Reverse Engineering of Dynamic Networks

:  We consider the problem of reverse‐engineering dynamic models of biochemical networks from experimental data using polynomial dynamic systems. In earlier work, we developed an algorithm to identify minimal wiring diagrams , that is, directed graphs that represent the causal relationships between network variables. Here we extend this algorithm to identify a most likely dynamic model from the set of all possible dynamic models that fit the data over a fixed wiring diagram. To illustrate its performance, the method is applied to simulated time‐course data from a published gene regulatory network in the fruitfly Drosophila melanogaster .