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Engineering Cybergenetic Cell-Based Therapies
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Top 1% · Validated by QED

Engineering Cybergenetic Cell-Based Therapies

Ching-Hsiang Chang, Asterios Arampatzis, Samuel Balula +5 more
January 2026
 ·
bioRxiv
 ·
DOI 
10.64898/2026.01.16.699922
94
%
Reproducible
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It ranks in the top 1% of preprints assessed in Synthetic Biology this quarter.
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Why this is interesting

Closed-loop cell therapies were previously impossible to design rationally; nullgram profiling and Cyberpatient-in-the-loop now make self-correcting, inflammation-regulating cell therapies predictable, testable, and deployable, opening a new engineering paradigm for adaptive medicine.

QED claims analysis

Summary

Multi-dimensional nullgram profiling and Cyberpatient-in-the-loop testing enable rational design of closed-loop cell-based therapies, revealing that dimeric v2-buffer controller topology overcomes high-setpoint limitations of monomeric controllers through elimination of dominant-negative splice products, thereby achieving robust autonomous regulation of TNF-α and downstream inflammatory cytokines in primary human immune cells.

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Main Claim 1 –

Nullgram Profiling Predicts Steady-State Regimes Without Closed-Loop Assembly

Main Claim 2 –

Cyberpatient-in-the-Loop Tests Controllers in Disease-Relevant Dynamic Environments

Supporting evidence

  • NF-κB Nonlinearity and Dominant-Negative Splice Products Reduce Sensitivity
Main Claim 3 –

v2-Buffer Preserves Sensitivity; v1 Fails via DNA-Binding Splice Products

Supporting evidence

  • v2-Buffer Regulates at High Setpoints; v1 Collapses Dynamically
  • Only v2-Buffer Maintains High-Setpoint TNF-α Control in Co-Culture
Main Claim 4 –

v2-Buffer Suppresses TNF-α and Downstream Cytokines in PBMCs

Supporting evidence

  • v2-Buffer Tracks Tunable Setpoints Across Eightfold Range Under Disturbances

Abstract

Adaptive, closed-loop control of cellular behavior is essential for next-generation therapies, yet most current treatments operate in an open-loop manner and lack robustness to patient variability and disease dynamics. Here, we establish a controltheoretic platform for rational engineering of closed-loop cell-based therapies that achieve precise and robust regulation. First, we introduce multi-dimensional nullgram profiling, a high-throughput approach that enables quantitative prediction and design of advanced genetic controllers in human cells across circuit topologies and parameter regimes in a single experiment. To evaluate dynamic therapeutic behavior, we next develop Cyberpatient-in-the-loop, an optogenetic digital twin platform that interfaces engineered mammalian cells with computational disease models, enabling systematic testing of closed-loop performance under realistic perturbations. Finally, we leverage these approaches to implement integral feedback cell therapies that sense inflammatory signals and autonomously regulate cytokine levels in primary immune cell cultures. Together, these results establish a general paradigm for engineering cellbased control systems and provide a foundation for next-generation cell therapies.

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