High Level Research
Del Vecchio’s group focuses on model-based analysis, design, and control of biomolecular networks in living cells. The objective is to make the engineering of synthetic genetic circuits in living organisms more robust to context, modular, and hence more scalable. One of our target applications is the design of synthetic genetic circuits that control cell fate to produce patient-specific somatic cells for regenerative medicine. Our approach is grounded on rigorous mathematical analysis of physics-based models of biological network dynamics, on control-theoretic tools for design, and on theory-educated experiments in living cells, from bacterial to mammalian.
Research
Modular design of biological networks
We seek to understand the limits of modularity in biologica l networks and to engineer biomolecular devices that enforce it when needed.
Analysis/design of stochasticity
Biomolecular processes are stochastic and evolve on multiple time scales. We are developing techniques to understand the implications of time-s cale separation on stochastic properties, such as noise attenuation and multi-modality.
Feedback control of natural networks
We are designing synthetic genetic feed back controller circuits to accurately steer the state of endogenous biomolecular network to desired locati ons.
Transportation research
We have been designing algorithms for th e design of safety-guaranteed driver assist systems and semiautonomous vehicle functionalities.
Latest Publications:
Reprogramming cooperative monotone dynamical systems
R. Shah and D. Del Vecchio
IEEE Conf. on Decision and Control
Accepted
August 2018
A Model for Resource Competition in CRISPR-Mediated Gene Repression
P. Chen, Y. Qian, and D. Del Vecchio
IEEE Conf. on Decision and Control
Accepted
August 2018
A Singular Singular Perturbation Problem Arising from a Class of Biomolecular Feedback Controllers
Y. Qian and D. Del Vecchio
IEEE Conf. on Decision and Control
Accepted
August 2018