Active Cells, Biochemistry & Physiology Plants, Animals & Ecology

Combo: Control-based biodesign of mammalian cell dynamics

In plain English

AI plain-English summary

Cells are being programmed like machines to follow precise instructions, using real-time feedback control to override biological noise and unpredictability. This matters because living cells are inherently messy. Their internal networks are full of feedback loops that make behaviour hard to predict, and current methods for engineering cells—whether for stem cell therapies or drug responses—struggle with variability. Mathematical models help but are limited by noisy data and uncertain parameters. This project sidesteps those models entirely, using external feedback control and a microfluidics platform to observe and adjust cells in real time, directly programming complex dynamics without needing to fully understand every internal detail. If successful, the approach could transform stem cell culture protocols, making them more reliable across different scales—a practical bottleneck in regenerative medicine. It could also design optimal drug dosing schedules for cancer cells, tailoring timing rather than just dosage. The work is fundamental in nature, combining control theory with synthetic and stem cell biology, but its tools could eventually underpin more predictable manufacturing of cell-based therapies and smarter, feedback-driven drug delivery systems.

View original technical description
Systems Biologists, by combining cell biology with mathematical approaches, have shown that feedback loops in molecular regulatory networks tightly control cellular homeostasis and responses. The interplay between endogenous feedbacks and the extracellular environment results in complex and non-linear cellular dynamics. Mathematical models can help in tackling this complexity, aiding in characterising the links between cellular dynamics and cell-decision making. However, the validity of models relies on modelling assumptions and the quality of data used for parameter fitting: stochasticity and noise limit the power of model predictions across Systems Biology and Systems Pharmacology applications. Conversely, the forward engineering of exogenous gene expression dynamics that recapitulate native cellular behaviours, often used by Synthetic Biologists, is limited by poor robustness to physical parameter variations, diverse modular parts and choice of chassis. To tackle these challenges, this Fellowship proposes to directly and automatically program complex dynamics in mammalian cells, by combining external feedback control to ensure robustness and a microfluidics/microscopy platform to observe and perturb cells in real-time. Exploitation of this technology will allow to: i) Unravel causation in coupled processes and dissect the role that temporal patterns across scales (i.e. gene expression dynamics and cell-cycle) play in stem cell fate, ultimately exploiting such dynamics for the design of superior stem cell culture protocols. ii) Directly track from experiments non-linear biochemical dynamics, without the need of mathematical models, to quantitatively determine causes/robustness of complex native/engineered behaviours, respectively, using experimental and Control-Based Continuation. Direct industrial applications will be explored, including the characterisation of stem cell culture protocols across culture scales, and the use of feedback control to design optimal drug dosing schedules for target cancer cell responses. Our aims are underpinned by two highly synergetic research tracks at the interface of interdisciplinary disciplines. The combination of methodologies from control theory, Synthetic, Systems and Stem cell biology will provide a quantitative framework and highly novel tools to understand, steer and design mammalian cell dynamic phenotypes, with great potential for future therapeutic purposes.

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Researchers

Lucia Marucci (Principal Investigator)

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Original classification

Fellowship

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