Abstract: The present invention provides a method to create a Boolean or multilevel logic network model of a dynamic system of interdependent variables from observed system states transitions that can (1) operate with data consisting of only a fraction of all possible transitions, (2) accommodate measurement error on these transitions (3) produce a probability distribution over network functions (rather than simply giving one set of network functions that match the data) (4) support asynchronous activation times for different genes (5) support varying delays between gene activation and gene expression for different genes (6) accommodate the stochastic nature of gene network operations (7) support varying degrees of gene activation (not simply Boolean activation states) and (8) incorporate prior knowledge of the nature and limitations of the actual network functions being modeled.