Avida-ED Sample Model Lessons
Note: 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 .................................................................................1
Model In-Class Exercise 1 ..................................................................................................................................2
Model In-Class Exercises 2 ................................................................................................................................7
Model Homework Exercise 1 ...........................................................................................................................9
Model Homework Exercise 2 ........................................................................................................................ 11
Model Open-Ended Experiment Project ................................................................................................. 12
Model Lab Exercise 1 ........................................................................................................................................ 13
Model Lab Exercise 2 ........................................................................................................................................ 17
MODEL IN-CLASS EXERCISE 1
Objectives:
1.
2.
Use Avida-ED as a model of evolution by natural selection.
Apply the principles of random genetic mutation, phenotypic variation, heredity and fitness to explain how
Avidian 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 is
also 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 their
own 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
50
100
200
300
400
500
600
Population size
56
322
895
896
895
896
891
Not
0
29
269
414
495
518
553
Nan
Orn
Ant
And
0
7
38
61
137
289
0
15
17
63
75
0
114
338
498
557
562
0
7
1
0
4
11
Not
Number of individuals
performing given functions
Avida-ED test run
Nan
Orn
600
Ant
500
And
400
300
200
100
0
0
100
200
300
400
500
600
Updates
Observe the population composition at two different time points (e.g., 200 updates and 600
updates) - look at both tabular data and graph - :
- what is similar and different among the two populations? (same size, different phenotype
frequencies, 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
updates
Population at 100
Population at 200 updates
updates
Population at 600
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 evolution
by 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 on
Wilson Road to the engineering department and visit the Digital Evolution Laboratory. Here you’ll 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 organisms—strings of commands—akin to computer viruses. Each organism can produce
tens 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 called
Avida 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, Avida’s 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.
“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 things
replicate, they mutate, they are competing with one another. The very process of natural selection is
happening there. If that’s 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
it’s 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 same
way. Consider a program that adds two numbers. The numbers go in like carbon dioxide and water, and the
sum comes out like a cherry tree.
In the late 1990s Ofria’s 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. An
organism 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, Adami’s 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 I’d 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 the smell of life,” says
Lenski.
It also mirrored Lenski’s own research, launched in 1988, which is now the longest continuously running
experiment in evolution. He began with a single bacterium—Escherichia coli—and 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, they’ve 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 didn’t 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.
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 previously
unimaginable. Modern evolutionary biologists have a wealth of fossils to study, and they can compare the
biochemistry and genes of living species. But they can’t 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
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. That’s where we set up the
conditions 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 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?
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 (we’ll 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
click the 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 aren’t all organisms performing both tasks?
Click “Start New Experiment” again to reset to the default values.
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
Start button. 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?
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
variations 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 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 org’s 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 organism’s DNA. In
AVIDA-ED as well, phenotypic variation (e.g. Nan+, And-) depends upon genotypic
variations, i.e. the different sequences of instructions in the org’s genome that can
produce different functions.
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
and that these can affect the sequence’s 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 <www.avida-ed.msu.edu>. 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 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 offspring’s genome after replication. (For this
exercise, it will be sufficient just to list the mutated instructions. Do that in
clockwise order starting from the 3 o’clock 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 number of mutations always be the same?
Yes / No
- (2) Why?
- (3) Will the specific 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 AFTER
you have written down your predictions above).
- Observations Run #2:
- Observations Run #3:
- Observations Run #4:
• 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 could
test your hypothesis. (Give your answers on the back of this sheet.)
MODEL HOMEWORK EXERCISE 2
10/26/2006 09:49 AM
AVIDA-ED
Page 1 of 2
http://www.msu.edu/course/lbs/144/f06/hw3_avida.html
LBS144 F06
October 26, 2006
Homework #3 (20 pts.)
AVIDA-ED
Introduction
Evolutionary 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 a
computational environment.
In this homework exercise, you will use the computer program, AVIDA-ED, to test
hypotheses 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 AVIDAED 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 Tasks
1. (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-ED
for 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 AM
AVIDA-ED
Page 2 of 2
http://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 Kristin
Bott (kristin.bott@gmail.com) by Sunday, Nov. 5th at 11:59 pm. Kristin may have
additional 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 at
mutation rates of 0.2%, 2.0% and 20%;
Your brief descriptions of your Petri plates from these three runs; and
Your paragraph about the class data.
MODEL OPEN-ENDED EXPERIMENT PROJECT
Spring 2007
Experimental Evolution with Evolving Digital Organisms
Project Tasks:
• Hypothesize: Propose an evolutionary hypothesis—a simple one will do—that 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, and
how 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 the
appropriate 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 should
include (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.
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 (as
reported 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
the soil 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 and require 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 can biodegrade 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 can’t know to solve this problem?
List below:
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 are
using 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.
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 info panel 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 carbonless
paper
1. Get Avida-Ed:
a. Download Avida-Ed [PC ( zip 13 meg or executable 78 meg), Mac ( dmg
22 meg) ] from the Angel group website. If you don't know how to unzip a
2.
3.
4.
5.
file, download the executable. To start Avida-Ed, just double click on the
executable.
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 AvidaEd user manual for more detailed information.
Design the experiment:
1. Write a hypothesis to test an idea about the most efficient method to
evolve 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 your
hypothesis.
 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.
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
record observations that you think might be important, but that are not
included in the experimental design. Use these observations to help you
explain the results.
Data Analysis:
o Plot graphs of the results.
 What are the independent and dependent variables?
 Label the axes?
Discussion:
o Write a description of how your results support or refute the
hypothesis. Use the readings, observation, notes, and information from
the model to explain your results with regard to your hypothesis.
o Propose a protocol for evolving bacteria to degrade TCE based upon
the results.
o
MODEL LAB EXERCISE 2
Artificial Life & Evolution
Objectives:
 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 the
evolution 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 real
world.
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 OnLine Lab Manual for this lab. We will be using AVIDA-Ed (AVIDA for
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 outcompete 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:
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 contain
genetic 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 Avidan’s 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
Avidan’s 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 of
themselves; 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
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 Avidan’s 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.
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:
Viewer
chooser
buttons
Petri dish
viewpane
Panel
Changer
Button
Petri Dish:
Avidans live
here.
Freezer
This is the view you will observe as your population of Avidans evolves.
Statistics
viewpane
You now need to set up the petri dish environment, add a starting Avidan, and
run a simulation.
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).
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 sufficient
nutrition, 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
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 appropriatelymodified 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:
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:
Start/Pause
Button
Be sure this is
set to Fitness.
• 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 Avidans
being 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
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?
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 it’s
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 0.25
in this case:
0.25 x 8 x 8 x 16 = 256 which is close to 277.69
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 population
that 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:
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?
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 sugars
into a table on the blackboard (one column for each group; one row for
each sugar). Why aren’t 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.
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 following
questions 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.
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)? Provide
a plausible explanation for this result. What does this have to do
with evolution in the “real world”?
o Why aren’t the numbers the same for all groups?
d) How do these results address Misconceptions I and II?
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 class’s 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 notose
50
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.

What would you have expected if Misconception III were true?
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 can’t 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 class’s
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.
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.
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?
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 you
don’t 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?”