  The assembly of virus capsids from free coat proteins proceeds by a
complicated cascade of association and dissociation steps, the great majority
of which cannot be directly experimentally observed. This has made capsid
assembly a rich field for computational models to attempt to fill the gaps in
what is experimentally observable. Nonetheless, accurate simulation predictions
depend on accurate models and there are substantial obstacles to model
inference for such systems. Here, we describe progress in learning parameters
for capsid assembly systems, particularly kinetic rate constants of coat-coat
interactions, by computationally fitting simulations to experimental data. We
previously developed an approach to learn rate parameters of coat-coat
interactions by minimizing the deviation between real and simulated light
scattering data monitoring bulk capsid assembly in vitro. This is a difficult
data-fitting problem, however, because of the high computational cost of
simulating assembly trajectories, the stochastic noise inherent to the models,
and the limited and noisy data available for fitting. Here we show that a newer
classes of methods, based on derivative-free optimization (DFO), can more
quickly and precisely learn physical parameters from static light scattering
data. We further explore how the advantages of the approaches might be affected
by alternative data sources through simulation of a model of time-resolved mass
spectrometry data, an alternative technology for monitoring bulk capsid
assembly that can be expected to provide much richer data. The results show
that advances in both the data and the algorithms can improve model inference,
with rich data leading to high-quality fits for all methods, but DFO methods
showing substantial advantages over less informative data sources better
representative of the current experimental practice.
