Why and How to Use Computational Models in Biology

The Next Generation Science Standards (NGSS) emphasize modeling both as a Science and Engineering Practice and as a Crosscutting Concept. Biology lends itself to a systems approach, and there are a lot of opportunities for students to model systems. Students are going to be most familiar with things like physical models, diagrams, and flowcharts, but computational models are a natural extension.

Why Use Computational Models in Biology?

There are a lot of instructional advantages to using and developing computational models in a biology classroom. Models allow students to try out “what if” scenarios, seeing how changes in an input variable can affect the system, so this really helps them discover material for themselves. Creating a model also helps students develop a much deeper understanding of the inter-relationships within a system. It will help spark students’ own questions that will lead to further investigations.

Using Computational Models to Teach the Carbon Cycle 

There are many examples where you can use computational models in the biology curriculum. One example would be the carbon cycle and tracing the flow of carbon between reservoirs. These models are really useful for looking at food chains and predator-prey relationships.

You can get students started by considering how animals might choose where to spend their time. The first thing needed is a reward. Start by setting up two different habitats. One has eight rewards; the other has two. Call 10 students up to the front of the class and tell them to choose which habitat they want to go forage in. What you’ll see is that the students are going to distribute themselves exactly proportional to the amount of reward there. So if the ratio between the two habitats is eight rewards to two rewards, you are going to have eight students to two students, and your students are going to see this. 

Then switch it up into another round where the ratio is six to four rewards. Ask the students to develop a model for this relationship. They’ll find that they can easily model that the proportion of reward is equal to the proportion of foragers. Ask them to predict what will happen if you change these proportions.

The other great thing with models like this one is that you can then ask students how they are realistic and how they are not realistic to get them thinking about other questions. They may want to know what would happen if a predator were involved. It really gets students thinking and guiding their own inquiry.

Using Computational Models With Food Chains and Food Webs 

Food chains and food webs are another great place for students to get an entry into computational modeling. If we think about the most basic example, we can use the rule of 10: the idea that only 10 percent of the energy passes from one trophic level to the next. 

If we think about sea grass and sea cows, there should be 10 times more biomass of sea grass than of sea cows. You could get the students to create a mathematical model that represents this then ask them how much biomass we should have of the predator of sea cows: tiger sharks.

This is really just a starting point. You then want to get the students to think, “How can we make this more realistic, because there’s nowhere in the world where we just have sea grass, sea cows, and tiger sharks?” The world is much more complex, so one add-on is to have students make the food web more realistic. The more realism you add shows students that you can go from very simple models to very complicated ones in a way that’s not that challenging. This is a great example of using the NGSS style to get students to think about the material and try to discover it themselves.

The Role of Teachers

Teachers play a really critical role in effectively using computational models in the classroom. The models are really great because the students discover things for themselves. You want to ask really deep and penetrating questions focusing on the overarching question of: “How does this model represent the world, and what are its limitations?”

Models aren’t perfect, and the limitations are often where we learn the most from them, so have students think about what they would add to a model to make it more realistic. As they think about that, they’re going to have to really understand how the system they’re studying is working. 

By using these models effectively to guide investigations that the students make on their own, you’re going to reinforce the learning you want.

There’s no question that computational models can be intimidating to students. But if you play games, make the math come alive, and start simple, you can really engage your students and help them thrive in the biology classroom. 

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Learn more about HMH Science Dimensions, which is designed to address the Next Generation Science Standards.

*This blog post was based on a Professional Development video found within HMH Science Dimensions.