Control Engineering aims at designing and implementing algorithms for the automatic regulation of dynamical systems towards desired behaviors. The regulation of a room temperature, the car cruise control and the modern autopilot systems on vehicles are just few examples of Control Engineering applications in our daily life.

Usually control algorithms are implemented via the so called “negative feedback paradigm” by which the quantity to be controlled or the system output (e.g. the room temperature) is measured at regular time intervals and subtracted from the control objective or control reference (e.g. the desired temperature); this difference, called control error, is the quantity used by the controller (e.g. the thermostat) to calculate the control input (e.g. heater switched on or off) actually needed to minimize the control error at the following iteration of the algorithm itself.

In this talk I will introduce the details of a microfluidics based experimental platform designed and implemented to achieve the real time control of gene expression in living cells.

Furthermore, I will present a comparative analysis of different control algorithms implemented in-vivo on yeast cells, hence providing hints for the implementation of the negative feedback paradigm in general applications for which the tight regulation of gene expression (e.g. control of signaling pathways, bio-signal generation, dose-response promoter characterization) despite environmental disturbances and physical limitations affecting the actuation can be desirable.

Moreover, I will describe the extension of the implementation of the negative feedback in microfluidics to mammalian cells and, the motivations at the basis of the intriguing challenge of designing embedded cellular controllers.