Biological Automated Recombinase Architect

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Bara

Biological Automated Recombinase Architect

How It Works

  1. Collect Data: Extract genetic circuit data from iGEM and SynBioHub.
  2. Analyze Design: Feed design features into an AI model trained to recognize function.
  3. Predict Behavior: Receive a functional prediction of the genetic circuit.

Modeling Methods

Our model architecture uses ensemble learning techniques—like random forests and gradient-boosted trees—to identify relationships between genetic components and their resulting behaviors in synthetic circuits. These models are trained on curated datasets from iGEM and SynBioHub. We're currently exploring multi-layer prediction methods and integrating symbolic logic into neural networks to better model complex, hierarchical designs.

Prompt-Based Design Preview

Try entering a prompt like:

"Design a circuit that activates GFP only when arabinose is present and IPTG is absent."

Output: A NOT-AND gate activating GFP with araC and lacI regulatory logic (Preview Only).

How BARA Works

BARA analyzes genetic circuits using a combination of machine learning and curated biological datasets from sources like the iGEM Registry and SynBioHub. By transforming circuit designs into feature representations, the system will learn to classify and predict their functions using tools like ensemble models (Random Forests, XGBoost) and deep learning architectures.

But our ultimate goal goes further: we aim to create a generative system that can design entirely new genetic circuits based on desired behaviors. This means reversing the process — using insights gained from analysis to enable synthetic biology tools that recommend novel circuits to achieve specific logic or output.

This system could eventually empower synthetic biologists to go from "What do I want this cell to do?" to "Here's how to build it" — automatically.

Modeling Methods

BARA begins by parsing genetic circuits from open databases like the iGEM Registry and SynBioHub. Each circuit is broken down into standardized biological parts (e.g., promoters, RBS, CDS, terminators), which are then converted into computational feature sets.

Our early modeling pipeline will leverage interpretable machine learning tools like Random Forests and XGBoost to classify circuit behavior based on structure. These provide a foundation for understanding which circuit components influence specific functions.

As we scale, we plan to adopt Graph Neural Networks (GNNs) to represent circuits as directed graphs and capture more complex interactions between genetic elements. We are also exploring neuro-symbolic models to blend logical gate inference with deep learning.

Our long-term vision is to invert this pipeline — using learned models to generate circuits from desired behaviors , enabling fully AI-assisted synthetic biology design.