Bioreactor Lab: A Cultivated Meat Simulation
I’m sure that I am not the only person who knows what a GIZMO is. You know, those lab simulations that your science teachers make you do. They always come along with a worksheet (or what one of my teachers likes to call a “Learning Facilitation Device”). Personally, my biology teacher has assigned me two of these GIZMOs in the past week. Anyways, as a small project, I decided to create a GIZMO-style simulation of my main focus area which is the future of food specifically regarding cultivating meat in bioreactors. But first of all what the heck is a bioreactor and more importantly, what the heck is cultivated meat?
Brief overview of important concepts
So cultivated meat is essentially meat grown in vitro or outside of an animal and in a lab. I know what you are probably thinking: “Ewww meat grown in labs, I would never eat that” and I feel for you but the reality is that current meat production is unsustainable. The population is growing and meat production is a huge contributor to greenhouse gases worldwide. Cultivated meat provides a more sustainable alternative for the future, but there are still some challenges. To find out more you can read my previous article. Now on to bioreactors. Bioreactors are the vessel in which cultivated meat is grown and they are basically these huge steel vats that house a nutrient rich medium that allows meat cells to grow and develop. They are a very interesting piece of tech and can be found across many disciplines notably in pharmaceuticals. You can also read more about them in my previous articles. Anyways, enough self-advertising, lets get on to how I built a simulation surrounding the process of making cultivated meat.
The Process
I started with some idea generation because I wanted to build something around future food technologies. Some of my earlier ideas surrounded physical experiments such as a cultivated meat scaffold model or optimizing fermentation but I ended up deciding to build a simulation instead. In order to build this simulation , I used ChatGPT and Claude to help build an HTML-based simulation of animal cells growing inside of a bioreactor. The initial model was mainly focused on cell population growth but I ended up introducing environmental controls for nutrients, oxygen, temperature and other variables later on. I also added animations, statistics, and graphs showing how the population of animal cells changed over time. Over time, I began to realize that I didn’t want the simulation to only focus on the division of animal cells because that was quite boring so I decided to shift the simulation towards the actual processes in making cultivated meat. I used additional prompts to Claude to introduce new features such as batch-based production, a cell inspector that allows you to zoom in on cells dividing, and scaffold and microcarrier development among others. With that I want to dive into the different features of the simulation a bit more.
The Simulation
The simulation focuses on 5 main concepts
- cell proliferation through mitosis
- nutrient and oxygen consumption
- waste accumulation and removal
- cell differentiation from stem cells into muscle cells
- scaffold attachment and tissue maturation
In terms of the features that express these concepts, the central feature of the simulation is an animated bioreactor containing muscle precursor cells. “But what does this look like?” you might ask. Well, it includes an animated liquid environment with cells floating about and a mechanical impeller (stirrer) that mixes the contents. You can see oxygen bubbles entering the liquid as well as nutrient and oxygen supply lines that go into the bioreactor and a waste removal line going out of the bioreactor. The cells can also visually multiply and they change (grow and differentiate) as the simulation progresses. There are also microcarrier beads that appear during the scaffold stage. Users can zoom into the bioreactor, pan around the environment, and select individual cells to inspect them dividing.
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Users can also manipulate four variables that can influence the growth rate of the cells.
- Nutrients — Rate at which fresh nutrients are supplied
- Oxygen — Rate at which oxygen enters the bioreactor
- Temperature — Temperature of the growth environment
- Waste production — Rate at which the cells produce waste
Users can also pause or reset the simulation, change the speed, or replace the growth medium to restore nutrients and remove waste.
One of the most important additions to the simulation was transforming the simulation from an endless growth process into a batch-based production cycle. Users can choose batch durations of 14, 21, 28, 42, or 56 days with the default being 28 days or four weeks and the process is divided into these 5 stages:
- Cell inoculation — Muscle precursor cells are introduced to the bioreactor
- Cell expansion — Cells multiply through mitosis
- Scaffold/Microcarrier Phase — Microcarrier beads are introduced, providing surfaces for the cells to attach to
- Differentiation — Some precursor cells develop into specialized muscle cells and begin to form structures resembling muscle
- Tissue maturation and harvest — Differentiated cells become more organized developing into more tissue like structures before harvest.
In addition to all of these features, there are also live statistics and graphs and track several different variables in real time and give an overview at the end of the experiment. This allows users to conduct multiple simulations and compare their results. Users can also export their results as a CSV file, allowing them to analyze the data outside the simulation.
Behind the animations is a simplified mathematical model.
Rather than randomly generating cell growth, the simulation calculates population changes based on environmental conditions.
The basic growth equation is:
growth = max rate × nutrients × oxygen × low waste × temperature × free space
Each environmental factor contributes a score between 0 and 1. If one condition becomes unfavorable, the overall growth rate decreases.
The model also accounts for cell death, which increases when cells experience nutrient shortages, insufficient oxygen, excessive waste, or high temperatures.
Limitations
Although the simulation includes a variety of important biological concepts and processes, it is just a simplified mathematical representation of the cultivated meat process. The numerical values and the timelines here are based on truths but largely illustrative and not really experimentally validated. Real bioreactors are incredibly complex machines and they include an even greater variety of variables that need to be carefully measured and monitored. Nevertheless, it provides an interactive way to explore how environmental conditions contribute to the challenge of producing cultivated meat. Here is a video of a walkthrough of the simulation. I hope you learned something from this article and you can try out the simulation yourself here
Hey, I’m Nathan Huang a grade 10 student at the University of Toronto Schools in Ontario. I’m really passionate about food and I’m currently looking into innovative forms of food production as well as sustainability. To me, food seems to be something that humanity has both greatly appreciated, but also taken for granted, especially in recent times. With the increase in population over the next two decades, food insecurity around the world will only get worse and I want to help take the steps towards combating it. If you’d like to get in contact with me, you can send me an email at nathanhuang2020@gmail.com. Hope you enjoyed. Cheers!
