Using Loopy to Better Understand Disease Dynamics

According to the Center for Disease Control, 36.7 million people around the world were living with a complex retrovirus called Human Immunodeficiency Virus (HIV) in 2016. In 2018, four scientists from Institute for Systems Biology set out to better understand its infection dynamics. In this activity, you will be using Loopy to build models and understand relationships between components within this multifaceted system. You will be guided through creating a simple model to explore T cell dynamics during HIV infection. After mastering Loopy through this exercise, you will build your own HIV gene regulatory model in the second exercise.

Activity 1: Understanding T cell dynamics in Human Immunodeficiency Virus (HIV)

Background: The HIV virus is unique—it targets CD4+ T cells, the cells in the immune system that respond to viruses and get rid of them. T cells send out signals, telling additional T cells to come to the site of infection to help with this process. This response is effective and helpful when the body encounters most infections, but in the context of HIV infection, will the increased number of T cells in this area always be a good thing?

In this activity you will explore this phenomenon and use modeling to gain new insights. Thinking about a phenomenon (HIV infection) as a system helps us see how individual parts interact, and allows us to study complex scientific questions.

Note: If you did not yet read about feedback loops, you may find this background information useful as you work with these models. You can also explore the other background info about systems, models, and dynamic models.

1. Open Loopy: Search “loopy systems” on a browser, or go directly to Scroll down and choose to make a model from scratch.

NOTE: Each student or pair will need a computer or laptop. Loopy doesn’t work well on mobile devices. You may also find it useful to print out this page to refer to as Loopy works best in full screen mode.

2. Become familiar with the following vocabulary terms that you will see throughout this activity.

  • HIV: Human Immunodeficiency Virus. A virus transferred through bodily fluids which targets T cells to weaken the immune system until it eventually can no longer defend the body. Can lead to Acquired Immunodeficiency Syndrome (AIDS).

  • System: a collection of parts, modeled as nodes, and their interactions, modeled as edges.

  • Node: A part of a model of a system. Shown as a circle in Loopy. Can be influenced by inputs and outputs shown as edges.

  • Edge: A part of a model of a system. A line or arrow connecting nodes in Loopy, modeling a relationship between them.

  • T cells: a type of white blood cell. Destroys invading pathogens and signals to additional T cells to also target these invaders. HIV directly targets T cells. Therefore, an increased number of T cells means an increased number of targets for HIV.

  • Phenomenon: a fact or situation that is observed to exist or happen, especially one whose cause or explanation is unknown.

3. Begin building your model

3a. Delete the text label inside the nodes. To do this, double click on the node and edit the text on the right hand sidebar. Delete the text on the workspace by selecting the eraser icon and clicking on the text. Now there should be two blank nodes and two edges. Your workspace should look something like this:

Make sure your workspace looks like this

3b. Hit "play" and click the up arrow on the blue node to see how the model works. Think about what is actually happening:

    • What do the edges represent?

    • What does increasing or decreasing different nodes do to the system?

3c. Hit "stop", then click on the hand icon to move the nodes around on the screen.

3d. Move the nodes a little further apart, stretching the edges. **Note that the longer the edge, the slower the interaction**

3e. Rename the two nodes

    • Click in the text box of the blue node. Rename it “number of infections”.

    • Click in the text box of the purple node. Rename it “T cell response”.

    • Imagine a body that has some number of infections at the site at risk for contracting HIV.

3f. Click on the pencil icon. Very close to the blue node, create a new node by drawing a circle with your mouse. Name this new node “infectious/dirty environment”.

3g. Connect the “infectious/dirty environment” node (the node you just drew) and the “number of infections” node with a positive (+) input arrow running from the red node to the blue node.

3h. Click on the pencil icon again. Very close to the purple node, create a new node by drawing a circle with your mouse. Name this new node “chance of HIV contraction”.

3i. Connect the “chance of HIV contraction” node (the node you just drew) and the “T cell response” node with a positive (+) input arrow running from the purple bubble to the red node.

4. Check your model!

So far, your model should look something like this ----->

You can also view this model here:

NOTE: If you have trouble seeing the whole model, zoom out using your browser's controls, then REFRESH the page. The model should load so it is all visible. If it is still cut off, repeat the process.

Make sure your workspace looks like this

4a. Hit play to begin the simulation. Before you do anything else, slide the speed bar closer to the turtle to slow things down a bit (see image of speed bar below). The model may move too fast to understand what is happening, otherwise.

4b. Click the up-arrow once on the “infectious/dirty environment” red node and watch what happens to the nodes after the arrow passed through through three nodes). Stop the model after the arrow cycles back to the blue node.

5. Check your model!

Here’s a video of what you should see and when you should stop the model:


  1. Give an example of a node in your model

  2. Give an example of an edge in your model

  3. You may have noticed one edge is a positive (+) input arrow and one is a negative (-) input arrow. These edges have either a “more-more” relationship or a “more-less” relationship. What is the difference between these relationships?

  4. Explain the biological phenomena you observed. Example: "When there are (more / fewer) T cells present at the site of infection, the person’s chance of contracting HIV at that site (increases / decreases / is not affected)".

  5. Let’s think back to our question at the beginning. Scientifically and logically, why does an increased amount of T cells at the infection site mean an increased risk of contracting HIV? (Hint: the HIV virus prefers to infect T cells)

  6. a) What do you think will happen in the model after the first three actions (after it passes through at least 4 nodes)? Write a hypothesis.

Let’s test your hypothesis! Play the model from the start again (click the up-arrow once on the “infectious/dirty environment” red node) and watch the model longer than you did before, past the first cycle through the blue node for six actions. Observe the positive and negative effects on the nodes.

b) What actually happened in the model after the first three actions? Was your hypothesis correct or not?

Analyzing your results

Let’s think about your results from the "Reflection" section for a second. After completing question number 6, it seems that our model is no longer presenting an accurate depiction of the biological phenomenon we are trying to show. Our model is broken.

Let's review what happened.

When we begin using the model, the number of infections increases the T cell count. This is an accurate depiction of what would happen. The T cells decrease the number of infections and increase the chance of contracting HIV.

But, notice what happens after that.

The T cell count receives a negative signal; that node then decreases. Because of our “more-less” relationship, the number of infections increases. In the real world, this wouldn’t always happen, as an infection won’t come back over and over again as soon as it's been fought off.

Models will never be perfectly accurate.

The fact that this model broke doesn’t make it completely useless—it still has served its purpose of helping us visualize and better understand this phenomenon. The most important thing to think about when using models is your end goal. Does the model make logical sense based on the known information you’ve been given, and does it give you insight into the phenomenon you are researching? If not, you may need to rethink your model.

In addition, there are many other modeling programs that may serve your needs better.

Activity 2. Building an HIV gene regulatory model

Want to give modeling a try on your own? Here is an opportunity to take information provided to you and visualize what’s happening biologically.

Individually or with a partner, you will build a Loopy model showing how the regulatory genes of HIV affect the viral load and the body’s health. Below we have given you information on 6 regulatory genes of HIV. Your job is to take at least 3 of the provided genes and show how they affect two different nodes: the body’s health and the viral load.


  1. Use at least 3 genes from the table below (at least 1 must be the REV gene)

  2. Your goal is to model the effects on both the body's health and viral load

Some Helpful Hints:

  • If a gene affects something else, such as protein production, create a node to represent protein production. You will also need to create edges to model the relationship between them. You can connect genes to a factor that affects body health and/or viral load.

  • Genes might affect other genes.

  • Some genes may have a direct or indirect effect on the body’s health-- you may need to create an edge directly from a gene to the body’s heath node.

  • Some nodes you may want to draw are but not limited to: infectivity of HIV particle, virus Production, protein production, or speed of cell cycle.

  • Feel free to research and look for more information on individual genes! If you can incorporate outside information to help build your model, even better!

  • Nodes can have multiple edges.

  • Different edges can have negative or positive effects.

Once you've completed your model, you can check it by viewing the complete gene regulatory model.

NOTE: If you have trouble seeing the whole model, zoom out using your browser's controls, then REFRESH the page. The model should load so it is all visible. If it is still cut off, repeat the process.


  1. After completing your model, describe what happens with the increase or decrease of the genes you used. This does not need to be long, but it should describe what you think is important about the model.

  2. Remember an important question scientists ask when modeling: “Does the model make logical sense based on the known information you’ve been given and does it give insight into your question or phenomenon?” Answer that question in your own words.

Students, please take this 1-minute survey, now that you've completed this activity. We are interested in learning about your experience so we can improve these resources. All responses to this survey are anonymous, all questions are optional, and your feedback is much appreciated.

For educators: Access the teacher guide and key for lesson here


  1. Appay, V., Papagno, L., Spina, C. A., Hansasuta, P., King, A., Jones, L., . . . Rowland-Jones, S. L. (2002, April 01). Dynamics of T Cell Responses in HIV Infection. Retrieved from

  2. Boskey, E., & Olender, S. (n.d.). How Does One STD Infection Increase HIV Risk? Retrieved June 25, 2018, from

  3. German Advisory Committee Blood (Arbeitskreis Blut), Subgroup “Assessment of Pathogens Transmissible by Blood.” (2016). Human Immunodeficiency Virus (HIV). Transfusion Medicine and Hemotherapy, 43(3), 203–222.

  4. HIV genome: Genetic structure and function of HIV explained. (2010, August 2). Retrieved from

  5. Shelton, M. N., B.S. (2002). Hiv-1 Nef Secretion Modification Region Binds Cellular Proteins Involved in Exosomal Nef Secretion (Doctoral dissertation, University of Notre Dame).

HIV Modeling Curriculum Contributors and Supporters

Sarah Brossow: 2018 Baliga Lab High School Summer Intern
Annabelle Smith: 2018 Baliga Lab High School Summer Intern
Jessica Day: Systems Education Experiences Project Manager, ISB
Adrian Lopez Garcia de Lomana: Research Scientist, ISB
Martin Shelton: Post Doctoral Fellow, ISB
Christopher Lausted: Senior Research Engineer, ISB
Rachel Calder: Education Coordinator of SEE, ISB
Dexter Chapin: Teacher Scholar, Seattle Academy of Arts and Sciences
Claudia Ludwig: Director of Systems Education Experiences, ISB
Nitin Baliga: ISB SVP and Director, Founder of Systems Education Experiences

Funding to support the development of this lesson was provided by the Institute for Systems Biology Innovator Award (ISB Project #10520010000) and National Science Foundation Award DBI-1565166 . The content of these pages was created by students for students with the help of teachers and scientists. The views expressed herein are those of the authors and do not necessarily reflect the views of NSF or ISB.