Regulation of Gene Expression and Signaling Pathway Activity in Mammalian Cells by Automated Microfluidics Feedback Control

Lorena Postiglione, Sara Napolitano, Elisa Pedone, Dan Rocca, Francesco Aulicino, Marco Santorelli, Barbara Tumaini, Lucia Marucci, Diego di Bernardo

Research output: Contribution to journalArticle (Academic Journal)peer-review

35 Citations (Scopus)
803 Downloads (Pure)


Gene networks and signaling pathways display complex topologies and, as a result, complex nonlinear behaviors. Accumulating evidence shows that both static (concentration) and dynamical (rate-of-change) features of transcription factors, ligands and environmental stimuli control downstream processes and ultimately cellular functions. Currently, however, methods to generate stimuli with the desired features to probe cell response are still lacking. Here, combining tools from Control Engineering and Synthetic Biology (cybergenetics), we propose a simple and cost-effective microfluidics-based platform to precisely regulate gene expression and signaling pathway activity in mammalian cells by means of real-time feedback control. We show that this platform allows (i) to automatically regulate gene expression from inducible promoters in different cell types, including mouse embryonic stem cells; (ii) to precisely regulate the activity of the mTOR signaling pathway in single cells; (iii) to build a biohybrid oscillator in single embryonic stem cells by interfacing biological parts with virtual in silico counterparts. Ultimately, this platform can be used to probe gene networks and signaling pathways to understand how they process static and dynamic features of specific stimuli, as well as for the rapid prototyping of synthetic circuits for biotechnology and biomedical purposes.
Original languageEnglish
Pages (from-to)2558-2565
Number of pages8
JournalACS Synthetic Biology
Issue number11
Early online date11 Oct 2018
Publication statusPublished - 16 Nov 2018

Structured keywords

  • BrisSynBio
  • Bristol BioDesign Institute
  • Engineering Mathematics Research Group


  • Synthetic Biology


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