Biography

I develop cutting-edge computational and experimental methods to design genetically encodable nanomachines, from fundamental principles to applications

I followed a double degree program in medical and biological sciences. In my motivation to explore and engineer the interface between fundamental and applied sciences, I was early interested by computational, quantitative and integrative approaches to biological sciences. I specialized first in molecular and cellular biology, then in biophysics and computational protein design.

During my PhD, I applied synthetic biology to human health and biotechnology. I developed concepts, systematic methods, computational tools and biological standards to:

1. Engineer next-generation diagnostics. I engineered autonomous and programmable biosensors integrating multiplexed pathological biomarker detection and complex biological signal processing as intelligent diagnostic devices.
2. Engineer novel biocomputing devices that solve complex problems and process/interface biological information at the microscale. I developed microfluidic and computer assisted methodologies to program these synthetic bio-systems from the bottom-up (e.g. protocells).

My Postdoctoral research focused on developing computational protein design methods powered by machine learning to design synthetic, self-assembling and genetically encodable nanomachines. As a proof of concept of rational design of dynamic and mechanical behavior within de novo protein nanostructures, I focused on the design of rotational mechanics from first principles. This work relies on the computational sculpting of the energy landscape of mechanically coupled de novo protein components in order to capture favorable Brownian fluctuations allowing to perform work.

I believe that collaboration between scientists and promotion of interdisciplinarity is the key to understand and engineer biology.