From hypothesis to experiment

The computational workflow proposes a candidate, mechanism or intervention to investigate.

INPUT

Model predictions and the discovery objective.

WHAT HAPPENS

Capture the hypothesis, prediction context and candidate identifiers.

OUTPUT

A traceable hypothesis record.

↳ Assess whether the current evidence is sufficient.

Decide which uncertainty should be addressed experimentally before spending a study cycle.

INPUT

Hypothesis record, prior evidence and model uncertainty.

WHAT HAPPENS

Prioritize evidence gaps by relevance to the intended decision.

OUTPUT

A prioritized validation question.

↳ Translate the gap into a human evidence request.

Turn a computational question into a request the experimental team can evaluate.

INPUT

Validation question, candidate conditions and expected evidence.

WHAT HAPPENS

Agree on endpoints, model suitability, controls and the data return format.

OUTPUT

A scoped experimental request and data contract.

↳ Proceed to protocol design and human validation.

Run the agreed study using a suitable human-relevant model and experimental configuration.

INPUT

Experimental request, materials and study protocol.

WHAT HAPPENS

Prepare the model, execute interventions and collect the defined measurements.

OUTPUT

Experimental observations linked to run and sample records.

↳ Package measurements with enough context for downstream use.