Mermaid Flowcharts and Smarter Perturbation Design

Abstract

Perturbation experiments in transcriptomics and related fields succeed when investigators can state, in advance, what might change when they intervene. Logic-style flowcharts—especially when authored as diagrams-as-code with Mermaid—offer a lightweight, inspectable complement to pathway databases, genome browsers, and machine-learning models. This article synthesizes material from a classroom-oriented seminar: it motivates Mermaid in the wet-lab and computational workflow, compares six deliberately different representations of the classical E. coli lac operon (literature-informed LLM output, hybrid RegulonDB-informed charts, and database-native views from RegulonDB, EcoCyc, KEGG, and Abasy), explains why only some encodings surface the full two-input AND logic of induction, and recommends layered hybridization rather than naive merging of incompatible ontologies. The emphasis throughout is on habits students can adopt with generic tools, not on any single curated corpus.

1. Introduction

Classical bacterial gene regulation—notably the lac operon—remains a touchstone for teaching how signals, repressors, and catabolite activation combine to gate transcription.¹ Modern courses increasingly pair that biology with bulk and single-cell readouts, predictive models, and “virtual cell” narratives. In that stack, a hand-drawn or code-generated flowchart is not a substitute for a mechanistic model or a trained neural network; it is a hypothesis sketch: an explicit map of branches, feedback, and candidate intervention points that can be shared, versioned, and critiqued in a lab meeting.

Mermaid is a text-based diagram language embedded in many Markdown workflows; renderers produce flowcharts, sequence diagrams, and state machines from source that diffs cleanly in Git.² Large language models (LLMs) can draft Mermaid blocks from natural-language prompts, which lowers the barrier to first drafts but raises the cost of verification: different prompts and models yield legitimately different graphs for the same pathway.³

The seminar used the lac operon as a worked example because multiple curated resources can be aligned to the same story at different abstraction levels (references 4–10). Comparing those views side by side illustrates a broader lesson: source choice is scientific information, and reconciling diagrams is analogous to reconciling pipelines or model outputs.

2. Pedagogical aim

The central messages for readers mirror the classroom deck:

• A well-designed perturbation begins with clarity about what could change when a gene, nutrient, or drug is manipulated.
• Mermaid (or equivalent diagram-as-code) supports rapid iteration and collaboration alongside literature and databases.
• The durable skill is tool stack plus critical reading, not dependence on any single database or pre-built chart.
• Prompt and model variation produces diagram variation; readers should validate against textbooks and curated resources.

3. Alignment with transcriptomics and AI-heavy curricula

Students learning to predict and interpret cellular responses to perturbations from high-dimensional data still benefit from a compact picture of inputs, branch points, and feedback before they commit to expensive readouts or large fits. A flowchart complements feature matrices, deep models, and virtual-cell lectures: it helps choose what to perturb and what to measure, and it supports sanity checks on whether predicted directions of change match mechanism.

4. What Mermaid offers in practice

• Text to diagram: Markdown-like syntax is rendered to vector graphics in editors, static sites, and the Mermaid Live Editor.
• Portability: the same source travels with version control and notebooks.
• Collaboration: readable diffs and straightforward post-meeting revision.
• LLM compatibility: models can emit fenced mermaid blocks; human authors should edit, cite, and verify.

5. Lac operon: an LLM-first draft

When asked (via a conversational prompt) for a Mermaid flowchart of lac operon biochemistry, contemporary LLMs often return a surprisingly rich first pass: two-input logic (lactose / allolactose and glucose / cAMP–CAP), default OFF with repressor on the operator, graded induction, and sometimes a feedback arc as lactose is depleted. That quality is not guaranteed; it depends on model, prompt, and training cutoff. Treat LLM output as a first draft to be checked against authoritative descriptions and databases.

6. V1 versus V2: one node teaches hybrid value

In the six-way comparison used in class, V1 denotes an LLM- or literature-leaning chart that captures regulatory logic but may elide explicit transcription of the lacI repressor gene as its own event. V2 (hybrid: LLM logic on a RegulonDB-informed backbone) adds exactly that node—LacI gene expression—between cAMP–CAP signaling and the conditional steps at the operator. For perturbation design, tuning or knocking down lacI is a real lever; representing it explicitly matters. The lesson generalizes: regulatory databases encode entity-level completeness that narrative or shortcut LLM graphs may omit.⁴

Interactive comparison (same operon, switchable versions): GLMP lac operon viewer (V1–V6).

7. Six versions, six questions

The same biological system can support six charts that differ systematically because each source was built to answer a different primary question: mechanistic logic (V1), logic plus regulator identity (V2), transcriptional wiring (V3), parts and reactions (V4), stable identifiers and pathway membership (V5), or regulon-level architecture (V6). None is “wrong” in isolation; each is partial relative to the others.

7.1 Feature comparison (summary)

8. Only some encodings surface the full AND condition

Strong induction of the lac operon requires lactose (via allolactose-mediated relief of LacI repression) and catabolite repression to be overcome (often summarized as glucose absent so cAMP–CRP can assist). In the seminar comparison, only V1 and V2 encode that two-input AND structure as explicit Boolean-style gates. Database-native views emphasize facts—who binds whom, which genes belong to which regulon—without necessarily packaging the simultaneous environmental logic the way a textbook narrative does. The LLM, when well prompted, supplies that interpretive layer; the hybrid preserves it while grounding entities in RegulonDB-style wiring.

9. Layered hybridization instead of one giant merge

Naively unioning nodes and edges from RegulonDB, EcoCyc, KEGG, and Abasy risks ontology collision: mixed abstraction levels bury the very conditional logic that makes V1/V2 useful for experiment planning. A more productive pattern is layered:

1. Structural partner: a regulatory database at TF→gene resolution (for E. coli, RegulonDB-class) for entity completeness compatible with logic graphs.
2. Parts audit: EcoCyc / BioCyc for enzyme names, reactions, and products—often as validation rather than wholesale structural merge.⁵
3. Identifier layer: KEGG locus tags and pathway IDs as metadata on stabilized nodes.⁶
4. Architecture check: Abasy or similar regulon summaries as a zoomed-out consistency check.¹⁰

10. Prompts change the picture

Wording shifts (detail level, audience, whether to foreground CAP, metabolism versus regulation) change node sets and layout. There is no single “official” Mermaid chart for a biological story—only better or worse fits to evidence. Good practice: generate two versions from two prompts, diff them, and reconcile against a trusted review or database page; store both Mermaid source and citations in the lab record.

11. Habits: use tools to reveal structure

Readers should build a habit of reaching for literature, databases, and diagram-as-code together so that inputs, branch points, and feedback are explicit before major experimental or modeling investment. Optional public demos (for example the GLMP viewer hub) illustrate how source mixtures change diagrams; they are galleries for comparison, not prerequisites for sound practice.¹¹

12. Seven resource buckets for perturbation design

Most real projects blend multiple evidence types:

1. Primary literature and reviews — mechanism, conditions, phenotypes; PubMed / Europe PMC.
2. Pathway and interaction context — Reactome, KEGG, WikiPathways, BioCyc; STRING for functional associations (references 5–9).
3. Gene-centric facts — NCBI Gene, Ensembl, UniProt, Gene Ontology; model-organism databases (e.g. SGD for yeast).
4. Regulatory layers — RegulonDB, Abasy, review figures.
5. Perturbation modalities — CRISPR, RNAi, libraries, small molecules; reagent repositories such as Addgene.¹²
6. Data-first inspiration — GEO, atlases, screens for realistic readouts.¹³
7. Organism choice — human cell lines versus classic genetics models; regulatory depth varies and should be stated.

13. How logic flowcharts supplement databases and papers

14. Synthesis

“Databases tell you what is connected; papers tell you what was measured; a logic flowchart helps you choose perturbations that disambiguate mechanisms and predict qualitative directions before you spend money and time on the experiment or on fitting a large model.”

That framing places interpretive structure on top of the usual evidence stack—complementary to transcriptomics, machine learning, and virtual-cell themes, not in competition with them. From chart to bench: mark manipulable inputs, mark branch points for discriminative perturbations, map boxes to measurement targets, and expect to revise after data—the loop is the science.

15. Structured prompting (optional)

For JSON-first extraction of steps and edges before Mermaid, readers may consult the Programming Framework materials on Zenodo and the one-page Appendix A prompt HTML linked from the slide deck.³

16. Related GLMP manuscripts (HTML)

These companion pieces develop genomic computational framing and empirical tests referenced in broader GLMP work:

• Primitive Relations, Computational Complexity, and a Conjecture on the Genomic Computational Class
• The Genome as Computer: Logical Primitives, Runtime States, and the Computational Limits of Biological Prediction
• Circuit Class Predicts Virtual Cell Model Accuracy: An Empirical Test of the Genomic Computational Complexity Hypothesis