Do you think bacteria can make smart decisions? Let's investigate!

Imagine that you are a bacterium.

In order to run the computational models that we will use in this course on your computer, you need to install a software called NetLogo.

Use this link to download and install NetLogo: DOWNLOAD NETLOGO

Click here to download the model.

We are going to be real scientists to figure out if bacteria can make smart decisions. We are going to use a computational model to perform our research investigations.

Let’s get to know the model first!

Components of the model:

How to run the model:

1. Click ‘SETUP’ to set the initial state for the bacterial cell.

This step is to setup the initial positions of the violet and brown proteins inside the cell. If you click ‘SETUP’ again, the positions of the violet and brown proteins change, whereas the position of the DNA stays the same.

2. Click ‘Go’ to run the model.

This model is a computational simulation of the external and internal environments of a bacterial cell. When you click ‘Go’, you can see the protein molecules move around inside the cell. They do not go outside of the cell. Some of them interact with DNA. Observe their interactions with the DNA. DNA and proteins are molecular machines. Smart decisions that cells make are because of interactions between genes and proteins.

3. Sugar control:

We are going to investigate how bacteria cells smartly make decisions to eat different sugars. In their natural environments, bacteria use different food sources to produce energy. They need energy to survive and reproduce. If they don’t get enough energy they die.

In our experiments, we can control which sugar is available to bacteria by turning ON or OFF the following switches:

Glucose and lactose are two different types of sugars. Using these switches, we can have different combinations of these two sugars available to bacteria.

For example, keeping these two switches ON means both these sugars are available to bacteria.

4. Genetic Control:

We have several sliders available to control the genetic properties of the bacterial cell.

We will investigate what each of these sliders do during the course of our investigation.

Use the RESET button to set the values to default.

Molecular biologists and synthetic biologists, which are special types of scientists, make such changes in real cells. We will make these changes to our computational cell!

NetLogo’s logging facility allows researchers to record student actions for later analysis.

Use the following information to find a logging file on your computer.

Logs are stored in the OS-specific temp directory. On most Unix-like systems that is /tmp. On Windows computers the logs can be found in c:\Users\<user>\AppData\Local\Temp, where <user> is the logged in user.

On Mac OS X, the temp directory varies for each user. You can determine your temp directory by opening the Terminal application and typing echo $TMPDIR at the prompt.

After you find the log files (.xml format), check for the file names that correspond to the date today. Upload those files.

In this lesson, you will explore the Genetic Switch model further. The focus of this lesson is on energy of the cell and cell division. 

Let's get started!

Let's explore the effects of the presence or absence of sugar/s in the environment on the energy of the cell.

Follow the instructions below to get started:

Open NetLogo folder and click on NetLogo Logging.

Open the Genetic Switch NetLogo Model that you downloaded earlier.

Let's understand how presence or absence of sugar/s in the environment affect energy of a cell.

Conduct computational experiments using the Genetic Switch Model. 

Let's understand how the energy graph of the cell changes as time progresses. 

Conduct scientific investigations using Genetic Switch Model to answer the following questions.

Let's try to understand the relationship between energy of the cell and cell division in this model.

NetLogo’s logging facility allows researchers to record student actions for later analysis.

Use the following information to find a logging file on your computer.

Logs are stored in the OS-specific temp directory. On most Unix-like systems that is /tmp. On Windows computers the logs can be found in c:\Users\<user>\AppData\Local\Temp, where <user> is the logged in user.

On Mac OS X, the temp directory varies for each user. You can determine your temp directory by opening the Terminal application and typing echo $TMPDIR at the prompt.

After you find the log files (.xml format), check for the file names that correspond to the date today. Upload those files.

In this lesson, you will explore the relationship with DNA and proteins in the context of this model.

What do you know about DNA and proteins? It's perfectly ok, if you do not know much. We will use this model to understand some of the functions different proteins and different parts of DNA play inside a cell.

Let's use the Genetic Switch model to understand the interactions between DNA and proteins.

Follow the instructions below to get started:

Open NetLogo folder and click on NetLogo Logging.

Open the Genetic Switch NetLogo Model that you downloaded earlier.

Proteins perform different functions. Let's explore the model to understand the different functions that the proteins in our model perform. 

Some proteins seem to interact with DNA. Let's figure out these interactions.

NetLogo’s logging facility allows researchers to record student actions for later analysis.

Use the following information to find a logging file on your computer.

Logs are stored in the OS-specific temp directory. On most Unix-like systems that is /tmp. On Windows computers the logs can be found in c:\Users\<user>\AppData\Local\Temp, where <user> is the logged in user.

On Mac OS X, the temp directory varies for each user. You can determine your temp directory by opening the Terminal application and typing echo $TMPDIR at the prompt.

After you find the log files (.xml format), check for the file names that correspond to the date today. Upload those files.

In this lesson, you will explore DNA protein interactions further to understand the molecular mechanisms of genetic regulation.

Let's get started!

Some proteins are always present, whereas some proteins appear and disappear based on certain conditions, like the presence or absence of certain sugars.

Let's understand why the appearance and disappearance of the proteins might be important for a cell.

In this lesson, we are trying to understand the mechanism of genetic regulation in case of this particular genetic switch. Let's describe it as we understand it.

NetLogo’s logging facility allows researchers to record student actions for later analysis.

Use the following information to find a logging file on your computer.

Logs are stored in the OS-specific temp directory. On most Unix-like systems that is /tmp. On Windows computers the logs can be found in c:\Users\<user>\AppData\Local\Temp, where <user> is the logged in user.

On Mac OS X, the temp directory varies for each user. You can determine your temp directory by opening the Terminal application and typing echo $TMPDIR at the prompt.

After you find the log files (.xml format), check for the file names that correspond to the date today. Upload those files.

In this lesson, you will explore a computational model of bacterial population to understand the idea of genetic grift and influence of carrying capacity in the process of genetic drift.

Click here to download the model.

Follow the instructions below to get started:

Open NetLogo folder and click on NetLogo Logging.

Open the Genetic Switch NetLogo Model that you downloaded earlier.

This is a model of a population of bacterial cells, E. coli.

The model starts with different colored E. coli cells, randomly distributed across the world. The E. coli cells move around the world and eat sugar if it’s available to them where they are present. Grey patches (in the image below) contain sugar. Eating sugar increases the energy of an E. coli cell, whereas movement and basic metabolic processes decrease its energy. When the energy of a cell doubles, it reproduces to form two daughter cells of its type (of the same color). If the energy of an E.coli cell reduces to zero, the cell dies.

Different colored cells do not have any ‘advantage’ over other cells in terms of growth rate or sugar consumption.

Components of the model:

How to run the model:

• Click ‘SETUP’ to set the initial population of the bacterial cells.
• Click ‘Go’ to run the model.

This model simulates the growth of a bacterial population. As the model progresses the cells move around. If they are at a patch that has sugar, they eat it.

• Number of types:

Use this slider to set the initial number of types (colors) of bacteria in the world.

• Maximum initial population:

Use this slider to set the maximum number of bacteria of all colors in the initial population in the world.

• Carrying capacity:

Use this slider to set the carrying capacity of the world. Carrying capacity is the maximum population that can be sustained in the world. This slider changes the availability of sugar in the world and thus controls the maximum population.

This is a model simulating the growth of a bacterial population in an environment containing sugar. Bacteria eat sugar and divide. Thus, the population of bacteria grow.

Start the simulation with one type of bacteria.

Let's investigate how the bacterial population changes over time.

Prediction time!

Set the carrying capacity to medium. Set the number of types of bacteria to ‘two’. Set maximum initial population to 10. Do NOT run the model, yet. Answer the questions below first.

Design a computational experiment to test your predictions.

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

Let’s investigate the effects of carrying capacity on this process of genetic drift.

Genetic drift is the process of one color surviving without having any selective advantage. How would the process of genetic drift differ at high and low carrying capacities? Make a prediction.

NetLogo’s logging facility allows researchers to record student actions for later analysis.

Use the following information to find a logging file on your computer.

Logs are stored in the OS-specific temp directory. On most Unix-like systems that is /tmp. On Windows computers the logs can be found in c:\Users\<user>\AppData\Local\Temp, where <user> is the logged in user.

On Mac OS X, the temp directory varies for each user. You can determine your temp directory by opening the Terminal application and typing echo $TMPDIR at the prompt.

After you find the log files (.xml format), check for the file names that correspond to the date today. Upload those files.

In this lesson, you will use a different version of the model of bacterial population from the previous lesson to explore and understand the idea of natural selection.

Click here to download the model.

Follow the instructions below to get started:

Open NetLogo folder and click on NetLogo Logging.

Open the 'Genetic Drift  and Natural Selection' NetLogo Model that you downloaded earlier.

This is also a model of a population of bacterial cells, E. coli, like the previous one.

The only difference is that there is something called '%-advantage' in this model. 

You can decide which type of E. coli  can have this selective advantage using another slider.

Let's start the simulation with the following conditions –

• number-of-types = 1
• carrying-capacity = “high”
• ecoli-with-selective-advantage = “red”
• natural-selection? ON
• max-initial-population 1

Design an experiment to see if %-advantage helps a type (color) to win the evolutionary competition in case of natural selection.  

NetLogo’s logging facility allows researchers to record student actions for later analysis.

Use the following information to find a logging file on your computer.

Logs are stored in the OS-specific temp directory. On most Unix-like systems that is /tmp. On Windows computers the logs can be found in c:\Users\<user>\AppData\Local\Temp, where <user> is the logged in user.

On Mac OS X, the temp directory varies for each user. You can determine your temp directory by opening the Terminal application and typing echo $TMPDIR at the prompt.

After you find the log files (.xml format), check for the file names that correspond to the date today. Upload those files.

In this lesson, you will use the genetic switch model to make modifications in the genetic circuit of the cell to make it fitter to compete and survive.

The models that you build will be used compete against each other in a population model to see which one survives and performs better in a competition.

Let's use the Genetic Switch model to understand how different parameters affect behavior of a cell.

Follow the instructions below to get started:

Open NetLogo folder and click on NetLogo Logging.

Open the Genetic Switch NetLogo Model that you downloaded earlier.

Or click here to download the model.

Let's refresh our memory!

Let’s focus on these three parameters.

Explain how each parameter affects the model. What changes do you see in the model when you change the values of these parameters? Describe the changes in terms of the number of protein molecules, or DNA-protein interaction.

Change one or more of the three parameters shown above in the model.

Make a prediction about how it will influence the behavior of the genetic switch. Design a test to see if your cell does better or worse in terms of responding to the change in the external environment and fast cell division rate.

What would be a beneficial behavior to get a cell to reproduce faster in a changing external environment in terms of the availability of sugar?

[In Evolutionary Biology lingo, this is called ‘Phenotype that has higher fitness’.]

Each of the teams are going to design ‘genetic circuits’ now. Work with your group. Design a ‘genetic circuit’ in your cell, so that the cell will have higher fitness. List the changes you made and explain why you made those changes.

You will get one more chance to enter the competition.

In this lesson, you will use a model of a Karnataka Straw Bird (Avis plasticopapyrus) living in arid regions of North Karnataka in India.

Only those birds which can successfully fly the long distances between the sparsely spaced food sources will be able to live long enough to breed successfully. In this lesson you will breed several generations of Avis plasticopapyrus and observe the effect of various changes on the evolutionary success of these birds.

Make sure you have the following material on your workstation.

chart paper, tape, straws, scissors, coin, six-sided die

Prepare the ancestral/parental bird:

Cut two strips of paper, each 3 cm x 20 cm.

Loop one strip of paper with a 1 cm overlap and tape. Repeat for the other strip.

Tape each loop 3 cm from the end of the straw.

Find a reference picture below:

Read instructions very carefully about how the bird will breed and produce offspring.

Each Origami Bird lays a clutch of three.

Record the dimensions of each chick and hatch the birds using these instructions:

The first chick is exactly like the parent. In the interest of time you may substitute the parent when testing this chick.

The other two chicks have changes based on the following rules:

For each chick, flip your coin and throw your die then record the results. 

Rule 1: The coin flip determines where the mutation occurs: the head end or tail end of the bird

HEAD     ►   Head/cephalic end

TAIL        ►  Tail/caudal end

Rule 2: The die throw determines how the mutations affect the wing:

1 = The wing moves 1 cm toward the end of the straw

2 = The wing moves 1 cm away from the end of the straw

3 = The circumference of the wing increases 2 cm

4 = The circumference of the wing decreases 2 cm

5 = The width of the wing increases 1 cm

6 = The width of the wing decreases 1 cm

Lethal changes:

A change which results in a wing falling off the end of straw, or in which the circumference of the wing is smaller than the circumference of the straw, etc. is lethal.

Survival of the birds:

You will release the birds with a gentle, overhand throw, making sure that you release the birds as uniformly as possible.

You will test each bird twice.

The most successful bird is the one which can fly the farthest.

The most successful bird is the sole parent of the next generation.

Breeding instructions:

Here are the instructions again for your quick reference. 

Each Origami Bird lays a clutch of three.

Record the dimensions of each chick and hatch the birds using these instructions:

The first chick is exactly like the parent. In the interest of time you may substitute the parent when testing this chick.

The other two chicks have changes based on the following rules:

For each chick, flip your coin and throw your die then record the results.

Rule 1: The coin flip determines where the mutation occurs: the head end or tail end of the bird

HEAD     ►   Head/cephalic end

TAIL        ►  Tail/caudal end

Rule 2: The die throw determines how the mutations affect the wing:

1 = The wing moves 1 cm toward the end of the straw

2 = The wing moves 1 cm away from the end of the straw

3 = The circumference of the wing increases 2 cm

4 = The circumference of the wing decreases 2 cm

5 = The width of the wing increases 1 cm

6 = The width of the wing decreases 1 cm

Lethal changes:

A change which results in a wing falling off the end of straw, or in which the circumference of the wing is smaller than the circumference of the straw, etc. is lethal.

Survival of the birds:

You will release the birds with a gentle, overhand throw, making sure that you release the birds as uniformly as possible.

You will test each bird twice.

The most successful bird is the one which can fly the farthest.

The most successful bird is the sole parent of the next generation.

Use this spreadsheet to record your data.

Upload the photos of first generation surviving bird.

Make sure you record all your observations in the spreadsheet.

Take pictures of surviving birds of 5th generation, 10th generation, 15th generation and 20th generation. Use a reference object in all your pictures for an easy comparison. 

Upload your spreadsheet and pictures below.

Let's think about how natural selection operated in the bird flight activity. 

The purpose of this activity is to discover how the combination of mutations, natural selection, and environmental change generate progressively better-suited adaptations.

Purpose  
How do new species emerge?

Brainstorm
You know that many species that were alive in the past have gone extinct.  Many of the species that are alive today did not exist at one point in the past.  

There are 13 species of finch on the islands, but they are at once both so similar and so diverse that they have provided a fertile ground for exploring evolution since Darwin’s 1835 visit.  Darwin himself did not realize their role in explaining evolution until after ornithologists revealed the abundance of speciation to him.

The finches are proposed to have arrived on the volcanic islands from the South American mainland and are now considered part of the tanager family rather than the finch family.  There are four genera recognized in the group, and the species occupy overlapping but distinct ecological niches. In the genus Geospiza, there are six species.  In good times, they often eat the same foods, but in times of scarcity, each species has a specialized niche – large seeds, cactus fruits, etc. – on which they rely.  Their mating behaviors, such as times and songs, differ greatly, maintaining the distinct species.  The ecology of the different islands influences which species live on each island, and especially which species co-exist on an island.  Gene flow between islands occurs with occasional immigrants depending on storms and the distance between islands.

For several decades, scientists have gone to the Galapagos islands to study the physical characteristics of the finches there.  They recorded data on many traits including beak dimensions and weight.  In this lesson you will explore some of that data to understand the processes underlying speciation and adaptive radiation.

Below is a data analysis tool called CODAP created by educators at the Concord Consortium. Using this computational tool, you will be able to delve deeply into the finch data mentioned on the previous page. When the page loads you will see the basic finch dataset with columns for sex, weight (g), beak length (mm), beak depth (mm). In CODAP we are able to interact dynamically with the data, allowing us to make connections and draw conclusions. We will use this set to answer several questions about these Galapagos finches.

The scientist that have collected this data have done so for over 40 years now, the first bar graph that you saw in section one contains finch data from 1973 -1981. Lets see what we can find if we look deeper into the data.

In this data set, "Last Year" is the record of the last year an individual finch was seen by the researchers.  This typically means that the individual finch died during that year.

Use the methods you learned in the last activity to compare the finches that died during 1977 with the finches that survived 1977 and answer the questions below.

This CODAP frame has a much larger data set than those you have explored in the previous activities.  To help you gain a better understanding of the finches in the Galapagos, there are many more physical traits to explore.  Use the skills you developed in the previous activities to use CODAP to look at several traits and compare them across species and locations.

Not all of the traits were measured on each individual, so some traits will have more complete information than others.  We will focus on a trait that has a lot of data points, beak height.