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Engineering Cell Fate with Adaptive Feedback Control.

Frank Britto Bisso1, Giulia Giordano2, Christian Cuba Samaniego1

  • 1Computational Biology Department, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States.

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|July 23, 2025
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Summary
This summary is machine-generated.

We developed a synthetic biology controller to guide stem cell differentiation, improving cell therapy potential. This adaptive controller favors specific cell fates, overcoming challenges in producing pure cell populations for regenerative medicine.

Keywords:
adaptive controlcell fatefeedback controlgenetic circuitsincoherent feedforward loopmultistability

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Area of Science:

  • Synthetic biology
  • Stem cell engineering
  • Biomolecular engineering

Background:

  • Stem cell therapies aim to replace damaged cells but face challenges in producing pure cell populations.
  • Current differentiation strategies often mimic embryonic development but yield mixed cell types, hindering scalability.
  • Synthetic biology offers potential solutions for increasing desired cell type yields through engineered gene circuits.

Purpose of the Study:

  • To design and analyze a synthetic biomolecular adaptive controller for engineering specific stem cell fates.
  • To create a controller that favors a desired cell fate with minimal disruption to endogenous regulatory networks.
  • To provide design guidelines for optimizing the controller's performance and applicability.

Main Methods:

  • Designed a synthetic controller with an Incoherent Feedforward Loop (IFFL) topology.
  • Utilized a sequestration mechanism and a time delay via an intermediate species for adaptive behavior.
  • Employed theoretical and computational analysis to study controller dynamics and performance.
  • Investigated the controller's ability to create a tunable synthetic bias toward a specific cell fate.

Main Results:

  • The controller exhibits adaptive, non-reference-based behavior, requiring minimal knowledge of endogenous networks.
  • The synthetic circuit's output approximates a discrete temporal derivative of its input under specific conditions.
  • The controller introduces a tunable bias favoring desired cell production with minimal impact on the overall equilibrium landscape.
  • Design guidelines for optimal operation and performance under perturbations were established.

Conclusions:

  • The biomolecular adaptive controller effectively engineers specific cell fates, addressing a key challenge in stem cell therapy.
  • This approach enhances the yield of desired cell types, paving the way for scalable cell-based regenerative medicine.
  • The controller's design principles are applicable to various multistable biological systems, broadening its potential impact.