FOR YOUR NOTEBOOK:

What Is a Scientific Model?

A scientific model is a representation of an object, system, process, or idea that helps scientists understand, explain, predict, or communicate how something works.

Models are useful because many biological systems are:

• Too small to see

• Too large to observe all at once

• Too complex to study directly

• Changing over long periods of time

• Difficult or impossible to experiment with directly

Scientists often use different types of models to study the same biological system.

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The Four Types of Models

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Physical Model:   3-D objects or materials   | Show structure or relationships

Mathematical Model:  Numbers, equations, graphs  |  Calculate and predict

Conceptual Model:  Words, diagrams, arrows, ideas  |  Explain relationships

Simulation Model:  Computer programs/data  |  Mimic a system over time

1. Physical Models

A physical model is a three-dimensional representation of a biological object or system.

It can be something that students or scientists can see, touch, and manipulate.

Examples in Biology

• Model of a cell

• DNA model made from beads or pipe cleaners

• Model of the human heart

• 3-D model of a protein

• Model of the digestive system

• Plastic model of a skeleton

• 3-D model of an ecosystem

Advantages

Physical models can:

• Make structures easier to visualize

• Show the shape and arrangement of parts

• Allow people to manipulate structures

• Help explain structures that are too small to see

Limitations

Physical models:

• Are often simplified

• May not accurately represent scale

• Usually cannot show complicated changes over time

• May leave out important variables

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Physical Model Example:  Draw a model of good behavior in a student. 

2. Mathematical Models

A mathematical model uses numbers, equations, formulas, graphs, or mathematical relationships to represent a biological system.

Mathematical models are especially useful for making calculations and predictions.

Examples in Biology

• Population growth equations

• Hardy-Weinberg equations

• Predator-prey equations

• pH calculations

• Enzyme reaction rates

• Genetic probability

• Disease-spread calculations

• Carrying capacity calculations

Advantages

Mathematical models can:

• Make precise calculations

• Identify patterns

• Make predictions

• Test relationships between variables

• Handle large amounts of data

Limitations

Mathematical models:

• Depend on the assumptions used

• May leave out important variables

• Can be difficult to understand

• May not perfectly represent real biological systems

Mathematical Model Example: Population Growth Over Time

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3. Conceptual Models

Definition

A conceptual model uses ideas, words, diagrams, arrows, and relationships to explain how parts of a system interact.

Conceptual models focus on relationships rather than exact numbers.

Examples in Biology

• Food webs

• Carbon cycle diagrams

• Photosynthesis diagrams

• Flowcharts

• Cell signaling diagrams

• Ecosystem diagrams

• Diagrams showing natural selection

• Diagrams showing energy flow

Advantages

Conceptual models can:

• Simplify complicated systems

• Show relationships between parts

• Help explain cause and effect

• Make complex ideas easier to communicate

• Show how one part of a system affects another

Limitations

Conceptual models:

• May not include exact numbers

• Can oversimplify systems

• May not show every variable

• Can be interpreted differently by different people

Conceptual Model Example: Population Growth

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4. Simulation Models

Definition

A simulation model uses a computer or digital system to imitate a real biological system.

Simulations allow scientists to manipulate variables and observe what might happen.

Examples in Biology

• Population simulations

• Climate/ecosystem simulations

• Disease-spread simulations

• Predator-prey simulations

• Evolution simulations

• Genetic inheritance simulations

• Ecosystem simulations

• Cellular simulations

Advantages

Simulations can:

• Model systems over long periods of time

• Change many variables

• Test scenarios that would be difficult in real life

• Run experiments repeatedly

• Make predictions

• Model systems that are too dangerous, expensive, or slow to study directly

Limitations

Simulations:

• Depend on the information programmed into them

• May contain assumptions or errors

• Cannot perfectly reproduce nature

• Can sometimes be difficult to interpret

• Require computers/software

Simulation Model Example: Covid Spread

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The Big Idea:

Scientists choose models based on what they are trying to understand, explain, or predict.

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