Model-based design of synthetic circuits under uncertainty
Synthetic biology deals with substantial challenges due to uncertainty
that traditional engineering disciplines do not face to the same extent.
This opens questions such as: Which gene circuit architecture will let
us achieve a given (complicated) design objective? Which are the most
informative experiments for circuit design? And how can we deal with
non-genetic cell-to-cell variability? The talk will present
computational design methods based on mechanistic mathematical models
that address these questions. Their common element is the use of
computational / mathematical approaches that systematically represent
uncertainty in circuit architectures, (unseen) experimental data, or
individual cell behaviors. Example applications include the design of
orthogonal signaling pathways and of informative sub-circuits to
characterize experimentally for circuit selection. The examples help to
argue that the computational design of synthetic circuits under
uncertainty provides an engineering framework for real-world applications.