Digital Research Lab for Systems Biology & Clinical Decision-Support

BBTech is a software-native digital research lab that uses basketball analytics as a structured modeling language for biological and clinical research. We translate game concepts—players, archetypes, playbooks, seasons—into formal models, simulations, and decision tools for real-world biomedical questions.

Mission: Build a Basketball-to-Biotech OS that makes complex systems biology accessible, reproducible, and actionable.

• Basketball as Biological Language: Elite players become disease archetypes (Curry = virus, Jordan = cancer, Draymond = immune system), making complex biology intuitive
• Reproducible Protocols: The Playbook Series provides lab-manual-style system designs anyone can implement
• Built-In Provenance: Every experiment is ledger-logged with tx_hash and parameters for full auditability
• Autonomous Capable: Agent-based infrastructure designed for 24/7 research operation
• Clinically Grounded: CIS and GenomeOS interfaces turn research into patient-facing tools

Biological models mapped to elite basketball players

• Steph Curry – Viral Archetype (infection dynamics, R₀, viral load, immune escape)
• Kyrie Irving – Mutation Archetype (adaptation under stress, defensive evasion, paint infiltration)
• Michael Jordan – Malignant System (clonal expansion, host takeover, metastasis)
• Nikola Jokić – CNS/Endocrine Hub (network control, hormonal distribution, latency)
• LeBron James – Master Regulator/Stem Cell (plasticity, differentiation, regulator impact)
• Draymond Green – T-Cell/Immune Orchestrator (coordination, cytokine bursts, autoimmune risk)
• Dennis Rodman – Macrophage/Resource Recycler (phagocytosis, recycling, inflammation)
• Giannis Antetokounmpo – Invasive Species (barrier breach, metastatic reach, structural deformation)
• Harden/Luka – Rule-Exploiting Pathogen (exploit efficiency, rulebook sensitivity, entropy manipulation)

Each archetype includes:

• Biological label and clinical mapping
• Basketball phenotype
• Complete metric suite with formulas
• Narrative arc from emergence to ecosystem impact

→ Read Full Pathogen Archetypes Doc

Reproducible system protocols for basketball-to-biotech research

A library of canonical experimental designs, each structured like a lab manual:

• Volume 1: Viral Systems – Infection dynamics, viral gravity, endemic shifts
• Volume 2: Mutations & Micro-Mobility – Adaptation, evasion, stress testing
• Volume 3: Malignancies & Takeovers – Clonal expansion, host reorganization, metastasis
• Volume 4: Immune & Defensive Systems – Coordination, phagocytosis, cytokine cascades
• Volume 5: Control Towers & Endocrine Hubs – Network control, plasticity, master regulators
• Volume 6-7 (Future): Rule-exploiting pathogens, invasive species

Each Playbook System includes:

1. System overview & biological mapping
2. Required archetypes & parameter ranges
3. Metric formulas & Codex integration
4. Simulation design (time scale, state variables, update rules)
5. Experiment templates
6. Data requirements & validation criteria

Example: Viral Gravity Offense
Models how a single deep-range shooter (Curry-type virus) reshapes an offensive ecosystem via infection dynamics. Maps to treatment-resistant mutation spread in tumors.

→ Read Full Playbook Series Doc

Fundable research infrastructure

Layer 1: Statistical Translation Engine (The Analyst)

• Ingests biological data → outputs Basketball Codex metrics
• Metrics: TER, Trueness, Flow, Gravity, R₀, SVI, archetype-specific
• Tech: Data pipelines, Naive Bayes, PK models, spatial stats

Layer 2: Strategic Optimization Core (The Coach)

• Simulation & game-theory engines
• Stackelberg solvers, agent-based models, swarm optimizers
• Designs therapy "game plans" from Playbook Systems

Layer 3: Public & Clinical Interfaces (The Arena)

• CIS (Cancer Information System)
• GenomeOS
• Dashboards for patients, clinicians, researchers

1. Adaptive Therapy & Evolution – Optimal drug "lineups" to prevent resistance
2. Spatial Ecology – Immune/tumor spatial dynamics ("guarding space")
3. Patient Engagement – Gamified metrics (DRtg, Pace, XP, seasons)

• Phase 0 (Complete): Foundational platform with Analytics Engine, BioBrief API, ledger
• Phase 1 (2026 Q2-Q3): 90-day clinical/biotech pilots
• Phase 2 (2026 Q4): Lab formalization with steering committee, IRB pathways
• Phase 3 (2027+): Expansion fundraise, hire 2-4 staff, new disease areas

→ Read Full Digital Lab Doc

24/7 research infrastructure

• Researcher Agent (Scout) – Monitors literature, proposes new archetypes
• Data Engineer Agent (Stat Crew) – Ingests, validates, computes Codex metrics
• Simulation Agent (Head Coach) – Runs Playbook experiments, optimizes strategies
• Referee Agent – Enforces safety, validates outputs, audits ledger
• Operations Agent (GM) – Monitors infra, scales resources, manages costs

1. SENSE – Detect triggers (new data, scheduled slot, partner question)
2. PLAN – Select Playbook, generate experiment spec
3. ACT – Clean data, run simulations, compute metrics
4. EVALUATE – Validate outputs, check for PHI leaks, verify ledger
5. REPORT – Draft BioBrief, update Playbooks, surface to CIS/GenomeOS
6. LEARN – Capture feedback, refine agents and Playbooks

• Phase 1 (Target): Semi-autonomous (agents propose, humans approve)
• Phase 2: Supervised autonomous (agents execute, humans review)
• Phase 3: Fully autonomous research mode (24/7 operation)
• Phase 4: Clinical deployment (real-time therapy optimization)

• $0.52-5.11 per experiment (compute + storage)
• Break-even at 200-1000 experiments/month vs. human researchers
• 10-100x more cost-effective at scale

→ Read Full Agent Stack Doc

• Analytics Engine – Core BBTech compute platform
• Cancer Treatment – Basketball analogy metrics for oncology (TER, Four Factors mapped to proliferation/clearance/resource/metastasis)
• Genetic Coach – Algorithmic coaching staff model
• Formula Integration – Codex metric definitions and calculators

• Backend – Oncology analytics platform with ledger integration
• BB Extension – Browser extension for data capture
• Basic Platform Frontend – Example UI implementations

• Pilot and Verification Flow – 90-day partner engagement protocol
• Commercial Tools – Analyst/Coach/Arena product descriptions
• Coding Outline – GenomeOS and Genetic Coach architecture

Core measurements that apply across archetypes and Playbooks:

• TER (Tumor Efficiency Rating) – Basketball PER adapted for malignancy scoring
• Trueness – Signal accuracy of metric outputs
• Flow – System tempo and possession quality
• Gravity – Spatial disruption and attention capture
• Four Factors – Proliferation, Clearance, Resource Capture, Metastasis
• DRtg / ORtg – Defensive and offensive system health
• Pace – Cell-cycle or possession timing
• Clutch Performance – High-pressure environment response

Every computation writes to an immutable ledger:

• tx_hash – Unique identifier
• Payload & parameters
• Compute time & agent/user ID
• Input/output hashes

Allows full experiment replay and audit trails for funders/regulators.

• Choose a Playbook System matching your biological question
• Implement simulation following provided update rules
• Run experiments and log results in standard format
• Compare to validation criteria and contribute back to BBTech

• Map patient data onto archetype parameters
• Run simulations to predict treatment response
• Use CIS/GenomeOS interfaces for patient-facing explanations
• Track outcomes and refine models

• License Playbook Systems for internal decision-support
• Run 90-day pilots for specific targets or indications
• Generate BioBriefs as structured research reports
• Clear ROI: 25-35% prioritization speed improvement target

• Teach systems biology using familiar sports language
• Assign Playbook Systems as lab exercises
• Students implement simulations and present findings

1. Pathogen Archetypes – Understand the biological-player mappings
2. The Playbook Series – Browse reproducible system protocols
3. BBTech Digital Lab – Learn about lab structure and funding
4. Autonomous Agent Stack – See the 24/7 infrastructure design

We have converted the conceptual metagame specifications into a fully functioning, production-ready Python package containing the entire analytical, clinical, agent-based, and game-theoretic codebase!

To run a full demonstration of the computational research pipeline (including Tumor Efficiency calculations, Dean Oliver's Four Factors, Voronoi tessellation, evolutionary agent rosters, and Lotka-Volterra Stackelberg adaptive therapy vs MTD simulations):

1. Install Dependencies:

   pip install -r requirements.txt

2. Execute the Integrated Diagnostic Suite:

   python run_demo.py

This will run the entire codebase end-to-end and display dynamic therapeutic results, secure on-chain Polygon transaction hashes, and autonomous triage logs!

• Academic researchers: Collaborate on pilot studies
• Biotech/pharma: License Playbook Systems or run custom pilots
• Clinicians: Deploy CIS/GenomeOS interfaces
• Funders: Support digital lab expansion (grants, venture, partnerships)

Contact: (details to be added)

• Complete Volume 1 (Viral Systems) and Volume 3 (Malignancies) of Playbook Series
• Run first clinical/biotech pilot (90-day model)
• Build Researcher, Data Engineer, Coach agent prototypes

• Complete Volume 2 (Mutations) and Volume 4 (Immune Systems)
• Formalize BBTech as named digital lab (steering committee, IRB pathways)
• Integrate agents via orchestration layer

• Complete Volume 5 (Control Towers)
• Deploy semi-autonomous agent stack
• Production scaling: 1000+ experiments/month

• Volumes 6-7 and custom Playbook development
• Fully autonomous research mode
• Clinical integration pilots

BBTech welcomes contributions:

• New Playbook Systems within existing volumes
• Validation studies using different datasets
• Implementations in different languages/frameworks
• Visualizations and dashboards
• Bug reports and parameter refinements

See CONTRIBUTING.md for guidelines.

Apache 2.0 (see LICENSE file)

Commercial Use:

• Core Playbook Systems: Open-source for academic/non-commercial research
• Advanced/clinical systems: Available to research partners
• BioBrief generation and CIS/GenomeOS: Licensing available

✅ Reproducible by Design – Ledger-backed provenance for every experiment
✅ Transparent Metrics – Explicit formulas and validation criteria for all measurements
✅ Accessible Language – Basketball metaphors make systems biology intuitive
✅ Fast Iteration – 90-day pilots vs. multi-year traditional studies
✅ Scalable Infrastructure – Software-native, low marginal cost per experiment
✅ Autonomous Ready – Agent stack designed for 24/7 operation

Current Phase: Platform operational, pilot-ready, agent stack in design

Active Development:

• Pathogen Archetypes catalog (9 archetypes defined)
• The Playbook Series (Volumes 1-5 planned, examples drafted)
• Autonomous Agent Stack (Scout, Stat Crew, Coach, Referee, GM agents specified)

Production Ready:

• Analytics Engine
• Cancer Treatment mapping
• BioBrief generation
• Ledger integration
• 90-day pilot protocol

If you use BBTech in your research, please cite:

BBTech: Basketball-to-Biotech Translation Framework
Digital Research Lab, 2026
https://github.com/ncsound919/BB-Tech

BBTech transforms basketball analytics into a rigorous, reproducible, and fundable research platform for systems biology and clinical decision-support. From Curry's viral spread to Jordan's malignant takeover, we make complex adaptive systems intuitive, auditable, and actionable.

Ready to play? Explore the Playbooks and start your first experiment.