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

Abstract

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 competing mechanisms. Logic-style flowcharts—authored as diagrams-as-code (here using Mermaid markdown)—provide a lightweight, versionable complement to pathway databases, genome browsers, and machine-learning predictors. This methods paper distills a seminar-tested workflow for a general research audience: we motivate text-based diagrams in wet-lab and computational pipelines; we compare three deliberately different encodings of the classical Escherichia coli lactose (lac) operon—a literature-first logic diagram with explicit Boolean-style gates, a RegulonDB-emphasis regulatory wiring diagram without those gates, and a hybrid diagram that retains interpretive logic while adding database-grounded entities (notably explicit lacI expression); we explain why the full two-input induction condition (inducer present and catabolite repression relieved) appears only in some encodings; and we recommend layered hybridization (regulatory backbone + literature logic + identifier and parts audits) instead of naively merging incompatible ontologies. No bespoke software is required beyond ordinary LLM access, Mermaid rendering, and public databases.

1. Introduction

The lac operon remains the canonical bacterial example of integrating environmental signals, transcription factors, and promoter logic.¹ Contemporary laboratories rarely study it in isolation, but it is ideal as a tutorial system: many curated representations exist, so source choice becomes visible as scientific information rather than as an invisible preprocessing step.

Meanwhile, perturbation-forward workflows—CRISPR screens, chemical genetics, single-cell readouts, virtual-cell-style predictors—benefit from an explicit, criticizable sketch of inputs, branch points, feedback, and candidate measurements before budget is committed. A flowchart is not a mechanistic ordinary differential equation model and not a trained deep network; it is a hypothesis artifact suitable for group review, supplementary files, and teaching.

Mermaid is a widely supported markdown-adjacent language for flowcharts and related diagrams; source text diffs cleanly in Git and renders in browsers, notebooks, and static site generators.² Modern LLMs can emit fenced mermaid blocks from natural language, which accelerates first drafts but increases the obligation to validate against authoritative resources: prompt and model choice change topology and node identity.

2. Objectives for the practicing investigator

• State what could change under a planned intervention before locking readouts or model architecture.
• Maintain diagrams-as-code beside literature notes and analysis scripts.
• Combine LLM-assisted layout with RegulonDB-class (or organism-equivalent) regulatory facts without collapsing distinct abstraction levels into one unreadable graph.
• Treat disagreement between diagram sources as data—analogous to comparing alignment or quantification pipelines.

3. Role alongside transcriptomics and predictive models

High-dimensional expression or chromatin data and modern predictors answer different questions than a hand-specified logic chart. The chart’s job is to make conditional structure discussable: which environmental axes gate which genes, where redundancy may hide causal effects, and which branch-point perturbations would be most informative. It should complement—not replace—controlled experiments and quantitative models.

4. Practical properties of Mermaid in research groups

• Text → vector diagram: the same source renders in CI documentation, lab wikis, and supplementary PDF pipelines.
• Collaboration: pull requests on diagram source are interpretable; meeting-driven edits are quick.
• LLM interface: models can propose structure; human authors remain responsible for citations, organism fit, and experimental feasibility.

5. LLM-first lac charts: what to expect

A well-posed conversational prompt often yields a surprisingly detailed first pass: two-input logic (allolactose / LacI and glucose / cAMP–CRP), default OFF at the operator, graded induction, sometimes a feedback arc as inducer is consumed. Reliability varies by model version and prompt; treat any auto-generated chart as revision zero to be checked against reviews and databases.

6. Three encodings of the same operon

GLMP-style public comparisons (see viewer linked below) contrast multiple evidence mixes for one operon. For methods exposition we isolate three complementary styles—without the older “V1/V2” labels that confused seminar audiences. Names here are descriptive only.

6.1 Literature-first logic diagram

Built from textbook-style reasoning, this encoding foregrounds explicit AND-style integration (e.g. strong transcription when the operator is free and CAP is productively engaged) and separates high-glucose / low-cAMP branches. It is optimized for pedagogy and perturbation intuition, not for locus tags.

What is typically missing here: there is no first-class node for lacI transcription or for the gene → protein production of the repressor. “LacI” appears only inside the wording of the first diamond—the repressor is assumed to exist whenever lactose logic is discussed. That is fine for high-level logic, but it hides a real experimental lever (CRISPRi on lacI, titration of repressor copy number, etc.).

6.2 RegulonDB-emphasis regulatory wiring

A chart faithful to how RegulonDB (and similar TF→target resources) represent E. coli transcriptional regulation centers who regulates whom with signed or labeled edges; it does not, by itself, encode the full Boolean “both conditions” story as explicit gate nodes. It excels at entity completeness for regulatory interactions.³

6.3 Hybrid diagram (literature logic + RegulonDB entity completeness)

The hybrid merges interpretive structure from reviews and LLM drafts with database-grounded entities. Concretely for lac, one adds an explicit step for lacI transcription → LacI protein upstream of operator control. That node matters for real perturbations (e.g. tuning repressor dosage) that a purely narrative “repressor exists” arrow can elide. Boolean-style gates from the literature-first chart are retained where they clarify conditionality.

Exactly one structural addition vs. Figure A: the two orange nodes at the top—lacI biosynthesis—are the RegulonDB-class spine that Figure A’s red “NOT SHOWN” box warned was missing. The purple diamonds and the final green transcription outcome use the same color language as Figure A so the logic layer is visually comparable; only the orange chain is new.

6.4 At-a-glance: literature-first vs. hybrid

Interactive multi-encoding views for the same operon (many additional source mixes exist in the public corpus) are available at the GLMP demo viewer:
https://storage.googleapis.com/regal-scholar-453620-r7-podcast-storage/glmp-v2/viewer_demo/glmp-viewer-demo-v1-v6.html?process=ecoli_lac_operon&version=v1

7. Why the two-input induction condition is not automatic in database-native views

Strong induction requires relief of LacI-mediated repression and favorable catabolite control (often summarized as glucose scarcity → sufficient cAMP for CRP). Textbook diagrams express that as AND logic. Curated regulatory databases excel at listing regulators, targets, and evidence; they do not always export that simultaneous requirement as explicit gate nodes. LLM- or review-driven charts supply that interpretive layer—subject to validation—while RegulonDB-class edges supply who is connected to whom.

8. Layered hybridization (recommended workflow)

1. Regulatory backbone: TF→gene resolution from a trusted resource (for E. coli, RegulonDB or equivalent).
2. Interpretive overlay: literature or LLM-assisted gates and feedback arcs where they clarify conditionality.
3. Parts audit: EcoCyc / BioCyc for enzyme identities and reactions—often checked rather than merged as raw topology.⁴
4. Identifier layer: KEGG or model-organism locus tags attached as node metadata after topology stabilizes.⁵

Naive union of every node type from multiple ontologies typically obscures the very branch logic that makes diagrams useful for perturbation planning.

9. Prompt sensitivity

Same pathway, same organism, different natural-language instructions (detail level, whether to foreground CRP, metabolism vs. regulation) yield different Mermaid topologies. Best practice: generate two charts, diff the source, reconcile against one primary review figure or database page, and archive both the Mermaid text and the citation in supplementary material.

10. Operational habit: tools before sunk cost

Investigators benefit from routinely pairing literature search, structured databases, and diagram-as-code so that inputs, branch points, and feedback are explicit before large experiments or model training runs. Public GLMP galleries illustrate how source mixtures change charts; they are optional references, not prerequisites.

11. Evidence buckets for perturbation design

Real projects combine:

1. Primary literature and reviews (mechanism, conditions).
2. Pathway and interaction databases (Reactome, KEGG, WikiPathways, BioCyc; STRING for functional linkage).^6–8
3. Gene-centric resources (NCBI Gene, UniProt, Ensembl, GO).
4. Regulatory layers (RegulonDB, Abasy-class summaries where available; refs. 3, 9).
5. Perturbation execution (reagents, libraries, chemistry).
6. Data archives for realistic readouts (e.g. GEO).¹⁰

12. What logic charts add on top of databases and papers

13. Synthesis

Databases summarize what is connected; papers record what was measured under stated designs; a logic flowchart helps investigators choose perturbations that disambiguate mechanisms and anticipate qualitative directions before committing full experimental or computational cost.