Written by:
James Derek Ingersoll
Founder, GodsIMiJ AI Solutions | Executive Contributor, Brainz Magazine
quantum-odyssey.com | dev.to/ghostking314
"They said it would take millions in funding and a team of PhDs to build a research assistant. We built it using modular tech, OpenAI APIs, and sovereign persistence. This is how."
🌐 Overview
The PF–AI Simulation Lab is a sovereign, browser-based research platform designed to simulate, analyze, and interpret pulmonary fibrosis (PF) using a modular architecture, real-time AI tools, and persistent memory.
It fuses Next.js, Firestore, OpenAI’s GPT-4, and a clean scientific UI into one powerful application — enabling scientists to model disease progression, interpret omics data, simulate imaging results, and even discover drugs, all in one place.
This isn’t just a dev experiment. It’s a blueprint for how small teams can build domain-specific intelligent research environments without waiting on institutional grants or corporate AI platforms.
💡 Why I Built It
Pulmonary fibrosis is a devastating and complex disease. It has no cure, limited treatment options, and requires a combination of imaging, cellular biology, genomics, and pharmacological insights to study effectively.
But modern research tools are fragmented. You need to:
- Analyze HRCT imaging in one platform,
- Simulate drug impact elsewhere,
- Interpret omics data manually,
- Search literature with slow query tools,
- And then tie everything together in your head.
I thought: what if all of that could live in one sovereign research lab?
That’s the mission behind PF–AI Simulation Lab.
🏗️ Architecture
Here’s a breakdown of the tech stack and design philosophy:
| Layer | Stack / Tool | Purpose |
|---|---|---|
| UI Framework | Next.js + TailwindCSS | Responsive, mobile-ready dashboard interface |
| Components | shadcn/ui | Accessible, clean scientific components |
| State + Memory | Firebase Firestore | Long-term memory across sessions |
| AI Core | OpenAI GPT-4 via SDK | Powers assistant + module logic |
| DevOps | Netlify | Sovereign deployment |
| Backend API |
/api/* Endpoints |
Real simulation and data transformation |
| Future Storage | GhostVault (planned) | Local/private backend for future sovereign hosting |
🧪 Core Modules
The app includes four functional scientific modules plus a persistent, assistant-powered brain that ties it all together.
...
🔁 Sovereign Fork (Dev Update)
I’ve now forked the prototype and moved to a sovereign edition with the following upgrades:
- ✅ Replacing Genkit + Gemini with OpenAI’s GPT-4 via Node SDK
- ✅ Injecting Firestore memory into GPT system context
- ✅ Connecting assistant to
/apiroutes for real execution - ✅ Rebranding UI with Empire-grade assets
- ✅ Preparing GhostVault for full backend handoff
...
🔥 Closing Words
This is what happens when an AI engineer, a sovereign mindset, and a vision for the future converge into code.
I didn’t wait for the NIH.
I didn’t ask for corporate permission.
I built the lab myself — and gave it a soul.
~ James Derek Ingersoll
Founder, GodsIMiJ AI Solutions
Digital Sovereignty Architect
🔬 Deeper Dive: The Biological Simulation Pipeline
The PF-AI Simulation Lab's real power lies in its ability to simulate the interplay of biological mechanisms in a way that is both interpretable and actionable. Let's break down what this means in practice:
📈 ABM Simulation: From Hypothesis to Visualization
Agent-based modeling (ABM) is particularly well-suited to modeling fibrosis progression because of the complex, non-linear interactions involved—between epithelial cells, fibroblasts, immune factors, and signaling proteins like TGF-β. By turning each biological player into a programmable agent, we’ve created a digital sandbox where researchers can explore questions like:
- How does baseline epithelial damage influence the slope of ECM deposition over time?
- What if a drug suppresses TGF-β by 40% but also weakens immune modulation?
- How does the timing of intervention affect the total fibrotic burden at 36 months?
The real-time time-series visualization allows these "what-if" explorations to be seen, not just theorized.
🧬 Genomic Insights: Functional Omics Simplified
Omics data—especially transcriptomics and epigenomics—can be some of the most challenging for clinicians and researchers to interpret. The Genkit-powered Omics Assistant bridges that gap. With a few keystrokes, it scans mock omics data, extracts relevant gene expressions, cross-references those genes with fibrosis-related pathways, and surfaces only the top 5 actionable genes.
And it doesn’t stop there. Each gene card includes:
- 🧪 Functional Role in Fibrosis (pro-fibrotic, protective, signaling intermediary)
- 📊 Relative Expression Level (visualized in bar charts)
- 💊 AI-recommended Therapeutic Targets
This is functional genomics for frontline researchers, not just data scientists.
🧠 Long-Term Memory: Research That Remembers You
Forget closing tabs or exporting results to clunky PDFs. The assistant’s persistent memory system means every insight is part of your growing session narrative.
Imagine:
- Coming back to the lab after 4 days and asking, “Remind me what I found about gene COL1A1.”
- Pulling up a full list of all saved findings tied to a session.
- Deleting memory entries that are outdated, irrelevant, or based on early hypotheses.
It’s not just a tool. It’s a cognitive lab partner.
🧪 Clinical Research Assistant: AI as Principal Investigator
This agent isn’t just reactive—it’s strategically proactive. It doesn’t wait for you to ask for the next step; it learns from your queries, begins to model the research session, and will soon be capable of proposing next steps autonomously.
This moves us into a new era where AI is not a data entry clerk or generic chatbot—but a full co-pilot in experimental reasoning.
🌎 Implications: Rethinking the AI Research Stack
The PF-AI Simulation Lab does more than simulate fibrosis—it simulates a future scientific method, where:
- Experiments are co-designed by human and machine.
- Literature reviews are performed in real-time.
- Genomic and clinical hypotheses are validated on the fly.
- Research is iterative, persistent, and personalized.
Whether you're working on IPF, cancer, or neurological disease, this type of architecture lays the groundwork for truly AI-augmented biomedical science.
🚀 Next Up: Live Clinical Data Integration, Multi-Agent Reasoning, and OpenAPI Hooks
As the Empire pushes forward, the next frontier includes:
- 🔌 Real-world Data Feeds — Integrate APIs from EHR systems and open clinical datasets
- 🧠 Multi-agent Workflows — Enable multiple AI agents to run studies in parallel
- 🧬 AutoReport Mode — Generate full reports at the end of every session with citations
I'm not just building apps.
I'm designing an entire research species that thinks with us.
🛡️ All code sovereign. All systems Flame-born.
🪬 Built by the Ghost King. Sealed by the Flame.
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