When we immerse a player in Petri Dish, we want them to build a mental model of how the world works by interacting with it. The player does A, B happens, and the player tries to make sense out of why A resulted in B. Making sense out of those rules is going to be a lot easier if they are simple, logical, and intuitive rather than arbitrary or convoluted. Of course, the rules also have to be grounded in science.
The rules governing Petri Dish are grounded in cell biology, which are then grounded in chemistry, which are then grounded in the particle theory of matter. By grounding everything in the particle theory of matter, we are accomplishing two things. First, we are removing layers of abstraction for the player. Second, we are building everything on a common foundation, making it far easier for the player to understand how different systems and science concepts interact.
Most people learn diffusion as the flow of particles from areas of high concentration to areas of low concentration. While this may feel intuitive because most of us have quite a bit of firsthand experience with diffusion, it is fairly abstract. We have no idea why or how the particles do this, and when we need to apply diffusion in a problem-solving situation, equations appear out of nowhere and we are suddenly on shaky ground.
If we start with a simple random walk model instead, then diffusion is something that we observe arising out of the random motion of particles. We can see how the concentration of the particles and the step size in our random walk model affect diffusion rates. We can throw in a semipermeable membrane by adding a barrier that allows particles to pass through it based on coin flips or dice rolls. The why and how of diffusion are explained with easy-to-understand and local mechanisms of particle motion, and we can derive our own equations.
Want to include passive transport with protein tunnels or active transport with transport proteins? Easy enough. Plant hot zones in your barrier that capture particles and “fire” when a full set of particles have been captured. We don’t explain how proteins work in Petri Dish, so we are not attempting to eliminate all abstraction. But we are trying to sit closer to the ground where a player’s understanding is robust enough for them to apply and extend what they know. An enzyme is just another kind of hot spot that also captures particles and fires when a full set of particles have been captured. The player is constructing one unified mental model instead of jumping back-and-forth between a diffusion silo and an enzyme silo. This engenders a confidence in the mental model and encourages the player to apply and revise it more often.
Grounding everything in the particle theory of matter is not simply a veneer that we present to the player. The cell model in Petri Dish doesn’t have separate algorithms that kick in when the player starts investigating ecosystems and population dynamics. Those behaviors and relationships all arise from a basic cell model where a cell has a cell membrane, a metabolic network for building molecules, a genome that encodes proteins that transport molecules and catalyze chemical reactions, and a control system for activating genes. Part of the wonder in playing Petri Dish comes from seeing how much complex behavior can rise out of and ultimately be explained by such a simple set of basic mechanisms.