Cell Transport

• Insulin Secretion and Membrane Transport Model

• Osmosis and Water Potential Model

• Surface Area per Volume Simulation

• Diffusion of a Spherical Cell Model

Ecology

• Algal Blooms and Community Ecology

• Carbon Budget and System Dynamics

• Carbon Flux and System Dynamics

• Exponential Growth Model

• Extinction Vortex

• Limiting Nutrients with Data Analysis

• Logistic Growth Model and the Butterfly Effect

• Phosphorus in a Lake: Changes in a Dynamic System

• Population Dynamics of the White-Footed Mouse

• Predator-Prey Simulation of the Lotka-Volterra Model

• Strange Attractor of the Lorenz Model

• Aphid-Soybean Dynamics

Enzymes

• Enzyme Diversity

• Lactase Enzyme Simulation with Data Analysis

• Lactase with Variable Sample Sizes

• Testing Unknown Enzymes

Evolution

• Evolution of Antibiotic Resistance

• Evolution of Populations: Genetic Drift, Natural Selection, and Mutations

• Evolving Bits

• Ground Finch Adaptations

• Guppy Evolution: Natural Selection, Sexual Selection, and Genetic Drift

• Natural Selection in Blue Mussels: LAP gene

Genetics

• Drosophila Genetics Model - Apterous vs. Wild Type

• Drosophila Genetics Model - White-Eyed vs. Wild Type

• Gene Linkage, Recombination, and Independent Assortment

• Patterns of Inheritance with Gel Electrophoresis Analysis

• Simple Genetics Simulation

Metabolism

• Cellular Respiration Accounting Model

• Cellular Respiration - Open Inquiry Model

• Photosynthesis Model

• Photosynthesis Model with Variable Sample Sizes

Molecular Biology

• Genotyping with Gel Electrophoresis

• Lac Operon: an Inducible System

• Lac Operon with Diauxic Growth

• Transformation Experiment

• Transformation Experiment with Multiple Controls

• Trp Operon: a Repressible System

Physics

• Bathtub Dynamics

• osition, Velocity, and Acceleration

• Mechanical Energy

• Understanding Linear Motion

• Uniform Circular Motion

• Universal Gravitation and the Three-Body Problem

Physiology

• Body Temperature Regulation Simulation

• COVID-19 SIR Simulation

• Cardiovascular Simulation - Cardiac Cycle, Conduction, and Output Models

• Fruit Fly Choice Chamber

• HPV and Adaptive Immunity

• HPV and Influenza Immunity

• Neurophysiology and Information Processing

• Neurophysiology and Information Processing 2022

• Neurophysiology and Neurotoxins

• Oxytocin and Uterine Contractions Feedback System

• Relative Positions

• Thyroid Hormone Feedback System (set point)

• Perception Models

Timing and Coordination

• Cell Cycle Simulation - 2020

• Growth Factors, Cyclins, and Cancer

• Circadian Rhythm Simulation - Biological Clock Model

• Phenology of the Endangered Karner Blue Butterfly

Google Folder of Activity Sheets

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Computational models are thought experiments run on a computer. Computational modeling is a powerful tool for learning a system's dynamics. In a computational model, the parts are not simply linked by variable x affecting variable y. Rather, variables are linked operationally/mathematically. For example, y = 0.2x. 

Using computational models allows users to interact with the quantities of a system, perturb the system, and use the model to understand the dynamics. Many behaviors of common biological and ecological systems are complex. Representing those systems with algebra is extremely daunting for high school students. However, quantitative modeling allows students to interact with complex mathematical relationships without needing to understand all the math. More importantly, students can manipulate the models and easily discover complex properties of many biological and ecological systems.

For example, computational models allow students to manipulate systems that have:

• Positive and negative feedback loops

• Thresholds and tipping points

• Robustness and sensitivity

Additionally, computational models are ideal for representing biological concepts because they can be:

• Iterative

• Change over time (dynamic)

• Linear or non-linear

• Deterministic or random

• Predictable or chaotic

Students can explore these ideas with computational models. They don't need to know the math to become a computational thinker. They do have to understand feedback loops, thresholds, and other important behaviors of systems. Our world is complex, and students need methods to experiment with these complex behaviors. Computational models help students understand those concepts because students can manipulate quantities and watch how that new quantity affects the system.

To better assist you here are a few things to keep in mind:

1. The simulations may take 10 seconds to load depending on the speed of your connection.

2. Some simulations take longer depending on the mathematical relationships. Be patient. Real experiments take much longer.

3. For help navigating through the simulations and graphs see THIS HELP GUIDE.

The goal of my computational models is to make you better critical thinkers. I want you to manipulate variables, examine data, and draw your own conclusions.

I use these computational models to teach scientific practices throughout my curricula. I have found that simulations are a powerful method of teaching the Scientific Practices identified by AP science courses, and the Next Generation Science Standards (NGSS).