Physarum Chemotaxis 2

CCW Cohort Teachers, click on the link below to access editable worksheets and presentation slides.

Lesson Overview


This activity builds on the first physarum chemotaxis experiment. This iteration of experiments is a process that scientists and cellular engineers engage in regularly. Using computational thinking, students look for patterns in data from the first experiment, generalize that pattern to from a hypothesis, design an experiment to test that hypothesis, and consider possible scenarios (or outcomes) of their experimental design. As students have already had an experience with physarum, this activity is more open-ended, leading to more creative questions and experiment design.

Big Idea(s)

Computational thinking can be used to design experiments

Scientists regularly iterate their experimental design (Developing new questions from completed experiments that drive the design of the next experiment)

Considering possible outcomes (scenarios) of an experiment can help determine if the experiment design will adequately test the hypothesis

Vocabulary words

Computational thinking


Experimental Design




  • Dissecting Scopes
  • Dissecting Forceps
  • Slime mold growing kit (Carolina Biological, item # 155775)
  • Physarum live culture
  • 10 agar petri dishes
  • Scalpel
  • Autoclave bag
  • Physarum sclerotium (dried physarum)
  • Instructions
  • Petri dishes
  • Tinfoil
  • Humidifying Chamber (box with damp sponge inside)
  • Filter paper
  • Pencils
  • An assortment of chemicals to test physarum chemotaxis (suggested below):
    • Glucose (5mM, 10mM, 100mM)
    • 5mM Sucrose
    • 5mM Lactose
    • Milk
    • 5mM Splenda
    • 5mM Stevia
    • 5mM NaCl
    • 5mM KCl
    • 5mM glutamine
    • White Vinegar
    • Apple Cider Vinegar
  • An assortment of food items to test physarum chemotaxis (suggested below):
    • Jam
    • Cookies
    • Fruit/dried fruit
    • Dried pasta
    • Crackers
    • Vegetables
    • Candy/chocolate
    • Chips
    • Hot sauce
    • Cheese
    • Meat (lunch meat)
    • Bread
  • Food dye
  • Single hole punch/biopsy punch (for making gradient gels)

Daly Ralson Resource Center:

Dissecting Scope (E069, E070, E071, E073, E074, E075, E075, E076, E077, E078, E079, E080, E082, E083, E084, E088)

Dissecting Forceps (E254, E304, E371)


Groups of 3-4 students


4 days

15 mins – Intro

15 mins – Experiment design

10 mins – 2 days – Experimental set-up

2 – 4 days – Data collection

30 mins – 1 hour – Preparing slides or poster presentation

1 hour – Presentations

Prerequisites for students

Students should have completed a chemotaxis experiment using physarum. They should have collected data and presented that data to the class on a poster or on slides. It’s helpful to have data from several groups in the class (or across several classes) so students have a larger understanding of physarum’s chemotaxic response to many different substances.

Learning goals/objectives for students
  • Use computational thinking to design a new experiment
  • Identify patterns in data sets, generalize that pattern, and use that generalization to form a new hypothesis
  • Apply previous experience to design a new chemotaxis experiment to test a new hypothesis
  • Demonstrate their understanding of experimental design and their hypothesis by drawing the hypothetical scenarios for at least one petri dish (or experimental set-up)
  • Develop students ability to collect evidence through observation.
  • Use evidence to support a scientific claim.
Content background for instructor

Although physarum’s common name is “slime mold”, it is not a mold. It is actually classified as another protist. Physarum spends much of its life in the plasmodium stage (large yellow, web like structure). It searches for food and feeds in this stage. Although it is very large, it is technically one cells (one cell membrane surrounding the entire organism, but many, many nuclei). If it dries out, it will form a sclerotium (dormant stage of physarum) and wait until more favorable moist conditions return. If food runs out, it will begin the reproductive stage of its life cycle and construct spores. The spores are carried by the wind. When spores find favorable conditions, spores germinate and release ameboid single-celled swarm cells. The swarm cells will fuse together and create another plasmodium to start the cycle over again.

Slime molds are another example of a very smart protist. They can solve mazes, the traveling salesman problem, and can even teach other slime molds what they have learned (see: They do all of these things without a brain or nervous system.

To keep your physarum in the plasmodium stage, make sure it has plenty of food, and humid conditions in which to grow. Also, physarum does not like light. Keep cultures covered to prevent sporing. Physarum are very curious and will quickly explore their petri dish in search of food. To avoid exploring the same area twice, it leaves a chemical trail. Therefore, don’t be surprised if your physarum gets bored of its petri dish after a few days and escapes.

To keep your physarum healthy can contained, take a new cutting every few days and place it in a new agar petri dish with a few sterile oats. Cuttings of a thich yellow vein of physarum are easily transferred (and used in all experiments for this activity). Cut a 0.5 cm x 0.5 cm cube of agar with a vein growing across it (physarum will quickly heal after cutting) and place the cube physarum side down on a clean agar petri dish.

While computational thinking is often associated with programming or computing, this thought process can be used for problem solving on most any topic. Scientist regularly use computational thinking to help design new experiments. Often experiments fail and scientist must use troubleshooting and analytical skills to assess why the experiment failed, design a new experiment, and try again (using the iterative “design, build, test” of engineering). Moreover, some hypotheses are difficult to prove with current methods. Scientist regularly design new experimental methods and consider possible outcomes, or scenarios, to determine if this new experimental design will be able to sufficiently test their hypothesis.

Getting ready

Collect the data from the previous physarum experiments and organize them on a single handout. You can also use the data provided by our own workshop as part of this collection of data. Don’t worry if some data on physarum chemotaxis contradict. This is common in actual science and may be a result of a difference in scientific method between groups, or might represent that physarum might not have a preference for a particular substance.

When ordering physarum from Carolina, they will ask for a delivery date. Make sure to have your physarum delivered 1 or 2 days before your activity. It is even advisable to have two cultures delivered, one 1 or 2 days before, and 1 the day of (just in case something goes wrong with the first culture). 1 culture is enough for about 20 students (5 groups).

Cut filter paper so that two strips lay flat, side-by-side, inside a petri dish. Be sure to prep all of your “chemotactic” solutions before hand in clean (or sterile) water. Place a few sterile oats in petri dishes at each station. Wipe down a set of tweezers with alcohol for each station.

Lesson Implementation/Outline


(15 mins)

Remind students of the previous experiments preformed on physarum. We will be preforming additional experiments to understand what drives physarum chemotaxis. And we will be using computational thinking. Essentially you will think like a programmer to design your next science experiment. We will:

  • Decompose the problem
  • Look for patterns
  • Generalize your pattern
  • Design an algorithm (experiment) using scenarios

The problem, or larger question is: How does chemotaxis work in physarum? This is a complicated problem that might take years, or decades to answer. We don’t have that time, but we can start smaller by decomposing this problem and asking:

What compounds or substances attract physarum?

What compounds or substances repel physarum?

Let’s look for patterns to start to develop a hypothesis to answer one (or both) of the above questions. Below is data from a previous workshop (feel free to use data from your own class’s experiments):

  • glucose <- sucrose
  • splenda <- sucrose & sucrose <- splenda
  • splenda <- stevia
  • milk <- lactose
  • sucrose <- water
  • white vinegar ? apple cider vinegar (died)
  • 5mM glucose -> 10mM glucose <- 100mM glucose
  • 100mM glucose <- 5mM glucose
  • sucrose <- NaCl
  • glucose <- lactose
  • milk <- apple cider vinegar
  • glucose <- splenda

(Note: the arrow indicated the direction of physarum chemotaxis. Data is presented out of order so students can develop their own pattern. If you organize it in a particular way, you may inadvertently hide other patterns. Allow students to analyze the data on their own, and let them know there is no real order to this list.)

Example experiment design:

  • splenda <- sucrose & sucrose <- splenda
  • splenda <- stevia

I focused on the above results because I see a pattern: Physarum preferred splenda to sucrose and stevia (Note that splenda and sucrose have conflicting results. Let the students know it’s ok to test something that might have conflicting results. Possibly something differed between those two experiments that accounts for that difference.).

My generalization of that pattern is: Splenda attracts physarum more than other sugars.

My hypothesis from that generalization is: I hypothesize that Splenda is a stronger chemoattractant than other sugars because it “tastes” sweeter than sugar (and maybe physarum’s chemotaxis sensors are similar to human taste buds).

Note that the hypothesis includes a reason for the guess (because it “tastes” sweater than sugar), which is important component of a hypothesis as it makes it an educated guess rather than just a guess.

Experiment design:

IF Splenda tastes sweeter than other sugars

THEN phsarum should be attracted to splenda even when Splenda is dilute compared to the sugar control.

Above is my experimental design. Note there might be many ways to test my hypothesis, but this is the one that I came up with. Just like there are many solutions to the programming challenges, there isn’t one right way to build an experiment.


I will then come up with all possible scenarios (or outcomes) for my experiment. Below are four possible scenarios. (Note there may be more, just as is true in programming using scenarios).


Scenario 1: Hypothesis confirmed

Scenario 2: Hypothesis disproven. Physarum will go towards the more concentrated solution

Scenario 3: Physarum prefers sucrose. I was wrong about physarum’s preference

Scenario 4: Unknown meaning or this result (and it’s fine to have a possible scenario that students are unable to interrept)

Note for students that this isn’t the data. These are what is possible, not what happened. All I am doing in the above example is demonstrating that for most cases, I know how I would interpret, or report, my results for the above scenarios. It shows that I am thinking about my experimental design.

Other experimental designs:

The students should have already tried an experiment on filter paper. If you would like to offer them other methods to test their hypotheses, here are a few suggestions.

Place bits of food on an agar petri dish. Physarum will chemotax towards food it likes to eat (like oats) and avoid food it doesn’t. See materials list for suggested food items.

Gradients of solutions. Students can create a solution gradient in agar by cutting a hole out of the agar and filling that hole with a solution (like 100 mM glucose). Make sure to mark that solution with dye so you can see where the solution has diffused inside of the gel. This set-up takes a few days. Every few hours the solution in the hole must be refilled to allow the gradient to grow in size. Students should keep this in mind when selecting this method.

***One important thing to stress with students is that, because every student group could have a very different experimental design, every group must be responsible for checking on their own experiment. Part of experimental design is identifying when you will make your observations. This must be scheduled by the group and they can’t rely on their instructor to remind them.***


Experiment Design (15 mins):

All groups are given 15 mins to come up with their own experiment. Have them check in with you about their ideas. Help guide them to an experiment that is feasible and that will answer their hypothesis. Be sure to keep in mind the materials you have available for the entire class so that one group does not use up resources that another group might need.

Experimental set-up (10 mins – 2 days):

Once all groups have finalized their experimental design, allow each group time to set up their experiment. Some groups will be done quickly (placing food on a petri dish takes little time at all). Some groups will need a few days to complete set up (the gradient takes a long time). Encourage groups that finish an experiment quickly (because of easier set-up) to preform more experiments after they finish the first. These experiments are best run in the background while student work on other tasks, so keep this in mind when planning this activity.

Checking for Student Understanding

It is a great idea to create an example poster to demonstrate how you would like data to be presented by each group. In this activity, students find the concept of scenarios the most confusing. Make sure they understand that this is not their data, or their experimental design, but is a way of showing that the will know what their data means once the experiment is done. 

Often students design experiments over many petri dishes that are far more complex than the example experiment given above. Drawing all the scenarios for their experimental design might be too many. Encourage them to pick one petri dish and draw the scenarios for only that set-up, or to include only the four most likely scenarios for their experimental set-up.


(1 hour)

Students should present the results of their experiment using a poster or slides. Use the following guidelines to guide students presentation:

For the presentation, each person will present one of the following:

  1. Pattern(s) identified, pattern generalization, hypothesis (2 minutes)
  2. Algorithm (experimental) design and scenarios (2 minutes)
  3. Present the data from the experiment (2 minutes)
  4. Conclusions and future directions (2 minutes)

Can present using slides or a poster.

Must present at front of the class (even if your group creates a poster).



Structure and Function

Matter and Energy in Organisms and Ecosystems

ETS1 Engineering Design

ETS2 Links Among Engineering, Technology, Science, and Society

Performance Expectations

HS-LS1-3 From Molecules to Organisms: Structures and Processes

(Physarum move toward food using chemotaxis, which uses positive feedback to move towards potential food or away from toxic substances.)

HS-LS1-6 From Molecules to Organisms: Structures and Processes

(As physarum attach and digest oats, it grows larger because it is coverting the sugar molecules into proteins and other larger molecules to increase its size.)

HS-ETS1-2 Engineering Design

(Students develop a new scientific question about physarum, by taking the larger question “How does chemotaxis work in physarum?” into a smaller, more manageable question.)

HS-ETS1-3 Engineering Design

(Students design a new experimental method, assessing this new method on criteria based on feasibility, reliability, and whether it can accurately answer their scientific question. Drawing out the possible scenarios for one petri dish in their designed experiment especially helps students to think through whether their design will answer their scientific question.)

Disciplinary Core Ideas

HS LS1.A Structure and Function

HS LS1.C Organization for Matter and Energy Flow in Organisms

ETS1.B Developing Possible Solutions

ETS1.C Optimizing the Design Solution

ETS2.A Interdependence of Science, Engineering, and Technology

(Scientists must often engineer new ways to answer scientific questions be designing new experimental methods. Therefore the scientific method and engineering method are linked.)

Science and Engineering Practices

Practice 1. Asking Questions and Defining Problems

Practice 3. Planning and Carrying Out Investigations

Practice 4. Analyzing and Interpreting Data

Practice 5. Using Mathematics and Computational Thinking

Practice 6. Constructing Explanations and Designing Solutions

Practice 7. Engaging in Argument from Evidence

Practice 8. Obtaining, Evaluating, and Communicating Information

Cross-Cutting Concepts


Cause and Effect

Stability and Change