to make new organisms that contain the same genetic

instructions. You could consider a living organism as nothing more than an information channel, where

its transmitting its genome to its offspring, says Charles Ofria, director of the Digital Evolution

Laboratory. And the information stored in the channel is how to build a new channel. So a computer

program that contains instructions for making new copies of itself has taken a significant step toward life.

A cherry tree absorbs raw materials and turns them into useful things. In goes carbon dioxide, water, and

nutrients. Out comes wood, cherries, and toxins to ward off insects. A computer program works the sameway. Consider a program that adds two numbers. The numbers go in like carbon dioxide and water, and thesum comes out like a cherry tree.

In the late 1990s Ofrias former adviser, physicist Chris Adami of Caltech, set out to create the conditions

in which a computer program could evolve the ability to do addition. He created some primitive digital

organisms and at regular intervals presented numbers to them. At first they could do nothing. But each time

a digital organism replicated, there was a small chance that one of its command lines might mutate. On a

rare occasion, these mutations allowed an organism to process one of the numbers in a simple way. Anorganism might acquire the ability simply to read a number, for example, and then produce an identical

output.

Adami rewarded the digital organisms by speeding up the time it took them to reproduce. If an organism

could read two numbers at once, he would speed up its reproduction even more. And if they could add the

numbers, he would give them an even bigger reward.Within six months, Adamis organisms were addition

whizzes. We were able to get them to evolve without fail, he says. But when he stopped to look at exactly

how the organisms were adding numbers, he was more surprised. Some of the ways were obvious, but

with others Id say, What the hell is happening? It seemed completely insane.

On a trip to Michigan State, Adami met microbiologist Richard Lenski, who studies the evolution of

bacteria. Adami later sent Lenski a copy of the Avida software so he could try it out for himself. On a

Friday, Lenski loaded the program into his computer and began to create digital worlds. By Monday he was

tempted to shut down his laboratory and dedicate himself to Avida. It just had thesmellof life, says

Lenski.

It also mirrored Lenskis own research, launched in 1988, which is now the longest continuously runningexperiment in evolution. He began with a single bacteriumEscherichia coliand used its offspring to

found 12 separate colonies of bacteria that he nurtured on a meager diet of glucose, which creates a strong

incentive for the evolution of new ways to survive. Over the past 17 years, the colonies have passed

through 35,000 generations. In the process, theyve become one of the clearest demonstrations that natural

selection is real. All 12 colonies have evolved to the point at which the bacteria can replicate almost twice

as fast as their ancestors. At the same time, the bacterial cells have gotten twice as big. Surprisingly, these

changes didnt unfold in a smooth, linear process. Instead, each colony evolved in sudden jerks, followed

by hundreds of generations of little change, followed by more jerks.


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Similar patterns occur in the evolution of digital organisms in Avida. So Lenski set up digital versions of

his bacterial colonies and has been studying them ever since. He still marvels at the flexibility and speed of

Avida, which not only allow him to alter experimental conditions with a few keystrokes but also to

automatically record every mutation in every organism. In an hour I can gather more information than we

had been able to gather in years of working on bacteria, Lenski says. Avida just spits data at you.

With this newfound power, the Avida team is putting Darwin to the test in a way that was previouslyunimaginable. Modern evolutionary biologists have a wealth of fossils to study, and they can compare the

biochemistry and genes of living species. But they cant look at every single generation and every single

gene that separates a bird, for example, from its two-legged dinosaur ancestors. By contrast, Avida makes it

possible to watch the random mutation and natural selection of digital organisms unfold over millions of

generations. In the process, it is beginning to shed light on some of the biggest questions of evolution. ()

FULL-TEXT ARTICLE AVAILABLE AT:

http://discovermagazine.com/2005/feb/cover
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MODEL IN-CLASS EXERCISES 2

BIOL 391: Evo Modeling

In-Class Project 1: Familiarization with the Avida-ED Platform

The variables we can control are on the Settings screen. Thats where we set up theconditions for evolutionary runs.

First in-class exercise: Using all default settings, turn off all rewards by unchecking the

appropriate boxes in the rewards list on the Settings screen. Flip to the Petri Dish screen

and click the Start button. Monitor the average fitness graph for 250 Updates. Click thePause button as close to 250 updates as you can.

Discussion Questions:1. What is the form of the curve on the average fitness graph?

2. Why would the curve take that form?

3. Click the graph to Number of Organisms. Is the grid full?

In the Petri Dish screen click on Control in the menu bar and click Start New

Experiment. Click Start New Experiment on the popup (well look at freezing (saving)

an evolved Petri Dish later).

Second in-class exercise: On the Settings Screen select just the two boxes associated with

Not and Nand. Uncheck all the other boxes. Flip back to the Petri Dish screen and clickthe Start button. Run for 250 Updates, monitoring the Average Fitness graph. Click the

Pause button as close to 250 updates as you can.

Discussion Questions:1. What is the form of the curve on the average fitness graph?

2. Why would the curve take that form? Why is it different from the first exercise?

3. Click the graph to Number of Organisms. Is the grid full?4. In the table titled Population Statistics how many organisms are performing Not

and Nand? Why arent all organisms performing both tasks?

Click Start New Experiment again to reset to the default values.
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Third in-class exercise: On the Settings Screen use the Mutation Rate slider to set the

Mutation Rate to zero. Leave everything else at the default settings. Flip back to the Petri

Dish and click the Start button. Run it for 200 updates and click the Pause button.

Discussion Questions:

1. Why is the grid a uniform color?2. Why is the Average Fitness curve flat?

Click Start New Experiment again to reset to the default values.

Fourth in-class exercise: On the Settings Screen use the Mutation Rate slider to get as

close to a 10% mutation rate as you can adjust the slider (+/- 0.5% is OK). Leave

everything else at the default settings. Flip back to the Petri Dish screen and click the Startbutton. Let it run for 500 Updates and then pause the run.

Discussion Questions:

1. How does the form of the Average Fitness curve differ from the first two?

2. Why does it look like that?3. On the menu below the graph select Number of Organisms. On the first two runs

the grid filled completely within 250 updates, but with a high mutation rate it has

not filled at 500 updates. Why is it not filled? How would you test your answer in

Avida-ED?
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MODEL HOMEWORK EXERCISE 1

AVIDA-ED HW #1

Understanding the Introduction of Genetic Variations by Random Mutation

Background

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

how it can arise.

Natural selection acts upon phenotypic variations in a population of organisms. Variations

can arise in a population in several different ways. Here we will look only at variationsintroduced by genetic mutationsrandom changes in an organisms genomeand not at

other processes such as recombination, horizontal transfer, etc. Genetic mutations may be

point mutations (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).For simplicity, this version of AVIDA-ED allows only point mutations, not insertions or

deletions in an orgs genome.

In real organisms, heritable phenotypic variation is in part determined by differences in

genotype. For instance, a strain of bacteria may have the ability to metabolize some sugar

(e.g. Glu+) because it produces a particular functional enzyme. Such an enzyme is coded

for and produced by some sequence of instructions in the organisms DNA. In AVIDA-EDas well, phenotypic variation (e.g. Nan+, And-) depends upon genotypic variations, i.e. the

different sequences of instructions in the orgs genome that can produce differentfunctions.

Your goal in this exercise is not to look at what exactly makes a sequence of instructions

functional, but just to understand how mutations produce varieties of genetic sequences andthatthese can affect the sequences functionality. There are several common

misconceptions people have about how random mutations work in the evolutionary process

and these experiments should help you learn to avoid them.

Assignment Tasks

Preparation. Download AVIDA-ED software from . Go to

the Download page and then click the Mac or PC link to get the appropriate version of the

software for your computer. You can also download the user manual, which is the same

for both versions.

Pilot study: Use the Organism Viewer to see how point mutations change the genomes of
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organisms.

(i) Flip to settings. Set the per site mutation rate to 10%. Keep repeatability mode

set to Experimental. Flip back to viewer.(ii) Drag in the @ancestor from the freezer and click play to watch it run.

(iii) Record the changes in the offsprings genome after replication. (For this

exercise, it will be sufficient just to list the mutated instructions. Do that inclockwise order starting from the 3 oclock position on the circular genome).

- Observations Run #1:

Predict: What do you expect to see if you repeat this several times using the same

ancestor with the same mutation rate?

- (1) Will the numberof mutations always be the same? Yes / No

- (2) Why?

- (3) Will thespecific mutations always be the same? Yes / No

- (4) Why?

Test: Repeat steps (ii) and (iii) from the Demo at least three times (but not until AFTERyou have written down your predictions above).




Results: Were your predictions confirmed or disconfirmed?

Discussion: What do your tests above reveal about how genotypic variations arise in a

population?

Future research: Do specific mutations arise because they are the ones that the

organism needs in a given environment? Given your observations above, what would be

your hypothesized answer to this question? Then describe a simple experiment that couldtest your hypothesis. (Give your answers on the back of this sheet.)
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MODEL HOMEWORK EXERCISE 210/26/2006 09:49 AMAVIDA-EDPage 1 of 2http://www.msu.edu/course/lbs/144/f06/hw3_avida.htmlLBS144 F06October 26, 2006

Homework #3 (20 pts.)AVIDA-EDIntroductionEvolutionary theory is widely misunderstood and even rejected by a majority of

Americans. To help address this problem, we are working to adapt an artificial life research platform, "AVIDA", as an educational tool. Organisms in this system,Avidians,are digital organisms that self-replicate, mutate, and adapt by natural selection to acomputational environment.

In this homework exercise, you will use the computer program, AVIDA-ED, to testhypotheses about the evolution of Avidians. We will develop a set of hypotheses about the effect of mutation rate on the overall fitness of a population. We will each run AVIDA- ED under a specified set of conditions, and then create a data set based on everyone's results. We will then explore the class data in lecture and try to understand what the data are telling us.Homework Tasks1. (5 pts.) Download the AVIDA-ED software from Angel. On "My Page" in Angel, go to,"My ANGEL Groups", and in "Find a Group", search for AVIDA under, "Keywords". Enroll in this group, and then download the appropriate version of AVIDA-EDfor the computer that you are using.2. (5 pts.) Write two sets of hypotheses and corresponding null hypotheses regarding what will happen when the mutation rate increases drastically and decreases drastically.(We will do a preview exercise of this in class on Thursday Oct. 26th.)

10/26/2006 09:49 AMAVIDA-EDPage 2 of 2http://www.msu.edu/course/lbs/144/f06/hw3_avida.html Then, on your own, do three runs of AVIDA using the following conditions: World Size =60 X 60; Pause Run at 200 updates, Starting Organism = @ancestor; Mutation Rate =0.2%, 2.0% and 20%.For each of these three runs:Record or make a graph showing fitness as a function of update.Record the Population Size and Average Fitness for your population at Update 200. Briefly describe the visual image of the Petri plate. (I.e., are there a lot of different colors? Are the different colors grouped together? , Etc.) Are there similarities and differences between the three runs?3. (5 pts.) Submit your fitness and population size data from each run in Part 2 to KristinBott ([email protected]) by Sunday, Nov. 5th at 11:59 pm. Kristin may haveadditional instructions on how these data should be submitted.We will explore the class data in lecture on Thursday, Nov. 9th. 4. (5 pts.) After we go over the class data, write a paragraph about what happened. Explain whether the data support or contradict the hypotheses, and why. 5. Turn in the following, on paper, in class Tuesday Nov. 14th:Your two sets of hypotheses and corresponding null hypotheses;Your graphs showing fitness as a function of update for the AVIDA-ED runs atmutation rates of 0.2%, 2.0% and 20%;
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Your brief descriptions of your Petri plates from these three runs; andYour paragraph about the class data.
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MODEL OPEN-ENDED EXPERIMENT PROJECT

Spring 2007

Experimental Evolution with Evolving Digital Organisms

Project Tasks:Hypothesize: Propose an evolutionary hypothesisa simple one will dothat you can

test using Avida-ED.

Design an Experiment: Work up an experimental protocol to follow to test your

hypothesis. You should consider what the relevant variables are, what data to take, andhow many replications are needed. Say what predicted observations will confirm or

disconfirm your hypothesis.

Run Your Experiment & Analyze your data: Follow the protocol and record theappropriate data. Perform statistical tests to as needed to analyze your data.

Write up a report: Write up your experiment as a standard scientific paper. This shouldinclude (i) introductory background information and statement of your hypothesis, (ii)

methods, (iii) results, and (iv) conclusions and discussion. The complete report should be

no more than 5 pages long.
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MODEL LAB EXERCISE 1

LBS-145 Stream II Lab Exercise and Homework #1:Evolving TCE

Biodegraders

The soil and water on the corner of Grand River and Hagadorn are contaminated (asreported last semester in the State News). One of the contaminants is trichloroethylene

(TCE), a hazardous chemical used as a spot remover in dry cleaning. Since bacteria in thesoil are decomposers, theoretically they should be able to break down the toxic chemical to

non-toxic elements (i.e., chloride, water, CO2).

Assume an environmental consulting company visited our LBS-145 class to ask for help

cleaning up the trichloroethylene (TCE). Each year the company spends millions of dollars

on similar spills all over Michigan. Current methods to remove TCE are expensive andrequire that contaminated soil be removed and disposed in hazardous waste dumps. The

environmental consulting company is interested in spiking the soil with bacteria that

biodegrade (break down) TCE. Your goal is to evolve a bacterial strain that canbiodegrade TCE so the soil can be cleaned up on-site instead of dumped into hazardous

waste landfill.

1) What do you know or think you know about this problem?List below:

2) What things do you not know about this problem?List below:

3) What would you like to know, but cant know to solve this problem?List below:
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Background information:

Objective: For your exercise you will use Avida-Ed (an environment for artificial bacterial

evolution) to find the most efficient method to evolve an organism to degrade TCE.

Researchers use models to test hypotheses when an experiment would take too long to

perform, be difficult to manage, or be too expensive to conduct. For this project we areusing a artificial bacterial organisms in a Petri dish (Avida-Ed). In real life, bacterial

populations can double as fast as every 30 minutes. In Avida-Ed, doubling times are about

1 second.

Problem: TCE in Avida-Ed

The or function of Avida-Ed degrades TCE (or is an enzyme). Your job is to evolve

organisms that make or.

On the back of the Petri dish change the environment for evolving organisms by 1)

changing the mutation rate, 2) changing the world size (maximum population size) or 3)

adding or removing the reward for functions (Figure 1). If a function is checked,organisms that make the function are rewarded with a higher reproductive rate. Organisms

that make more functions have higher fitness.

Figure 1: Some features may have changed a little bit in the latest version of the software.
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If an organism in Avida-Ed can make the enzymes that degrade TCE, a number will appear

next to the function or in the organism info and population info panels. The organism

highlighted in Figure 2 can make the functions not and ornot. The population infopanel shows what all organisms in the Petri dish can do. In the example, organisms can

make 766 not, 651 nand and 536 ornot functions. Use the population info panel to

determine if any of the organisms can make or, which consumes TCE.

Figure 2. Some features may have changed a little bit in the latest version of the software.

Downloading Avida-ED software for your own computer:

We set up an Angel guest account that you can access to download the software. The page

also has a discussion section that you can use to post comments.

Complete the Avida-ED laboratory assignment individually or in pairs:

Put Group Name and Names on the hard copies.

Write up the text part of this homework (word process)

Use a spreadsheet to make graphs (Excel) or hand draw your graphs on carbonlesspaper

1. Get Avida-Ed:

a. Download Avida-Ed [PC (zip 13 meg orexecutable 78 meg), Mac ( dmg22 meg) ] from the Angel group website. If you don't know how to unzip a

file, download the executable. To start Avida-Ed, just double click on the

executable.
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b. Complete the tutorial. It is short. This tutorial will quickly familiarize

you with Avida-Ed and how to use it. You may also download the Avida-Ed

user manual for more detailed information.2. Design the experiment:

1. Write a hypothesis to test an idea about the most efficient method toevolve a TCE degrading bacteria. To do so, change variables on the back

side of the Petri dish. The changeable variables include mutation rate,population size, and function rewards.

2. Write a description of an experimental design to test your hypothesis

using Avida-Ed. The dependent variable will be the number of updates to evolve

TCE (or) biodegradation.

In your description, state how the conditions you selected test yourhypothesis.

The description should be clear enough that another group can

replicate your experiment. The design must include at least 5 runs for each treatment.

3. Write a prediction based on the hypothesis before you run the simulation

Remember, a hypothesis does not have to be correct, but your experiment

must be designed to logically test it.2. Simulations:

o Collect data. For each run, write down the parameters you changed and the

number of updates it took to evolve or.During and after each run recordobservations that you think might be important, but that are not included in

the experimental design. Use these observations to help you explain the

results.2. Data Analysis:

o Plot graphs of the results.

What are the independent and dependent variables?

Label the axes?

3. Discussion:

o Write a description of how your results support or refute thehypothesis. Use the readings, observation, notes, and information from themodel to explain your results with regard to your hypothesis.

o Propose a protocol for evolving bacteria to degrade TCE based uponthe results.

o
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MODEL LAB EXERCISE 2

Artificial Life & EvolutionObjectives:

To explore evolution with evolving digital organisms.

To test evolutionary hypotheses.

To try out different evolutionary scenarios.

To address several misconceptions about evolution.

Introduction:

Life only evolved once on earth. In addition, for most organisms,

evolution happens very slowly on a human time scale. As a result, it is difficult

to explore evolution experimentally to address questions like What would

have happened if ? or Did it necessarily have to happen this way?

In the Population Genetics lab, you simulated evolution for a single

simple gene with two alleles. Although this is important, it does not capture

much of the complexity of evolution in the wild.

To explore evolution in more detail, you need organisms with a more

complex genotype that can reproduce rapidly. Many researchers study theevolution of micro-organisms for these reasons. In this lab, you will explore the

evolution of simple digital micro-organisms as they evolve in the computer

simulation, AVIDA.

In AVIDA, the organisms are short computer programs that carry out

only one function: they replicate themselves. They are similar to computer

viruses, which reproduce by copying themselves from one computer to another.

Since computer viruses copy themselves exactly, they do not evolve; any changes

are due to human intervention. The organisms in AVIDA are subject to random

mutation and non-random selection, so they do evolve like organisms in the realworld.

AVIDA was developed by researchers as a tool to study a variety of

evolutionary principles and has resulted in several interesting findings. You can

find links to these on the AVIDA web page; there is a link to this page on the On-

Line Lab Manual for this lab. We will be using AVIDA-Ed (AVIDA for


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Education) developed by Robert Pennock and others. AVIDA-Ed is full-featured

AVIDA with a user-friendly user interface. A link to the AVIDA-Ed home page

can also be found on the On-Line Lab Manual page.

The basic requirements for evolution are:1. Genomes . Organisms must have a genome, a complete set of genetic

instructions for making themselves.

2. Self-reproducing organisms . Organisms must be able to make copies of

themselves, including copies of their genome.

3. Mutation . The copying in (2) is not always perfect, so genomes can

change.

4. Limiting Resources . There is only enough space, resources, etc. for a finite

number of organisms, so some organisms reproduce less frequently than

others.Given these four conditions, the organisms will evolve they will adapt to their

environment by a process of natural selection. The more fit variants will out-

compete the less-fit variants and the population will change over time to adapt to

the given environment.

AVIDA simulates a world that satisfies these four requirements for digital

organisms called Avidans. The AVIDA program simulates the world that the

Avidans live in; it simulates feeding the Avidans, replicating them, and

removing them when they die. In order to understand how this or any other

simulation works, you need to consider each of the four requirements in three

different ways:

A. How these issues manifest themselves in the real world. This will be

shown in italic type.

B. How is this shown in the simulation. This will be shown in bold type.

C. The underlying mechanism that the simulation uses to simulate this

behavior. This will be shown in regular type.

Here are the four requirements in detail:


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1. Genomes . In the real world, most organisms have a DNA genome. This

sequence of DNA determines the genetic properties of that organism and contains

instructions for making that organism. It is not a set of instructions like a

computer program, but it results in the production of a set of proteins, etc. that

are capable of replication, behavior, etc.Avidans have genomes that containgenetic information. This genetic information tells the AVIDA software

how to replicate the organism. Each Avidan has a short circular genome,

like a DNA molecule. In Avidans, there are 26 different kinds of bases

in their DNA, represented by the letters a through z. Each different

base (a through z) corresponds to a particular instruction for the AVIDA

program to execute as it simulates the creature containing that instruction.

A given Avidans genome is always 50 bases long. The particular

arrangement of these bases determines if, and how, the Avidan will

reproduce and behave. A sample Avidan genome is shown below:

Each small circle is a base. The different letters are the different

instructions in the

Avidans genome. They form a simple computer program that is executed

by the

AVIDA software.

2. Self-reproducing organisms . In the real world, organisms make copies ofthemselves; they reproduce. In Bio 112, we have looked at both asexual and

sexual reproduction. Avidans reproduce asexually like bacteria and other

micro-organisms. The simplest viable Avidan genome contains just the

sequence of bases necessary to reproduce itself. It does nothing more

than copy itself. In this way, it is the simplest possible living thing in


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the AVIDA world. The AVIDA program reads the genome of each

Avidan and executes the sequence of commands listed in the genome.

The simplest viable Avidans genome is just the sequence of instructions

needed to tell the AVIDA program to make one copy of itself. Thus, in

one generation, a single viable Avidan gives rise to a copy of the Avidan;so now there are two Avidans. In the next generation, each of the two

produces a daughter, giving a total of four Avidans, etc.

3. Mutation . In the real world, DNA replication is not perfect; daughter cells have

genomes that differ slightly from those of their parents. This gives rise to the

variation that is necessary for evolution. When Avidans replicate, their

genomes are subject to mutation. You can control the chance of

mutation when you set up an AVIDA experiment. Each time the AVIDA

program copies an instruction from a parent Avidan to a daughter

Avidan, there is a small (and adjustable) chance that the copied

instruction will be different from the original (for example, changing an

a to an e). This results in a mutation that will be passed to the

offspring of the daughter.

4. Limiting Resources . In the real world, there is not enough space, food, light,

etc. for all organisms that are born to survive. As a result, some organisms

reproduce more than others, so succeeding generations have a higher frequency of

advantageous alleles. In the world simulated by AVIDA, there is plenty of

food, but space is limiting. Therefore, only a fixed number of Avidans

can be alive at any given time. This number is adjustable, but it

defaults to 900. When an Avidan is born (copied from parent to

daughter), it replaces a randomly-chosen neighbor of its parent. Open

spaces result when Avidans die. Avidans are chosen randomly for death

independent of genotype. Avidans are deleted randomly from the petri

dish.

In this lab, you will explore the evolution of Avidans and make

connections between this simulated world and evolution in the real world.


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Procedure

Part I: Warm-up Exercises

1) Start up AVIDA-Ed from the dock. It takes a little while to get started.

2) You will see something like this:

Viewerchooser

buttons

Petri dish

viewpane

Petri Dish:

Avidans live here.

Statistic

viewpan

Freezer

Panel

ChangerButton


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This is the view you will observe as your population of Avidans evolves.

You now need to set up the petri dish environment, add a starting Avidan, and

run a simulation.


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3) Click the Panel Changer Button (it is at the upper right of the Petri Dish

Viewpane and is marked Flip to Settings to get to the settings panel. You

should see this in the center panel:

This allows you to set the environmental parameters for the simulation run. The

are:

Per site mutation rate: This rate reflects the percent chance that an instruction

is incorrectly copied. So, if the per site mutation rate is 1%, there is a 1%chance that when an instruction is copied, it will end up as any one of the 26

possible instructions (one of which is itself, so it could mutate back to itself).

With a 1% per site mutation rate, if 100 instructions are copied one of them

will be mutated on average (although this number could be higher or lower

in any instance).


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World size: Sets the maximum number of Avidians that can exist in the

population. The two numbers specify the number of Avidians per row, and

per column. So, 10 x 10 = a maximum population of 100 organisms.

Ancestral organism(s): The organism(s) the population begins from. Drag in

organisms from the Freezer at the beginning of a run. Environmental Resource Settings: Avidians can receive extra energy and

have increased fitness if they evolve the ability to metabolize nutrients.

Here you can set what nutrients are available in the environment.

Exact Repeatability: Many steps in an Avida evolutionary run happen

randomly (e.g. what mutations will occur in the genome, into what cell a new

organism will be placed at division), so each run will be slightly different

even with the same general environmental values, as in nature. This is the

default setting. However, if you need to repeat a run (e.g. for a

demonstration) you can switch this to exactly replicate the sequence with the

same mutations and values.

Offspring placement: When an offspring is born, it can either be placed (at

random) in any of the eight cells adjacent to its parent, or anywhere (at

random) in the population. If the cell is already occupied the organism there

is overwritten.

Pause Run Manually/Automatically: If you set a specific number ahead of

time, the run will pause when this many updates have passed. If you set the

run to stop manually, it will continue indefinitely until it is paused using the

button under the Petri dish.

Freeze Petri Dish Button: Push snowflake button to save either just the

environmental configuration (by saving an empty) Petri dish, or else the

environment plus the organisms (by saving a full Petri dish).

For this lab, the most important one is the Environmental Resource

Settings. This models different resources available in nature and allows you to see how

evolution would proceed if conditions were different. Some of these resources are more

nutritious than others. Although all Avidans in the petri dish receive sufficientnutrition, if they are able to metabolize certain additional nutrient sugars

(notose, nanose, etc.) they receive a substantial increase in fitness. For

example, an Avidan that can use notose has twice the fitness of an otherwise

identical Avidan that cannot. Other sugars have even higher fitness

rewards; these are listed above the buttons in this part of the pane. Avidans


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that are more fit reproduce more often than those that are less fit. In the

AVIDA system, an Avidan is able to use a sugar if it can perform a particular

simple numerical calculation. The AVIDA system tries to send a number to each

Avidan, if that Avidan can read in that number and send back an appropriately-

modified number, then AVIDA gives that Avidan a fitness boost.Importantly, it is easier to evolve the ability to utilize some sugars than others.

That is, it takes more alterations of the starting Avidan to allow it to utilize equose than

to allow it to use notose. In the real world, some nutrient sources require more enzymes,

or more highly-modified enzymes, to be utilized by an organism. The ancestor Avidan

cannot use any of the sugars in the Envoironmental Resource Settings. Some

mutant versions of the ancestor can use some or all of these sugars. It requires

only a few mutations to make an Avidan that can use notose; it requires many

independent mutations to use equose. In order to use any sugar, the Avidan

must include instructions for getting a number from AVIDA and returning it to

AVIDA. It must also include instructions for the particular mathematical

manipulation. Some manipulations are simple, like the one for notose, and take

only a few more instructions; others are more complex, like the one for equose,

and take many additional instructions.

You should leave all the other settings at their default values in this part of

the lab. You may want to play with some of them in Part II.

4) For this first run, you should turn on all of the sugars in the Environmental

Resource Setting - this is the default. In this state, Avidans that have the ability

to use sugars are more fit than those that do not.

5) Load an ancestor Avidan into the petri dish. In the Freezer, look under

Organisms. Click on @ancestor and drag it into the Ancestral Organisms

pane described above. You should see this in the pane:


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Now you are ready to run a simulation and let these organisms evolve.

6) Click on the Flip to Petri Dish button in the upper right of the

Environmental Settings Panel. This will take you back to the petri dish view.

7) Start the simulation. At the bottom of the Petri Dish viewpane, you will see

these buttons:

Set the color code selector shown above to Fitness.

Click on the Start/pause button and the simulation will start.

You will then see several things happening:

Colored squares will start appearing in the petri dish - these are Avidansbeing born.

The Time (Updates) will start to increase to indicate that the simulation is

running.

The graph at the lower right of the Statistics Workpane will start being

drawn to show the average fitness of the Avidans in the petri dish.

The Population Statistics numbers at the upper right of the Statistics

Start/Pause

ButtonBe sure this is set

to Fitness.


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Workpane will start to change.

The arrow button will change to a || - pause - button.

8) Quickly pause the simulation by clicking the pause button after about 50

updates.

9) You should look at the various displays and discuss as a class what they mean:

The Average Fitness Graph. The ancestor has a fitness of 0.25. You will

note that the average fitness of the population falls briefly before rising.

Provide a plausible explanation for this observation.

The Color Scale Legend - the rainbow stripe just below the petri dish.

This is the color code for the Avidans in the petri dish - their color

depends on their fitness. This is useful because, in addition to showing

the fitness of each Avidan, it is constantly updated as the fitness of the

creatures increases. Therefore, if you look at the maximum value of this

legend, it gives you the fitness of the most fit Avidan currently in the petri

dish. Click on an Avidan with a low fitness and look in the Statistics

Workpane under Org. Clicked on Report to find the exact fitness of the

Avidan you clicked on.

10) Click the start (arrow) button to continue the simulation. Let it run until the

petri dish is full and the Population Size is about 900 and then click the pause

button. From the menu under the graph, choose Number of Organisms and you

will see a graph of the number of organisms over time. What kind of growth

does that show (linear, exponential, logistic)? Why?


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11) Run the simulation for a while longer until the highest fitness (shown by the

number at the right end of the color code bar above the start/pause button) gets

to about 100 or more. Pause the simulation and click on the Avidan with the

highest fitness. Look in the Org. Clicked on Report and you will see

something like this:

What is its fitness? What sugars can it use? How does this explain its

high fitness?

In the example at the right:

Fitness = 277.69

It can use Orose, Antose, and Norose

The high fitness = (base fitness) X (bonus from each sugar)

- the base fitness is 0.25in this case:

0.25 x 8 x 8 x 16 = 256 which is close to 277.69


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Part II: Misconceptions about Evolution

You will now use Avida to explore four important misconceptions about

evolution.

Misconception I: Mutations always reduce the fitness of organisms. In fact,mutations can be neutral, advantageous, or disadvantagous.

Misconception II:The presence of a selective agent causes advantageous mutations to

occur. In fact, the mutations occur randomly independent of the selection;

selection then favors the advantageous mutations.

You will address these two misconceptions through the following

experiments:

1) Choose Start New Experiment from the Control menu. Click Discard

and start new experiment.

2) Drag in a single @ancestor as you did before.

3) For this first run, you should turn off all of the sugars in the Environmental

Resource Settings. Click the checkboxes on all of them until they are all

unselected. In this state, all Avidans receive minimal nutrition and there is no

added fitness associated with being able to use any of the sugars.

4) Be sure the display is set to show Fitness.

5) Click the start button and let the simulation run until about 300

updates have passed, then click the pause button. Look in the

Population Statistics, you should see something like this (your

numbers will be different):

This display shows the number of Avidans in the populationthat are able to use each of the different sugars. You can click on

the buttons to identify the organisms that are able to use that sugar.

If you click on one of the identified Avidans, you will see that its

fitness is not higher even though it is able to use the sugar. You

should discuss the following questions as a class:


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a) Why is it that the Avidans that can use notose do not have significantly

higher fitness here? What does this have to do with evolution in the real

world?


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b) How is it possible that these Avidans have evolved to use these sugars

even though the sugars are not present? What would you have expected

if Misconception II were correct?

c) Pool the class results of how many Avidans could use each of the sugarsinto a table on the blackboard (one column for each group; one row for

each sugar). Why arent the numbers the same for all groups? What does

this have to do with evolution in the real world?

d) What is the range of fitness (lowest to highest) of the organisms in your

population?

e) Note the Average Fitness; we will use this data later.


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6) Now it is time for a new run. Select Start New Experiment from the Control

menu (or hit apple-R) and click Discard and Start New Experiment. The petri

dish will clear.

7) Click the Flip to Settings button.

8) Set up for your next run:

Start with one @ancestor as in step (2).

Set the environment to contain one and only one sugar: notose (upper left

of the list). Be sure that notose and only notose is selected.

Click the Flip to Petri Dish button.

9) Click the run (arrow) button and let the simulation run for about 300 updates

and then click the pause button. You should then answer the followingquestions as a class:

a) What is the Average Fitness? How does it compare to the average fitness

you observed in Step (5e)? Provide a plausible explanation for this result.

What would you have expected if Misconception I were correct?

b) What is the range of fitness values in your population? Does it differ from

your answer to (5d)? Provide a plausible explanation for this result.


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c) Pool the class results of how many Avidans could use each of the sugars

into a table on the blackboard (one column for each group; one row for

each sugar).

o How do these numbers differ from those you saw in (5c)? Providea plausible explanation for this result. What does this have to do

with evolution in the real world?

o Why arent the numbers the same for all groups?

d) How do these results address Misconceptions I and II?


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Misconception III:Because mutations are random, they cannot lead to the orderly

progress that is evolution. In fact, although mutations are random, selection

provides the guide that leads to an orderly change.

To address this misconception, you will need to pool the classs data.

1) Start a new run with @ancestor and all sugars present.

2) Stop the simulation when an Avidan appears that can use a new sugar. Note

the time at which this new feature evolved and the sugar involved. Then

continue the run and log when the ability to use other sugars evolves. For

example, you might find that:

Time Event

10 first Avidan that could use notose50 first Avidan that could use nanose

etc.

3) Do this for several runs and pool your results.

Is there any pattern to the order and timing of appearance of the different

types of Avidans?

Provide a plausible explanation for this pattern.


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What would you have expected if Misconception III were true?


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Misconception IV: Complex features (for example an eye) cannot evolve because either:

they are too complex to arise from one mutation

or if you tried to evolve a complex feature in several intermediate steps, there

would be no advantage for the intermediates (for example, a lens without a

retina), so they would never evolve.In fact, it is true that complex features are unlikely to evolve in one step.

However, complex features do evolve because the intermediates (for example,

primitive eyes rather than partial eyes) do confer some selective advantage. You

can look at it like this: although you cant jump to the top of a cliff in one jump,

you can get there if there is a staircase of intermediate steps and jump from one

to the other.

Addressing this misconception will also require pooling of the classs

data.

In this case, the complex feature will be the ability to use one of the

difficult sugars - one that typically evolves later than the others. In this case,

ornose. You will compare two different scenarios:

a) All in one jump - you will only provide ornose. In this case, the

Avidans will have to evolve the ability to use ornose without any

intermediate steps. Although they may happen to evolve the ability to

use simpler sugars along the way, there will be no advantage for this.

They only get a fitness boost if they get all the way to ornose.

b) Up the staircase - you will provide all the sugars in addition to ornose.

That way, Avidans who make the easy step of being to use any sugar will

get at least a small advantage. These Avidans will be at a selective

advantage and take over the population. From this new population, it

will take fewer mutations to get to the next sugar and finally on to ornose.

The other sugars provide a selective advantage for partial use of ornose -

steps on the ladder to ornose.

You will compare these two scenarios in terms of the amount of time it

takes to evolve the ability to use ornose. Half of the class should set up scenario

(a) and the other half scenario (b). Each should do many runs and tally the time

it took for the first Avidan capable of using ornose to appear. Since there will be

a wide range to these times, you will need to do many repeated runs and pool

your data.


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NOTE: if you are doing scenario (a), you will need to turn off all sugars

except ornose every time you set up a new run. Be sure to do this, since the

program defaults to including all sugars in every run.


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After you have collected your data, answer the following questions:

What are the results? Which scenario allows the more rapid evolution of

this complex trait?

How does this relate to evolution in the real world?

What would you have expected if Misconception IV were correct?


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Lab Report

Must be typed; hand-drawn graphs are acceptable.

Due at the start of lab during the week indicated on the syllabus; this is a firm

deadline.

Your lab report must be in your own words.

Your lab report must include:

Choose any one of the four misconceptions described in the lab manual and

answer the following questions about that misconception.

1) Which misconception did you choose?

2) Although misconceptions are not correct, they often seem reasonable if youdont know all the details. Explain what might lead someone to think that the

misconception you chose was plausible.

3) Explain how the data you collected in lab shows that the misconception you

chose is incorrect.

4) A skeptic could argue, Avida is just a computer simulation. It has nothing to

do with real organisms. Any conclusions you draw from it are not relevant in the

real world. How would you argue that, although Avida is a computer

simulation, the results from it are still relevant to the misconception you chose?

In other words, In what relevant ways is Avida similar to the real world so as to

allow one to draw meaningful conclusions about this misconception?

Documents

Avida ED Exercises