Driving cellular processes with quantitative accuracy using real-time control approaches
Improving our capacity to control cellular processes can have a critical impact in a number of applications in biotechnology and medicine. Prominent application examples include microbial bio-production, human microbiota control, and stem-cell-based tissue developments. However, the functioning of biological systems of interest results from complex interactions between numerous highly-complex cells in a time-varying environment. It is therefore very difficult to quantitatively assess the long-term effects of control actions. Real-time control approaches may then prove effective to accurately drive cellular processes in absence of long-term predictive capacities. In this talk, I will present ongoing efforts to accurately control cellular processes despite the fact that existing molecular biology tools provide only limited observation and actuation capabilities on live cells. Two case studies will be presented. The first one deals with using model predictive control approaches for driving gene expression in yeast cells. The second one deals with using PI-based and even open-loop control approaches for controlling and balancing a genetic circuit acting like a genetic inverted pendulum.
INRIA, Saclay & Institut Pasteur, Paris