  Data-driven dynamic models of cell biology can be used to predict cell
response to unseen perturbations. Recent work (CellBox) had demonstrated the
derivation of interpretable models with explicit interaction terms, in which
the parameters were optimized using machine learning techniques. While the
previous work was tested only in a single biological setting, this work aims to
extend the range of applicability of this model inference approach to a
diversity of biological systems. Here we adapted CellBox in Julia differential
programming and augmented the method with adjoint algorithms, which has
recently been used in the context of neural ODEs. We trained the models using
simulated data from both abstract and biology-inspired networks, which afford
the ability to evaluate the recovery of the ground truth network structure. The
resulting accuracy of prediction by these models is high both in terms of low
error against data and excellent agreement with the network structure used for
the simulated training data. While there is no analogous ground truth for real
life biological systems, this work demonstrates the ability to construct and
parameterize a considerable diversity of network models with high predictive
ability. The expectation is that this kind of procedure can be used on real
perturbation-response data to derive models applicable to diverse biological
systems.
