Name the core abstraction and its failure modes.
Maple DNA Course
DNA as an information system
A rigorous introduction to bases, symbols, constraints, and why DNA computing starts with representation.
Learning Objectives
What this lesson should make precise
Translate the concept into a Maple/Hermon proposal contract.
Define at least one evaluation case that can fail the model safely.
Tutorial Flow
How this lesson becomes a demo and training target
Each tutorial is written as a user education path and a model-improvement artifact. The diagram shows how the idea moves into a lab, a typed contract, an eval gate, and then a Hermon/MapleAI demo route.
Concept
Alphabet and symbols
Applied Lab
Applied lab: DNA as an information system
Output Contract
sequence, encoding_map, lint_checks, safety_class
Eval Gate
Does the answer separate model proposal from deterministic execution?
Demo Route
Maple DNA training and public model checks
01
Alphabet and symbols
The computational alphabet is A, C, G, and T. A toy binary mapping can explain the idea, but real systems need metadata, addressing, redundancy, and error correction.
- A/C/G/T are symbols.
- Mapping is not enough.
- Constraints define usable strings.
02
Constraint thinking
A DNA-like string may be invalid for a computational task if it has long repeats, poor GC balance, ambiguous characters, missing indexes, or unsafe annotations.
- Check alphabet.
- Check balance.
- Check declared purpose.
03
Maple boundary
Maple DNA treats encoding as computational representation. It may explain and simulate constraints, but it does not move into wet-lab protocol generation.
- Simulation only.
- No synthesis steps.
- Receipt the safety decision.
Lab
Applied lab: DNA as an information system
Encode a toy message into A/C/G/T, then produce a lint report with alphabet, GC balance, repeat, and safety fields.
Expected result
- A typed JSON-style proposal rather than free-form advice.
- Clear authority boundaries and denied operations.
- A test or rubric that decides whether the proposal is deployable.
Evaluation
How Maple would grade this work
Rubric
- Does the answer expose assumptions instead of hiding them?
- Does the answer separate model proposal from deterministic execution?
- Does the answer produce artifacts that can be tested, reviewed, and rolled back?
Output contract
sequence, encoding_map, lint_checks, safety_class, simulation_receiptUse this lesson as training direction
A strong lesson gives users a mental model and gives Hermon a sharper target for examples, probes, and demo prompts.