Abstract:
The ability to program ad hoc cells and biological processes offers exciting opportunities in basic research, in the biotechnology industry and in the clinic. Computer-aided design can significantly accelerate design-build-test-learn cycles for cellular programming; however, the lack of established design tools which can cover biological functions across scales, and difficulties in engineering systems resilient to changes and perturbations, still represent major roadblocks. In this talk, I will present two complementary approaches to rationally and robustly program cell phenotypes. I will first discuss how computer-aided cell design can be supported by whole-cell models (WCMs), which are mathematical models aimed at capturing the function of all genes and multiscale processes within a cell. The design of minimal bacterial genomes will be used as a proof-of-concept; I will also show how machine learning can support WCMs’ output interpretation and solve their computational burden challenge. The second approach leverages feedback control to engineer robust cellular phenotypes. I will show results obtained using intracellular, external or multicellular controllers in both bacterial and mammalian cells, and diverse applications of cybergenetics methodologies (e.g., control-based analysis of gene networks’ dynamics, and drug combination therapy design). Our tools and results will assist the engineering of complex biological processes, bridging the gap between the design and the implementation of robust cellular phenotypes.