Biological computation and architecture
Study the organization of biological computers as a systems problem: control, memory, signaling, I/O, routing, and computation-inspired design.
Research agenda
A responsible program for biological computation and whole-cell modeling.
BioCompLab treats living systems as programmable, modelable substrates only at the level supported by evidence: vision, hypothesis, model, simulation, prototype, experiment, and validated result are kept distinct.
Deterministic biochemical networks
Represent reaction networks with ODEs, kinetic laws, and steady-state analyses for didactic and research-facing biochemical models.
Metabolic and flux-based models
Use flux balance analysis and metabolic projections to connect abstract pathways with the state variables used by the whole-cell models.
Stochastic molecular dynamics
Model low-copy-number molecular events with stochastic reaction rules, stochastic transcription, and related discrete processes.
Whole-cell and compartment-aware assembly
Compose shared-state models that coordinate metabolism, compartments, growth, cell cycle, and division logic in one event-driven workflow.
AI-assisted scientific workflows
Use AI for literature triage, provenance-aware knowledge organization, model assembly support, and research communication workflows.
Modeling stack
Continuous
ODE biochemical networks, kinetic laws, and deterministic pathway dynamics.
Discrete
Stochastic molecular events, transcription, translation, and reaction rules.
Constraint-based
Flux balance analysis, metabolic optimization, and pathway projections.
Hybrid whole-cell
Shared state, compartments, growth, checkpoints, and division events.
Evidence discipline
Keep vision, hypothesis, model, simulation, prototype, experiment, and validated result distinct.
Treat provenance and implementation maturity as first-class metadata.
Prefer modular, reusable, and inspectable components over monolithic claims.
Avoid unsafe wet-lab operational guidance and avoid implying validated capability where only a model exists.
How the lab works
1. Define
Choose the biological question, the abstraction level, and the evidence threshold.
2. Model
Build the system with clear state definitions, compartments, and process boundaries.
3. Simulate
Run deterministic, stochastic, or hybrid workflows and record assumptions explicitly.
4. Validate
Compare with available literature, data, or expert review and label maturity honestly.
Scientific communication principles
Do not blur model status with biological validation.
Do not present hypothetical systems as established technologies.
Keep biosafety, licensing, and attribution visible in the portal.
Use explicit provenance for publications, software, and datasets.
Reference base
Material now reflected in the site
Whole-cell modeling guides for prokaryotic and eukaryotic assembly
Biochemical and metabolic modeling reports
Research project proposal and final report material
Course materials and educational modeling references
Publications and software artifacts with provenance