Fifteen years old. Working from Iran. Building a CUDA simulation that packs 34,359,738,368 base pairs of DNA onto a single RTX 3060.
That’s the setup behind ZeroSenophage, a project shared on Reddit by a young prompt engineer who goes by Zero. The build took about a month, running in close collaboration with Gemini 3.1 Pro. It leans on 85 peer-reviewed studies to back up the biology. I read through the whole breakdown and got hooked fast.
Most in-silico biology work like this comes out of a funded lab with a research team behind it. Zero built this alone, on a consumer GPU, using roughly 11 million tokens of back-and-forth with an AI model instead of a grant. A few commenters called the writeup dense and hard to parse. Fair, but the substance underneath is worth pushing through the noise for.
Here’s what the project actually does
ZeroSenophage simulates a custom immune cell, an engineered senolytic macrophage, hunting down senescent cells inside a virtual blood plasma world. The problem it’s solving is a real one in aging research. Senescent cells stick around, cause inflammation, and normal immune cells struggle to clear them fast enough.
The architecture stack is the part that makes this more than a toy demo:
- Async CUDA C++ streams with lock-free buffering for the raw compute
- A Lattice Boltzmann fluid model to simulate plasma hydrodynamics
- Michaelis-Menten kinetics driving how the cells “decide” what to do
- 2-bit genomic bit-packing, which is how 34 billion base pairs fit into 8GB of VRAM
- Direct3D phase-contrast shaders so the output actually looks like a microscope feed
The world holds 150 senescent cells, 150 senophages, and 30 backup scavenger cells, spread across 10,485,760 voxels. Senescent cells leak a chemical signal called CCL2, and the senophages track it like a scent trail. Once they attach, they digest the target, convert the leftover fat and tissue into ATP energy, and go hunting again. Senescent cells fight back with a CD47 “don’t eat me” shield, but Zero’s senophages are engineered blind to it. The receptor that normally reads CD47 gets its weight set to zero on purpose.
Now here’s the twist nobody saw coming
Early in the run, the CD47 shield kept senophages confused and slow, and progress crawled. Once the neural network reinforced itself, attacks got faster and more aggressive. The shield’s effectiveness dropped by a fraction of a percent, and everything cascaded from there. More kills meant more ATP, and more ATP fueled the PARP1 repair gene: DNA repair rates jumped up to 8x normal. Cancer never showed up in any run, and it’s not because mutations stopped. It’s because the senophages ate senescent cells faster than the cells could mutate into something dangerous. The predators ended up acting as a cancer clock, and nobody coded that in!
If you want to poke around the project yourself, here’s the fast path:
- 🧬 Open the GitHub repo and skim the source tree for the architecture breakdown
- 🔬 Check the 85 linked studies (DOI and PubMed links) behind each biological rule, from CD47 shielding to PARP1 repair
- 📊 Pull the 89-column CSV telemetry log to watch ATP, mutation counts, and CD47 levels move over a full run
- 🖥️ Look at the raymarched microscope output to see what the plasma environment actually looks like in motion
A few things worth stealing for your own build. Bit-packing your data structures buys you real scale before you ever touch a bigger GPU. That’s the reason this runs on a 3060 instead of a data center card. Logging everything, all 89 columns, is what let Zero catch the emergent cancer-suppression effect instead of losing it in the noise. And letting an agent make an “unrealistic” design call often pays off. Here, that meant senophages built to flat-out ignore a biological shield.
Zero is upfront about the limits too. Mutation and full-blown cancer haven’t appeared in any run yet, but the mechanics allow for it. It’s flagged as a real possibility in future versions. Overfed senophages can burn out their cholesterol pump and die from the cleanup work itself. There’s no wet-lab validation here either, this is code checked against published biology, not a petri dish. Zero says so directly instead of overselling the result, which is rarer than it should be in this kind of post.
If cell biology built like a living simulation sounds like your thing, the full writeup and code are worth the visit. Go dig through the repo, read the source studies, and tell Zero what breaks it first. 🏴☠
My 1-month in-silico project, ZeroSenophage, which is built with a C++ CUDA and Differential Equations architecture and processes 34,359,738,368 Base Pairs using a bit-packing architecture. A project that was built using 11 million tokens and collaboration with Gemini 3.1 Pro.
by u/ze707ro in PromptEngineering