The tech stack combines modern frontend technologies with robust backend architecture. The frontend uses Next.js 14 with TypeScript and Cytoscape.js for the visualization engine. The backend is built with FastAPI and Python.
The featured demo showcases a Traumatic Brain Injury Nasal Spray mechanism of action visualization, demonstrating the tool's capability to handle complex biological pathway mapping.
You can explore the live demo at <https://nodes.bio> to see the TBI Nasal Spray visualization in action, along with other biological network examples.
I'd love feedback on the visualization capabilities or any suggestions for biological data integration. What do you think?
The platform's biological network graph system (/api/v3/graph) allows users to create nodes representing proteins, genes, receptors, and enzymes, then define the complex relationships between these entities through edges that represent molecular interactions such as binding, activation, and inhibition. This isn't merely data visualization—it's a living, breathing representation of biological systems that responds to real-time updates and modifications.
TODO: Swagger documentation on the API
TBI Nasal Spray Mechanism - Shows molecular pathways and drug delivery mechanisms for traumatic brain injury treatment.
Biological Network Introduction - Demonstrates protein-protein interactions, signaling pathways, and cellular communication systems.
Innovation Pipeline Network - Visualizes the research-to-commercialization pipeline, mapping connections from scientific discoveries to market applications.
The full interactive experience is available on desktop for advanced features and larger networks.
Thanks everyone for the feedback about adding compelling demo screenshots - this gives mobile visitors a clear sense of what the tool can visualize across different biological domains.
To me, Cytoscape is simply outdated and an arbitrary constraint on the software, a relic of network science history.
The deeper problem though is how valuable is network data visualization from an epistemological standpoint? Pretty useless from my experiments besides for a kind of scientism performance art. That is not to say scientistic performance art data viz can not be lucrative in a post modern scientism society.
Take this with a grain of salt because biological networks are never going to be my strong point. There is this huge node data viz scaling problem I don't think has been solved and I am kind of waiting for this to be solved with biological network so I can just rip off the idea.
I used to share some of your skepticism — a lot of network visualizations do feel like scientistic performance art. That started to shift for me after discovering NFX and reading The Cold Start Problem by Andrew Chen. I began thinking more seriously about network effects when I applied for my first U.S. patent after leaving JP Morgan Chase, where I spent 14 years in technology, leadership, and innovation. That experience led to the early ideas behind Nodes.bio.
I do rely on Cytoscape.js as a rendering engine — so I want to be transparent about that. But I’ve moved deliberately away from the legacy metaphors and plugin model of desktop Cytoscape. Nodes.bio is browser-native, install-free, and designed for speed, storytelling, and shareability. It's built for researchers, founders, investors, and even patients — not just PhD bioinformaticians.
I understand the “performance art” critique, but I’ve seen what happens when visualization actually delivers insight. One of my early users, Dr. Patrick Sewell, a clinical geneticist, has gone on record saying:
“Nodes.bio transformed my hand-drawn network into a publication-quality, interactive diagram in minutes—something I simply couldn't have achieved with any other tool.”
And even from a utilitarian standpoint — forget truth claims, just ask: does this help people make better decisions, faster, with fewer errors? From what I’ve seen: yes. Visual structure helps surface non-obvious connections, prioritize experiments, flag off-target effects, and bridge gaps between data producers and decision makers. That’s reason enough to keep going.
As for scaling: I’m running on AWS ECR/EB, so infrastructure is solid. The real challenge is cognitive scaling — how to keep massive biological networks legible and meaningful. That’s where I’m focusing next.
I’ll also be publishing the Nodes.bio APIs in the near future — because the value here isn’t just in pretty diagrams, it’s in enabling others to build, extend, and integrate with their own data pipelines, dashboards, or discovery workflows.
Appreciate the challenge. If you do decide to build in this space, I’d love to trade notes!