  Ordinary differential equation models have become a standard tool for the
mechanistic description of biochemical processes. If parameters are inferred
from experimental data, such mechanistic models can provide accurate
predictions about the behavior of latent variables or the process under new
experimental conditions. Complementarily, inference of model structure can be
used to identify the most plausible model structure from a set of candidates,
and thus gain novel biological insight. Several toolboxes can infer model
parameters and structure for small- to medium-scale mechanistic models out of
the box. However, models for highly multiplexed datasets can require hundreds
to thousands of state variables and parameters. For the analysis of such
large-scale models, most algorithms require intractably high computation times.
This chapter provides an overview of state-of-the-art methods for parameter and
model inference, with an emphasis on scalability.
