Experiment Design in Static Models of Dynamic Biological Systems

Many studies aimed at elucidating the connectivity structure of biomolecular pathways make use of abundance or concentration measurements, and employ statistical and information theoretic approaches to assess connectivities. Here, we examine the effects of underlying system dynamics on structure learning efforts, and the type of data necessary to learn causal structures. We focus on single timepoint static models, which are often employed due to the infeasibility of obtaining sufficient dynamic data. We find that the dynamics of the system can have a confounding effect on causal structure learning. Thus we present, for the first time, the notion of entanglement as applied to a static model.