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