What can modeling do for you?

In the blog so far, we've talked often about computational models - how they're made, what can go wrong, and what they could be like in the future. But what exactly are they - and why should anyone (especially biologists) care?

A model is a representation, or imitation, of a system that is difficult to examine directly. Biologists already use models all the time. For example, we'd like to understand biological process in humans, but since most of us can't experiment on humans, model organisms and cell lines are used instead. "Model" also refers a working hypothesis about how a system functions, which is often presented as a cartoon diagram.

A cartoon model for how the Shc1 adaptor protein acts at different stages of signaling.

Zheng et al., Nature 2013

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Like a cartoon model, a computational model is created based on what a person knows, or on what they hypothesize. The difference is that instead of drawing a picture, which tends to be vague and qualitative, they make concrete and quantitative statements about how molecules interact. They then use this information to create a set of equations or a computer program, which is used to simulate system behavior. In modeling of chemical kinetics, the goal of simulation is often to see how certain outputs (like protein concentration) change over time, or under different conditions. So, like a model organism, a computational model can be used to make new discoveries with potential relevance to real-world questions.

Here are some of the reasons why I think biologists can get excited about what models can offer:

1. A roadmap for pursuing experiments. Why do we do experiments? Often, it's to test a hypothesis. The more complicated the hypothesis, the greater number of experiments one could try, and the more involved each experiment might be. At the same time, even the most diligent of us want to optimize, and do minimum work for maximum information. Models can potentially help identify which tests would be most meaningful for supporting or disproving a hypothesis. 
2. A way to make sense of complicated or conflicting data. Sometimes it turns out that two seemingly contradictory ideas are actually compatible if you think about the quantitative details, which is exactly what models are good for. 
3. Consolidating and testing knowledge about a system. A typical experimental study provides information about one or a few interactions. Models can help us put together multiple pieces of information, like assembling a jigsaw puzzle, to form a more complete picture. Furthermore, by simulating such a model and comparing it to experimental data, interesting discrepancies can sometimes be identified. In other words, we can see whether we have enough puzzle pieces, or if we need to find more through additional experiments. 

At the same time, we need to remember that models won't magically provide the answers to everything. A model that simply recapitulates your expectations could be appealing; however, a model's real worth is in its ability to generate non-obvious, testable predictions. If you're going to start modeling or are thinking about starting a modeling collaboration, try to first learn about about what models can and can't do.

So what does the word "model" mean to you? And what do you think they can be used for?