Mermaid flowcharts for perturbation design: diagrams-as-code, curated databases, and the E. coli lac operon as a worked example

Planning genetic, pharmacological, or nutritional perturbations is easier when the investigator can state,in advance, which molecular levers are plausible and which readouts would discriminate competingmechanisms. Logic-style flowcharts - authored as diagrams-as-code (here using Mermaid markdown) provide a lightweight, versionable complement to pathway databases, genome browsers, andmachine-learning predictors. This methods paper distills a seminar-tested workflow for a generalresearch audience: we motivate text-based diagrams in wet-lab and computational pipelines; wecompare three deliberately different encodings of the classical Escherichia coli lactose (lac) operon - aliterature-first logic diagram with explicit Boolean-style gates, a RegulonDB-emphasis regulatorywiring diagram without those gates, and a hybrid diagram that retains interpretive logic while addingdatabase-grounded entities (notably explicit lacI expression); we explain why the full two-input inductioncondition (inducer present and catabolite repression relieved) appears only in some encodings; and werecommend layered hybridization (regulatory backbone + literature logic + identifier and parts audits)instead of naively merging incompatible ontologies. No bespoke software is required beyond ordinaryLLM access, Mermaid rendering, and public databases.