A scientific rationale is an inspectable chain from a precisely defined claim to its evidence, assumptions, calculation and limits.
Methods, evidence and interpretation
01
A claim needs a chain of evidence
Start with a precisely bounded claim: which biological entity, condition, intervention and observable does it concern? Link it to identifiable evidence rather than only to a paper title. Keep the assay, species, tissue, exposure, endpoint and source location with the record. An observed measurement, an imported database annotation and a model inference are different objects. MIRIAM establishes annotation principles that connect computational entities to their biological meaning and associated descriptions; such annotation improves traceability without establishing clinical truth.
A model file is not a complete simulation experiment. SBML encodes model structure and mathematical semantics; MIASE describes the information needed to reconstruct an experiment, while SED-ML can encode model references, modifications, simulation tasks, post-processing and outputs. A replay record should additionally identify dataset and model versions, parameter provenance and units, initial conditions, software versions, solver settings and any random seed. Parameter values estimated from data should be distinguishable from literature values, fixed assumptions and illustrative choices.
Re-running an archived calculation tests whether its computational recipe is sufficiently specified. It does not determine whether the represented biology is correct. Record tolerances and algorithm choices, check numerical convergence, and compare implementations where feasible. A reproduced curve can still reflect an inappropriate structure, a measurement artifact or overfitting. Biological validation therefore needs independent observations relevant to the stated context, explicit failure criteria and comparisons with simpler alternatives. Reproduction, model verification and empirical validation should be reported separately.
Sensitivity asks how an output changes when a parameter changes; a local quantity is ∂y(t)/∂θ. Identifiability asks whether the available observations can distinguish parameter values. Structural non-identifiability can persist even with ideal noise-free observations; practical non-identifiability can result from sparse, noisy or insufficiently varied data. Profile likelihood fixes one parameter and re-optimizes the others to explore compatible values. A parameter can influence the output strongly while remaining difficult to identify because another parameter compensates for it.
Measurement noise, uncertainty about parameters and uncertainty about model structure should not be collapsed into one confidence label. Preserve the source and dependence of uncertain inputs, then examine their effect on outputs. A sweep over chosen values is a scenario analysis; it is not automatically a frequentist confidence interval or a Bayesian credible interval. State which assumptions generate each range, which uncertainty sources remain excluded and whether conclusions change under plausible alternative structures. Similar point estimates can conceal very different uncertainty.
A useful rationale distinguishes the evidence used, the assumptions imposed, the calculation performed and the conclusion that actually follows. An explanation of a prediction is not itself evidence of a causal biological mechanism. If a model is evaluated for clinical prediction, report its intended population, outcome, evaluation design, performance and limitations using appropriate guidance such as TRIPOD+AI. Transparent reporting does not certify clinical validity. Any proposed application still requires fit-for-purpose validation, interaction assessment and qualified review.
Synthetic educational examples · no patient data or compound recommendations
Inspect a claim’s provenance chain
Follow a fictional claim from source record to review. Each stage exposes the information needed to interpret or reproduce it.
An evidence record, not a conclusion
Example record E-017: assay context, species, endpoint, exposure and source identifier. A citation alone is insufficient when the claim depends on a particular figure, assay or subgroup.
Provenance checklist complete in this fictional example; replay and biological validation are still separate tasks.
All identifiers are fictional. This is an audit-design demonstration, not a live audit log or evidence that the proprietary Curetanics model has passed validation.
These sources support the scientific methods discussed. They do not validate Curetanics or establish efficacy for a compound. Reference titles remain in their original language.