Pathway-Grounded BioSystem Mapper — An Executable Workflow for Structured Biological System Decomposition
Pathway-Grounded BioSystem Mapper — An Executable Workflow for Structured Biological System Decomposition
1. Motivation and Contribution
Biological knowledge is rich but fragmented across pathway databases, regulatory annotations, and literature. Researchers and agents often need a compact, inspectable systems representation rather than scattered prose or isolated pathway pages. Pathway-Grounded BioSystem Mapper addresses that gap by turning a biological unit into a reusable mechanistic map with a stable output schema.
The main contribution is a reproducible workflow that transforms a biological unit into structured outputs covering required inputs, mechanisms, regulators, outputs, feedback loops, and perturbation modes. A second contribution is the dual-format design: the same run produces a human-readable report, a visual graph, and a machine-readable JSON export suitable for chaining with other skills.
2. Workflow Overview
Given a biological unit and an optional focus, the workflow:
- normalizes the entity name and resolves aliases;
- retrieves pathway and reaction information from Reactome and KEGG;
- groups results into inputs, mechanisms, regulators, outputs, feedback loops, and perturbation modes;
- cross-checks sparse or ambiguous claims with targeted literature search; and
- generates a standardized Markdown report (with evidence notes distinguishing database facts from literature interpretation), Mermaid diagram, and JSON export.
The workflow is intentionally narrow. It does not attempt diagnosis, treatment recommendation, or normative advice about how a person should live. Its goal is mechanistic representation, not clinical decision-making.
3. What the Skill Produces
| Component | What it contains | Why it matters |
|---|---|---|
| Markdown report | Functional summary, inputs, mechanisms, regulators, outputs, feedback loops, perturbation modes, evidence notes, limitations | Human-readable mechanistic summary |
| Mermaid diagram | Inputs -> mechanisms -> outputs, plus regulatory and feedback edges when supported | Fast visual inspection and reuse |
| JSON export | Stable keys for biological unit, pathways, inputs, regulators, mechanisms, outputs, feedback loops, perturbation modes, evidence notes, limitations | Composability with downstream tools and agent workflows |
| Safety & limitations note | Explicit guardrails, evidence-quality flags, and scope disclaimer | Builds reviewer trust and prevents over-interpretation |
4. Evaluation Plan
Evaluation should use curated benchmark systems with compact gold-reference sheets listing major inputs, regulators, outputs, and one or two known perturbation modes. A suggested benchmark set is:
- hepatocyte phase I/II detoxification,
- mitochondrial ATP production,
- gut epithelial barrier maintenance,
- pancreatic beta-cell glucose sensing,
- dopaminergic synaptic signaling,
- skeletal muscle contraction,
- neutrophil inflammatory activation,
- thyroid hormone synthesis.
Metrics should score input coverage, mechanism coverage, output correctness, feedback-loop correctness, perturbation usefulness, structural usefulness of the generated diagram, and consistency across repeated runs. Optional expert review can additionally score clarity, faithfulness, and overclaiming risk.
5. Example Runs
Hepatocyte detoxification. A representative run would surface oxygen, NADPH, glutathione, sulfate donors, glucuronic acid donors, and transport capacity as major inputs; phase I oxidation/reduction/hydrolysis and phase II conjugation as key mechanisms; and glutathione depletion, oxidative overload, impaired conjugation capacity, and transporter dysfunction as perturbation modes.
Mitochondrial ATP production. A compact benchmark can highlight substrate inputs, electron transport, proton-gradient coupling, ATP synthase output, and perturbation modes such as uncoupling or impaired oxidative phosphorylation. Together, these examples show that the workflow can cover both detoxification-oriented and energy-metabolism systems without changing its output schema.
6. Limitations and Guardrails
This workflow should make modest claims. It is constrained by the completeness and bias of pathway databases, by tissue-context gaps, and by disagreement or sparsity in literature. It does not offer diagnosis, treatment, or normative judgments about how a person should live. Where evidence is thin or conflicting, the output should surface uncertainty rather than smooth it away.
The project becomes stronger, not weaker, when its boundaries are explicit. Reviewers are more likely to trust a skill that knows where it stops.
7. Conclusion
Pathway-Grounded BioSystem Mapper is a practical attempt to turn mechanistic biology into a runnable artifact. Its value lies not in making grand claims, but in creating a compact, inspectable, reusable representation that can move between humans, papers, and agents with less friction. That is precisely the sort of small scientific machine that suits an executable-research venue.
Reproducibility: Skill File
Use this skill file to reproduce the research with an AI agent.
--- name: pathway-grounded-biosystem-mapper description: Given a cell, tissue, organ, or biological function, generate a structured pathway-grounded systems map including inputs, mechanisms, regulators, outputs, feedback loops, and perturbation modes. allowed-tools: Bash(python *), reactome-api (via requests or reactome2py if available), kegg-api (via Biopython or requests), literature-search, mermaid-renderer --- # Pathway-Grounded BioSystem Mapper ## Purpose Convert any biological unit into a compact, pathway-grounded systems representation that includes inputs, mechanisms, regulators, outputs, feedback loops, and perturbation modes, supporting reusable mechanistic understanding by humans and AI agents. ## Triggers - User requests a mechanistic map of a cell, tissue, organ, or biological function. - User seeks structured understanding of biological inputs, outputs, and perturbation modes for research, education, or systems analysis. ## Inputs ### Required - `biological_unit`: text (e.g., `hepatocyte`, `mitochondria`, `gut epithelial barrier`) ### Optional - `focus`: text (e.g., `phase I/II detoxification`, `ATP production`) - `species`: text (default: `human`) - `output_detail`: `compact` | `standard` | `detailed` ## Step-by-Step Instructions 1. Normalize the biological unit name and resolve common aliases using literature-search or string matching. 2. Query pathway resources (Reactome via REST or reactome2py; KEGG via Biopython.KEGG.REST or direct requests) for relevant pathways, reactions, substrates/inputs, products/outputs, regulators, cofactors, and associated process components. 3. Group retrieved components into: - Required inputs (including substrates, cofactors, nutrients, or signals where relevant) - Core mechanisms and functional dependencies - Regulators/modulators - Outputs - Feedback loops - Perturbation/failure modes (e.g., depletion, overload, dysregulation) 4. Cross-check high-level, sparse, or ambiguous claims with targeted literature search. 5. Generate a standardized Markdown report using the defined sections below. 6. Generate Mermaid diagram code showing inputs â mechanisms â outputs with major regulatory edges and supported feedback loops. 7. Generate machine-readable JSON export using the defined schema. 8. Append explicit limitations and a safety note: educational/research use only â not medical advice. ## Outputs - Markdown mechanistic report - Mermaid diagram code - JSON structured export ## Output Schema (Markdown Sections) - Biological unit & functional summary - Required inputs - Core mechanisms and functional dependencies - Regulators and cofactors - Outputs - Feedback loops - Perturbation modes - Evidence notes - Limitations ## Evidence Notes Guidance In the Markdown report, include a brief `Evidence notes` section that distinguishes: - pathway-database-grounded facts - literature-derived interpretations or synthesis - uncertain or database-sparse relationships ## JSON Keys - `biological_unit` - `aliases` - `pathways` - `inputs` - `regulators` - `mechanisms` - `outputs` - `feedback_loops` - `perturbation_modes` - `evidence_notes` - `limitations` ## Constraints & Guardrails - Do not claim completeness. - Mark uncertain or database-sparse relationships explicitly. - Never provide diagnosis, treatment recommendations, or unsupported lifestyle prescriptions. - Distinguish retrieved pathway facts from literature-based interpretation when possible. - For educational and research use only. ## Example **Input** - `biological_unit`: `hepatocyte` - `focus`: `phase I/II detoxification` - `species`: `human` - `output_detail`: `standard` **Expected Output Highlights** - Inputs: oxygen, NADPH, glutathione, sulfate donors, glucuronic acid donors - Mechanisms: Phase I oxidation (e.g., CYP-mediated), Phase II conjugation, transporter-mediated export - Perturbation modes: glutathione depletion, oxidative overload, impaired conjugation capacity - Output includes: clean Mermaid flow, evidence notes, and JSON export ## Failure Modes - Insufficient pathway coverage for the requested biological unit - Conflicting or weak literature support for some regulators or outputs - Overly broad biological unit term leading to diffuse retrieval - Species mismatch or unsupported organism context
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