Protein-protein interaction networks
Build PPI networks from curated interaction databases, filtered by confidence, to see how your hits physically and functionally connect.
Core Bioinformatics
Network Biology & Pathway Analysis
A long gene list tells you what changed; a network tells you how it fits together. We build and analyse biological networks—protein-protein interaction, gene-regulatory, and co-expression—detect the modules inside them, and map results onto pathways, so structure and mechanism come out of the hairball. Reproducible, from documented code.
Gene/protein lists or omics in
Networks · modules · pathways out
Typical turnaround 3–7 days
Individual pricing from $149
Illustrative sample output
module 1
module 2
module 3
Overview
From gene lists to the wiring behind them
Most omics analyses end with a ranked list of genes or proteins. Network biology picks up there: it places those hits into the web of interactions, regulation, and co-expression they belong to, so you can see which ones sit at hubs, which cluster into functional modules, and which pathways the changes converge on. A network turns a flat list into testable structure.
We construct the network that fits your question—protein-protein interaction from curated databases, regulatory from expression, or co-expression with WGCNA—then detect modules, run pathway and topology analysis, and prioritise the genes worth following up. This is the graph layer that complements multi-omics integration and biostatistics; we cross-reference those rather than repeat them, and everything ships with version-locked code.
Capabilities
What we build & analyse
One service spanning the network lifecycle—from constructing the graph through module detection and pathway analysis to prioritisation and visualization.
Gene regulatory network inference
Infer directed transcription-factor-to-target relationships from expression, to propose who regulates whom.
Co-expression networks (WGCNA)
Weighted co-expression networks that group genes varying together, relate modules to traits, and surface hub genes.
Pathway enrichment & topology
Map genes and modules onto curated pathways with over-representation, GSEA, and topology-aware methods.
Module & community detection
Partition the network into communities and dense sub-networks that behave as functional units.
Network propagation & prioritization
Propagate signal across the network and rank genes by centrality and proximity to seeds for follow-up.
Multi-omic & integrative networks
Combine layers—expression, protein, interaction—into integrative networks using curated prior knowledge.
Network visualization
Publication-quality, navigable network figures and interactive sessions collaborators can explore.
The Pipeline
How a network analysis runs
A transparent sequence from inputs to an interpreted, interactive network—the network type and methods chosen for your data, every step documented.
Steps adapt to your project: PPI vs. regulatory vs. co-expression, a single network vs. an integrative one, a static figure vs. an interactive Cytoscape session. We agree the plan with you first.
Scope & inputs
We agree the question and network type, and gather inputs—a gene or protein list, or an expression matrix for co-expression.
Inputs: gene list · matrix · analysis plan
Network construction
The network is built—PPI from databases, regulatory from expression, or co-expression—with confidence thresholds set explicitly.
Tools: STRING · WGCNA · GENIE3
Topology & QC
We characterise the network—degree, centrality, hubs, connectivity—and check robustness to thresholds.
Tools: igraph · NetworkX
Module detection
Community-detection algorithms partition the network into functional modules and dense sub-networks.
Methods: Leiden · MCODE · WGCNA
Pathway & functional analysis
Modules and gene sets are tested against pathway databases, with topology-aware enrichment where useful.
Tools: clusterProfiler · Reactome
Prioritization
Network propagation and centrality rank the genes and links most worth validating experimentally.
Methods: HotNet2 · RWR · centrality
Visualization & reporting
Results become clear network figures and an interactive session, with version-locked code so it reproduces exactly.
Tools: Cytoscape · ggraph · renv
Tools & Technologies
Established, peer-reviewed tools—matched to your network
We select from the field's standard toolkit rather than forcing every dataset through one pipeline. A representative set of what we work with:
Network construction
STRINGWGCNAGENIE3ARACNeSCENIC
Graph analysis
igraphNetworkXgraph-toolLeiden
Pathway & enrichment
clusterProfilerg:ProfilerGSEAfgsea
Module detection
MCODELeidenClusterONECEMiTool
Propagation & prioritization
HotNet2RWRnetwork diffusionPCSF
Visualization
CytoscapeGephiggraphCytoscape.js
Integration & prior knowledge
OmniPathmixOmicsSTRINGdb
Languages & environments
R / BioconductorPythonrenvconda
Foundations
Reference databases we build on
Networks are only as good as the interactions behind them. We draw on established, versioned resources:
- STRING
- Curated and predicted protein-protein interactions with confidence scores.
- BioGRID
- Experimentally verified protein and genetic interactions across organisms.
- IntAct
- Molecular interaction data curated from literature and direct submission.
- Reactome
- Peer-reviewed, ordered pathway and reaction knowledgebase.
- KEGG
- Pathway maps for metabolism, signalling, and disease.
- WikiPathways
- Community-curated, openly editable biological pathways.
- Gene Ontology
- Standardised terms for molecular function, process, and location.
- MSigDB
- Curated gene sets and signatures for enrichment analysis.
- OmniPath
- Integrated prior knowledge of signalling, regulation, and interactions.
Choosing an Approach
PPI vs. co-expression vs. regulatory networks
Three network types answer different questions. We choose the one that fits your data—or combine them.
| Dimension | PPI network | Co-expression network | Regulatory network |
|---|---|---|---|
| Built from | Curated interaction databases | An expression matrix across samples | Expression, sometimes motifs/ChIP |
| An edge means | Physical/functional interaction | Correlated expression | Inferred regulation (directed) |
| Needs | A gene/protein list | Many samples (tens+) | Expression + prior knowledge |
| Best for | Contextualising hits, hubs | Modules, hub genes, trait links | Proposing regulators & targets |
What You Receive
A complete, documented deliverable
Not just a picture of a network—every file you need to explore, publish, and reproduce it.
- Network files you can open in Cytoscape (GraphML / session)
- Module & hub-gene tables with membership and scores (TSV / XLSX)
- Pathway & enrichment results for each module
- Node-level metrics—degree, centrality, prioritisation scores
- Publication-quality network figures (vector PDF / SVG / PNG)
- A written methods & results summary for your manuscript
- Reproducible code with every tool & database version recorded
A network is a model, not a mechanism
A network is a hypothesis-generating model, and we treat it as one. Edges are curated or inferred associations with differing evidence—not proof that two molecules physically interact or that one causes another. We report edge confidence and source, keep enrichment honest with an appropriate background and multiple-testing correction, and check that modules are robust rather than artefacts of a threshold.
The practical payoff: you get a defensible network that points to the genes and links worth testing next, with every tool and database version recorded so it reproduces exactly. And we will say plainly when your data is too small, or too few samples, for a co-expression or regulatory network to be reliable.
FAQ
Network biology questions
What researchers most often ask before starting a network project.
Usually a gene or protein list (for example, differentially expressed genes or hits from a screen), or an expression matrix for co-expression work. We can start from your omics results or from a curated list, and we pull interactions from trusted databases like STRING, BioGRID, and Reactome.
They answer different questions. A protein-protein interaction network shows curated physical or functional interactions; a co-expression network links genes that vary together across samples; a regulatory network infers directed transcription-factor-to-target relationships. We pick the type that fits your data and question, and sometimes combine them.
Yes. Weighted gene co-expression network analysis is a core method here — we build the co-expression network, detect modules, relate modules to traits or conditions, and identify hub genes, with the parameters and diagnostics documented.
Yes. Enrichment and network analysis complement each other: we test modules and gene sets against pathway databases (Reactome, KEGG, GO, MSigDB) and, where useful, use topology-aware methods rather than simple over-representation.
No, and we're careful about this. Edges are curated or inferred associations with varying evidence, not proof of a physical or causal mechanism. We report confidence and source, treat the network as a hypothesis-generating model, and recommend experimental validation for key links.
Network files you can open in Cytoscape (GraphML or a session), module and hub-gene tables, enrichment results, publication-quality figures, and reproducible code with every tool and database version recorded.
Related
Where networks connect
Have gene lists that need their networks?
Send us your gene or protein lists, or your expression matrix—we'll scope the network analysis and tell you honestly what it can and can't show.