Modern artificial intelligence (AI) and machine learning (ML) techniques predict system
behavior from previous observations. However, in biology, and in true complex systems
in general, profound transformation in system behavior can occur from never before
seen observations. Moreover, AI/ML techniques are often unable to provide an explanation
for their predictions. I will argue that complex systems modeling, while using AI/
ML techniques to estimate parameters, needs to produce actionable, dynamical models
that can predict and explain system behavior for rare or unseen control events. This
is particularly true for understanding biochemical regulation under dynamical perturbations
from environmental and evolutionary events. Towards that goal, I will present
our recent work uncovering effective control pathways in the canalizing dynamics of
biochemical regulation. Our methodology centers on removing redundancy from systems biology models of development, cell cycle, and cancer response, as well as in
models of cortical networks cultured from mouse brains. Removing the large amounts of
redundancy in these models, reveals preferred pathways for the spread of perturbations
and the building blocks of dynamical response, leading to the prediction of actionable
control interventions
