Brahma

Revolutionizing Preclinical Research with Synthetic Mice

Comment

Inspiration

The inspiration for BRAHMA came from the critical inefficiencies and ethical dilemmas in modern preclinical research. Animal testing is not only expensive and slow but also raises significant ethical concerns. We saw that researchers and AI agents lacked a scalable, ethical platform to run rapid, realistic simulations. BRAHMA, named after the creator god, was conceived to fill this void by becoming the ultimate creator of synthetic biological data, forming a new triad for research: creation (BRAHMA), preservation of data integrity, and the destruction of outdated, inefficient methods.

What it does

BRAHMA is a platform for the on-demand generation of genetically accurate synthetic mice. It creates a complete digital twin by linking synthetic genomes, predicted phenotypes, and simulated behaviors. This enables researchers to run instant, large-scale in-silico experiments for critical applications like drug screening, genetic studies, and AI model training, all without touching a single live animal.

How we built it

We built BRAHMA using a multi-layered computational biology and AI architecture. The core system integrates:

  • Genome Generation Engine: Uses established genetic models and randomization to create synthetic mouse genomes complete with SNPs, copy-number variations (CNVs), and specific knockouts.
  • Phenotype Prediction Model: A machine learning model trained on public biological datasets to predict physical traits (like obesity or tumor growth) from a given genetic profile.
  • Behavior Simulation Module: An agent-based simulation that models mouse behavior in standard research environments like open-field tests and mazes, based on the underlying phenotype.
  • Data Export Pipeline: Packages all outputs into standardized, researcher-ready formats including FASTQ files for sequencing data, VCF files for variants, and CSVs for phenotype and behavior logs.

Challenges we ran into

One of the biggest challenges was accurately modeling the complex, non-linear relationships between genotypes and phenotypes. Integrating disparate biological data sources into a cohesive and realistic simulation pipeline also required significant effort. Furthermore, ensuring that our behavior simulations were both computationally efficient and biologically plausible pushed us to innovate in our agent-based modeling approach.

Accomplishments that we're proud of

We are incredibly proud of building a fully functional, end-to-end platform that goes from a genetic specification to a rich, multi-modal dataset. We successfully created a system that can generate a synthetic mouse with a linked genome, phenotype, and behavior in minutes. Most importantly, we built a tool that has the genuine potential to save millions of dollars in research funding and countless animal lives, accelerating scientific discovery in a more ethical way.

What we learned

Throughout this project, we deepened our understanding of computational genetics and the nuances of phenotype prediction. We learned how to better integrate different AI models to work in concert, and we gained a profound appreciation for the complexities and bottlenecks of real-world biological research. This project was a massive lesson in interdisciplinary collaboration, blending biology, computer science, and ethics.

What's next for BRAHMA

Our vision is to democratize access to preclinical testing and transform how science is done. Next steps include:

  • Expanding the Genetic Library: Adding more complex genetic models and disease-specific profiles.
  • Enhancing Simulation Fidelity: Incorporating more environmental factors and social behaviors into our simulations.
  • API Development: Creating a public API to allow researchers and AI agents to programmatically generate and access synthetic data at scale.
  • Validation Studies: Partnering with research institutions to validate our synthetic data against real-world experimental results, further cementing BRAHMA's role as a powerful tool for the scientific community.

Built With

Share this project:

Updates