PART COLLECTION

PHOENICS

Sensing Modules

Synthetic GPCRs and MESA receptors convert extracellular signals into phosphorylation events. Dual receptor design enables stimulatory kinase and inhibitory phosphatase activity for complex logic.

Processing Core

Modular kinases, phosphatases, and substrates perform immune-like logic integration. Fast, reversible phosphorylation dynamics enable precise threshold-based switching and real-time adaptation.

Response Systems

Rapid secretion platform and transcriptional activators translate computed signals into action. Pre-translated effectors enable immediate therapeutic protein release at the tumor site.

System Concept

The collection is modeled after the principles of a synthetic immune cell. A modular unit that perceives extracellular signals, computes them through intracellular logic, and executes tailored functions.

Explore our part collection here!

Sensing

Our receptor layer converts diverse extracellular cues, including small molecules, soluble proteins, and surface-bound antigens, into phosphorylation events using synthetic GPCRs and MESA receptors. A key innovation is the dual receptor concept: Stimulatory receptors drive kinase activity, while inhibitory receptors activate phosphatases.

Processing

The intracellular core performs immune-like logic integration, computing the opposing effects of kinase and phosphatase inputs on a shared substrate. This phosphorylation logic operates with fast, reversible kinetics, unlike transcriptional circuits that rely on slower protein expression, and ensures that switching occurs only once a defined phosphorylation threshold is reached. This translates to dynamic and precise adaptation to the rapidly changing tumor environment.

Responding

Upon activation, PHOENICS translates the computed signal into therapeutic action. Beyond expression-based effectors, the central innovation is the rapid secretion platform. Effector proteins are pre-synthesized, retained in the ER, and released only upon circuit activation. This minimizes delay and provides immediate secretion of therapeutic proteins directly at the tumor site.

Integration with SPARC

The collection was co-developed with SPARC (Simulator for Phosphorylation and Receptor Characterization), a computational framework that accelerates circuit design through the integration of iterative wet-lab and dry-lab processes. SPARC combines deep learning–based de novo binder design with physics-based molecular dynamics (MD) simulations to prototype new protein binders, receptor architectures, and phosphorylation circuits in silico. This modeling pipeline provides a pre-screening metric for binder affinity and receptor performance, guiding experimental validation and reducing the need for iterative testing.

Validated binders and receptor designs were experimentally confirmed in mammalian cells, closing the design–build–test–learn loop and demonstrating the predictive accuracy of our computational models. These data are incorporated into SPARC’s mathematical model of receptor dimerization and intracellular phosphorylation, which predicts circuit behavior and ligand sensitivity across different part combinations. The integration paves the way for streamlining the development of complex phosphorylation circuits and ensuring that each addition to the library is both computationally optimized and biologically verified.