Current microarray data analysis techniques draw the biologist's attention to targeted sets of genes but do not otherwise present global and dynamic perspectives (e.g., invariants) inferred collectively over a dataset. Such perspectives are important in order to obtain a process-level understanding of the underlying cellular machinery; especially how cells react, respond, and recover from environmental changes.

We have devised, GOALIE (Gene-Ontology for Algorithmic Logic and Invariant Extractor), a novel computational approach and software system that uncovers formal temporal logic models of biological processes from time course microarray datasets. GOALIE `redescribes' data into the vocabulary of biological processes and then pieces together these redescriptions into a Kripke-structure model, where possible worlds encode transcriptional states and are connected to future possible worlds. An HKM (Hidden Kripke Model) constructed in this manner then supports various query, inference, and comparative assessment tasks, besides providing descriptive process-level summaries.

GOALIE runs on Windows XP platforms and is available on request from the authors.