  In cell line perturbation experiments, a collection of cells is perturbed
with external agents (e.g. drugs) and responses such as protein expression
measured. Due to cost constraints, only a small fraction of all possible
perturbations can be tested in vitro. This has led to the development of
computational (in silico) models which can predict cellular responses to
perturbations. Perturbations with clinically interesting predicted responses
can be prioritized for in vitro testing. In this work, we compare causal and
non-causal regression models for perturbation response prediction in a Melanoma
cancer cell line. The current best performing method on this data set is
Cellbox which models how proteins causally effect each other using a system of
ordinary differential equations (ODEs). We derive a closed form solution to the
Cellbox system of ODEs in the linear case. These analytic results facilitate
comparison of Cellbox to regression approaches. We show that causal models such
as Cellbox, while requiring more assumptions, enable extrapolation in ways that
non-causal regression models cannot. For example, causal models can predict
responses for never before tested drugs. We illustrate these strengths and
weaknesses in simulations. In an application to the Melanoma cell line data, we
find that regression models outperform the Cellbox causal model.
