Research Program
The Généra research program is anchored in “Tissue Studies”, a rigorous fusion of bioengineering and the philosophy of technology. We assert that how we conceptualize living matter dictates how we can construct it.
Theoretical Frameworks
Tissue Engineering Triangle
A framework delineating the morphic, hylomorphic, and hylic regimes of tissue construction. It provides a topological map for understanding how biological matter is constrained and guided from raw biomaterials (hylic) through structural scaffolding (hylomorphic) to autonomous generative form (morphic).
Periodic Table of Tissue Engineering
A cartography of methods and formalisms in tissue engineering. By classifying techniques according to their underlying physical and biological operations, we reveal structural gaps in the literature—pointing the way toward novel methodological discoveries rather than incremental optimizations.
Universal Tissue Engineering Machine (UTEM)
Our core vision: defining the theoretical limits and necessary abstractions required to instantiate a substrate-independent platform for tissue generation. The UTEM represents the unification of the Triangle and the Map into a practically deployable architectural paradigm.
Methods & Constraints
We reject the 'black box' approach of brute-force machine learning in favor of interpretable, mechanistic models. Validation occurs via scaffold-free spheroid fusion models and microfluidic gradient chambers.
Strategic Goals
- Formalize the mathematical abstractions for the UTEM.
- Develop open-source hardware tools for hylic intervention.
- Establish standards for reporting epistemological assumptions in biomaterials papers.
- Create modular, substrate-independent algorithms for tissue patterning.
Methodological Axioms
Computational Rigor: We utilize category theory and complex systems modeling to define limits of form.
Wet Lab Validation: Validation occurs via scaffold-free spheroid fusion models and microfluidic gradient chambers.
Interpretability Constraint: We reject the “black box” approach of brute-force machine learning in favor of interpretable, mechanistic models.