  High-throughput data acquisition in synthetic biology leads to an abundance
of data that need to be processed and aggregated into useful biological models.
Building dynamical models based on this wealth of data is of paramount
importance to understand and optimize designs of synthetic biology constructs.
However, building models manually for each data set is inconvenient and might
become infeasible for highly complex synthetic systems. In this paper, we
present state-of-the-art system identification techniques and combine them with
chemical reaction network theory (CRNT) to generate dynamic models
automatically. On the system identification side, Sparse Bayesian Learning
offers methods to learn from data the sparsest set of dictionary functions
necessary to capture the dynamics of the system into ODE models; on the CRNT
side, building on such sparse ODE models, all possible network structures
within a given parameter uncertainty region can be computed. Additionally, the
system identification process can be complemented with constraints on the
parameters to, for example, enforce stability or non-negativity---thus offering
relevant physical constraints over the possible network structures. In this
way, the wealth of data can be translated into biologically relevant network
structures, which then steers the data acquisition, thereby providing a vital
step for closed-loop system identification.
