bijux-proteomics-lab

bijux-proteomics-lab is the operational lab layer for assay planning, readiness, handoff honesty, and observed-outcome reconciliation. It turns scientific intent into executable work only when queue pressure, material limits, controls, provenance, and operator-facing caveats stay explicit.

Within the suite, lab owns assay consequence planning, readiness, and observed outcomes.

Use this package when the question is whether a follow-up is feasible, safe to hand off, and traceable after execution. Its success criteria are operational honesty, feasibility, and traceability rather than analytical enthusiasm. Operational honesty, feasibility, and traceability are the non-negotiable release criteria for this package.

Release-facing maintainers should keep README.md, CHANGELOG.md, and the package docs/*.md set aligned before claiming stronger operational coverage or handoff safety guarantees.

At a glance

• Use lab when the real question is whether a follow-up should run, can run safely, and can be reconciled honestly after execution.
• Start with the planning entrypoints for advisory and executable assay plans, then open the lab handbook for readiness, handoff, and outcome routes.
• Route scientific truth to core, evidence memory to knowledge, recommendation posture to intelligence, and execution control to runtime.

Why teams pick this package

• dependency-aware planning that keeps queue pressure, gate pressure, and family capacity visible before spend is committed
• readiness and handoff contracts that refuse weak, ambiguous, or under-controlled follow-up instead of flattening those problems into notes
• outcome reconciliation that records what was asked, what actually ran, and how belief posture should change afterward
• benchmark rehearsals that show whether targeted operational claims are supportable before operators inherit them
• outcome dossiers and worth-it ledgers that show whether requested follow-up loops repaid their cost after execution pressure was visible

Typical use cases

• build executable assay batches from scientific requirements, dependencies, and capacity limits
• block or downgrade execution when material, controls, provenance, staffing, or instrument readiness is weak
• produce operator-facing handoffs that preserve protocol version, required controls, caveats, field loss, and refusal reasons
• reconcile observed outcomes back into supported, weakened, or blocked feedback for downstream review

0.3.8 Release Highlights

• Lab now publishes typed design, planning, readiness, handoff, reconciliation, and follow-up packet surfaces across DDA, DIA, LFQ, PTM, and targeted workflows.
• Targeted benchmark rehearsal, refusal, outcome dossier, and learning surfaces now make assay burden and observed follow-up visible before stronger downstream claims are made.
• The package root is deliberately narrowed to planning entrypoints while the durable owner bands keep readiness, handoff, outcome, and benchmark behavior auditable.

Installation

pip install bijux-proteomics-lab

Quick start

from bijux_proteomics_lab import (
    build_advisory_assay_plan,
    build_executable_assay_plan,
    plan_experiment_batches,
)

Import owner bands directly for deeper work:

from bijux_proteomics_lab.handoffs.explanations import build_handoff_explanation
from bijux_proteomics_lab.handoffs.exports import build_lims_export_bundle
from bijux_proteomics_lab.handoffs.transitions import build_targeted_transition_review
from bijux_proteomics_lab.readiness.operations import build_operational_readiness_report
from bijux_proteomics_lab.reconciliation.follow_up import (
    reconcile_planned_and_observed_outcome,
)

Public APIs

The stable root API stays focused on planning and execution readiness:

• plan_experiment_batches(...) for dependency-aware batch planning
• build_advisory_assay_plan(...) for non-executable scientific follow-up guidance
• build_executable_assay_plan(...) for execution-ready batch instructions

Minimal executable example:

from bijux_proteomics.domain.assays import AssayRequirement
from bijux_proteomics.domain.program_spec import create_program_spec
from bijux_proteomics.domain.reviews import ReviewGate
from bijux_proteomics_lab import build_advisory_assay_plan

program = create_program_spec(
    program_id="prog-readme",
    name="binder rescue",
    objective="recover binding while preserving folding",
    target_id="protein:p11111",
    target_name="PTM1",
    sequence="MPEPTIDEK",
    organism="human",
    mechanism="stabilize productive packing",
).model_copy(
    update={
        "assay_panel": [
            AssayRequirement(
                assay_id="primary-binding",
                purpose="confirm target engagement",
                readout="binding_score",
                sample_kind="biophysical",
                blocking=True,
            )
        ],
        "review_gates": [
            ReviewGate(
                gate_id="pre-synthesis",
                name="Pre-synthesis review",
                required_roles=["scientist"],
                decision_inputs=["evidence_bundle"],
            )
        ],
    }
)
plan = build_advisory_assay_plan(program)

assert plan.program_id == "prog-readme"
assert plan.recommendations[0].assay_id == "primary-binding"

Package identity

• Distribution name: bijux-proteomics-lab
• Import root: bijux_proteomics_lab
• Stable root entrypoints: plan_experiment_batches, build_advisory_assay_plan, and build_executable_assay_plan
• Stable owner bands: design, planning, readiness, lifecycle, handoffs, outcomes, reconciliation, and benchmarks

Package boundaries

This package owns execution reality for lab follow-up: planning, readiness, handoff honesty, observed-outcome reconciliation, and benchmark rehearsals that prove whether an operational story is supportable.

It does not own analytical recommendation logic, core scientific semantics, or execution orchestration or runtime policy.

What this package must not do

• it must not decide analytical ranking or recommendation posture
• it must not redefine core scientific semantics or runtime orchestration rules
• it must not hide blocked work inside optimistic handoff packets or free-form operator notes

Consequence chain route

Lab owns the downstream burden and observed outcome part of the shared consequence chain, not a separate trust story that can outrank knowledge or intelligence.

• use Workflow Consequence Maps when the question is whether assay burden or the cost of being wrong already blocks stronger public recommendation language
• use Outcome Learning Loops when the question is how requested-versus-observed follow-up should tighten or weaken the next recommendation
• use Workflow Refusal Handbook when the honest next action is to stop, rerun, narrow, or refuse before more assay spend

Contract checkpoints

• planning outputs preserve dependency, queue, and material context
• readiness reports keep blocked controls, provenance gaps, and staffing or instrument pressure explicit
• handoff artifacts preserve protocol version, required controls, refusal reasons, and lossy export notes
• reconciliation keeps requested assays, observed assays, belief posture, and operational follow-through aligned
• benchmark outputs keep supported, weakened, and blocked claims separate
• benchmark outcome dossiers keep requested assays, observed assays, and worth-it judgment visible in the same owner surface

Choose this package when

• a change alters lab feasibility, execution readiness, handoff integrity, or observed-outcome follow-through
• queue pressure, material limits, or control coverage need to change how work is planned or refused
• the package should be able to defend a handoff to operations reviewers without hiding weak evidence or fragile execution assumptions

Route elsewhere when

• the change decides which candidates should be recommended, ranked, or argued for analytically
• the change defines core scientific truth such as assay semantics, domain lifecycle law, sequence meaning, or evidence ontology
• the change owns execution orchestration, runtime policy, provider binding, route transport, or operator entrypoints

Verification route

• run the owner-family tests under tests/design, tests/planning, tests/readiness, tests/handoffs, tests/outcomes, and tests/reconciliation
• review docs/BOUNDARIES.md, docs/CONTRACTS.md, and docs/ARCHITECTURE.md when ownership or refusal behavior changes
• use tests/benchmarks when targeted operational claims or benchmark rehearsals change

Review questions

• does the change improve operational honesty, feasibility, or traceability rather than just making a recommendation look cleaner
• would operators learn about blocked controls, weak provenance, or material scarcity early enough to stop irresponsible spend
• can the package still explain the behavior without claiming analytical recommendation authority, core scientific semantics, or runtime ownership

Escalation route

• escalate out when the real owner is intelligence recommendation policy, core scientific semantics, or runtime execution orchestration
• stop and review the handoff and readiness owners when a proposal tries to smuggle fragile execution assumptions through a successful-looking packet
• escalate before release when operators could mistake advisory enthusiasm for executable readiness

Consumer impact signals

• treat changes to queue pressure, material feasibility, refusal reasons, protocol caveats, or reconciliation posture as high impact
• expect downstream review when benchmark, handoff, or readiness behavior changes because operators and reviewers depend on those claims staying interpretable
• treat root-surface changes as high impact because the root is intentionally narrow and curated

Explicit non-goals

• this package does not own analytical recommendation logic
• this package does not own core scientific semantics
• this package does not own execution orchestration or runtime policy
• this package does not hide blocked work inside optimistic packets or free-form operator notes

Source guide

• planning/assays.py, planning/scheduling.py, planning/priorities.py, and planning/next_cycle.py for executable planning, capacity fitting, practical prioritization, and next-cycle recommendation
• readiness/operations.py and readiness/stages.py for material, provenance, controls, staffing, instrument, and stage readiness
• handoffs/transitions.py, handoffs/explanations.py, and handoffs/exports.py for transition review, refusal behavior, explanation packets, and lossy export notes
• handoffs/risk.py and handoffs/ptm.py for assay-risk and PTM-specific operational controls
• outcomes/observations.py and reconciliation/follow_up.py for observed outcome posture and follow-through
• benchmarks/claims.py and benchmarks/rehearsals.py for targeted benchmark claim support and rehearsal delivery
• benchmarks/follow_up.py and benchmarks/outcome_dossiers.py for planned assay boundaries, requested-versus-observed closure, and assay-worth-it evidence

Documentation

• Lab consequence
• Outcome learning loops
• Product architecture
• Cross-package ownership
• Package guide
• Architecture overview
• Ownership boundary
• Interface contracts
• Workflow refusal handbook
• Experiment design and protocol planning