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Architecture

bijux-proteomics-lab architecture is where recommendation intent becomes workable assay reality. This section should help a reader see how planning, scheduling, outcomes, and feedback loops operate under lab constraints without pulling decision policy or shared meaning into the wrong layer.

flowchart LR
    intent["recommended assay intent"]
    constraints["capacity and dependency constraints"]
    planning["planning models"]
    schedule["executable schedule"]
    outcomes["observed outcomes"]
    rerun["rerun and escalation decisions"]
    feedback["repository feedback"]

    intent --> planning
    constraints --> planning
    planning --> schedule --> outcomes --> rerun --> feedback

Architectural Promise

  • the lab package should make operational reality explicit rather than implicit
  • schedule decisions should stay traceable back to assay requirements and constraints
  • outcome interpretation should feed back into the wider system without stealing program authority

Start With

  • open Execution Model when the question is how intent becomes schedules and then outcomes
  • open Integration Seams when a change risks importing recommendation policy or shared payload meaning into lab logic
  • open Module Map when you need the owner for planning, repositories, outcomes, or schema code

Read By Workflow Moment

First Proof Check

  • src/bijux_proteomics_lab/planning.py and outcomes.py for the lab-facing control flow
  • src/bijux_proteomics_lab/schema.py and serialization.py for contract structure
  • src/bijux_proteomics_lab/repositories.py for durable storage boundaries

Boundary Test

If a schedule decision cannot be explained in terms of assay intent, dependencies, and observed outcomes, the architecture is not telling the truth about the package.