Design a DNA-sequence optimization loop

Q: Design a DNA-sequence optimization loop

This question evaluates a candidate's ability to design an end-to-end ML-driven experimental optimization loop for DNA sequence engineering, including sequence representation, constraint enforcement, candidate generation, batch experimental design, and learning from noisy assay measurements.

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Question

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You are building an ML-driven platform to optimize DNA sequences (e.g., a promoter/enhancer/codon-optimized gene) for a target lab-measured property (e.g., expression level, binding strength, stability).

You have:

A robotic wet-lab that can synthesize/run an assay on a batch of candidate sequences per day.
Historical data: (sequence, assay_result, metadata) where assay results are noisy and may vary by batch.
A sequence model (could be a Transformer/LLM-style model) that can generate or score sequences.
Hard constraints (examples): GC content range, forbidden motifs, max homopolymer length, sequence length bounds.

Design an end-to-end system that repeatedly proposes sequences, runs experiments, and learns from results.

Address:

How you represent sequences and incorporate constraints.
How you generate candidate sequences (search / Bayesian optimization / evolutionary / RL / LLM prompting, etc.).
How you balance exploration vs. exploitation and handle noisy measurements.
How you choose a batch of sequences each round (not just one).
How you evaluate progress and decide when to stop.
Key failure modes (mode collapse, assay drift, data leakage, overfitting to simulator/predictor) and mitigations.
What you would log/monitor in production.

Design a DNA-sequence optimization loop

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