# Rules railroad: Syntax-inspired diagrams for visualizing and understanding rule-based model specifications

**Authors:** Reesha J. Patel, Michael L. Blinov

PMC · DOI: 10.1371/journal.pcbi.1014121 · PLOS Computational Biology · 2026-03-25

## TL;DR

RRR diagrams offer a new way to visualize complex biochemical interactions using continuous flow diagrams inspired by syntax diagrams in computer science.

## Contribution

Introduces RRR diagrams, a novel visualization method for rule-based models that unifies structural context and transformations in a compact format.

## Key findings

- RRR diagrams combine molecular context and transformations into a single continuous diagram.
- The diagrams are more compact and precise compared to traditional visualization methods.
- They enhance readability and are suitable for debugging, communication, and education.

## Abstract

Rule-based modeling provides a powerful framework for describing and simulating biochemical systems composed of multi-site molecules and multi-molecular species. By encoding molecular interactions as rules rather than enumerating all possible species, this approach naturally accounts for the combinatorial complexity of connectivity within chemical species. Despite these advantages, visualization of such models remains challenging. Existing approaches, such as contact maps, give a high-level overview of possible sites and interactions but lack explicit representation of dynamic processes, while traditional rule cartoons split reactants and products across a reaction arrow, separating molecular context from transformation. We introduce Rules Railroad (RRR) diagrams, a novel diagrammatic representation of rule-based model specification. Each RRR diagram encapsulates a single rule as a continuous flow diagram with embedded actions, including binding, unbinding, and state changes. Inspired by classical railroad (syntax) diagrams used to represent formal grammars, RRR diagrams encode both the structural context and the transformations of a rule in a unified format, more compact compared to a classical visualization approach of presenting a single rule as a reactant-product pair. This integration reduces ambiguity, enhances readability, and provides a systematic, human- and machine-readable visualization of any rule-based system. RRR diagrams are precise, and suitable for debugging, communication, and education.

Biological systems often depend on molecules that can exist in many different forms and interact in complex ways. Rule-based modeling is a powerful method for describing and simulating these interactions, but current ways of visualizing rules are either show only some details or split across separate before-and-after diagrams. To address this, we introduce Rules Railroad (RRR) diagrams, a new way of representing rule-based models. Inspired by the railroad diagrams used in computer science to explain programming languages, RRR diagrams combine the context of a molecular interaction and the changes it causes into a single continuous diagram. This makes them compact, precise, and easy to read.

## Full-text entities

- **Genes:** SRC (SRC proto-oncogene, non-receptor tyrosine kinase) [NCBI Gene 6714] {aka ASV, SRC1, THC6, c-SRC, p60-Src}, EGF (epidermal growth factor) [NCBI Gene 1950] {aka HOMG4, URG}, EGFR (epidermal growth factor receptor) [NCBI Gene 1956] {aka ERBB, ERBB1, ERRP, HER1, NISBD2, NNCIS}, GRB2 (growth factor receptor bound protein 2) [NCBI Gene 2885] {aka ASH, EGFRBP-GRB2, Grb3-3, MST084, MSTP084, NCKAP2}, PTPRU (protein tyrosine phosphatase receptor type U) [NCBI Gene 10076] {aka FMI, PCP-2, PTP, PTP-J, PTP-PI, PTP-RO}
- **Diseases:** breast cancer (MESH:D001943)
- **Chemicals:** tyrosine (MESH:D014443)
- **Species:** Canis lupus familiaris (dog, subspecies) [taxon 9615], Homo sapiens (human, species) [taxon 9606], Felis catus (cat, species) [taxon 9685]

## Full text

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## Figures

8 figures with captions in the complete paper: https://tomesphere.com/paper/PMC13035228/full.md

## References

41 references — full list in the complete paper: https://tomesphere.com/paper/PMC13035228/full.md

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Source: https://tomesphere.com/paper/PMC13035228