Research - NetBio

design of Robust modules

For realizing large-scale synthetic genetic systems, modules need to be designed to specs

One of the major indicators we look at is robustness to disturbances

Based on the quantitative models, we design and test multiple versions of the same module

For every design, we assess their performance considering the design specifications

We rank different designs and assess their behavior with respect to model predictions

Considering the data and the predictions, we revise and refine our quantitative models

Cellular processes share building blocks and machinery that have limited availability

Competition for shared resources introduces coupling among "independent" processes

By accounting for the limited availability of resources, we can develop predictive models

Based on models explicitly accounting for coupling, we can predict emergent behaviors

Our approach is validated experimentally in the lab using our customized Chi.Bio platform

Leveraging the predictive models guides the redesign of components for increased modularity

Biological systems are complex nonlinear systems, thus we rely on data about them

Leveraging data about their behavior, we use machine learning to obtain a model of cells

Model predictive control enables us to steer cellular behavior towards our goal

Relying optimization methods, we can prioritize some objectives over others

Our approach is validated experimentally in the lab using our customized Chi.Bio platform

All the data we collect forms the foundation of learning the biophsycal system dynamics