Title: Remembrance of Experiments Past: Analyzing Time Course Datasets to Discover Complex Temporal Invariants (NYU-CS-TR858) Author: Marco Antoniotti (1) Naren Ramakrishnan (2) Deept Kumar (2) Marina Spivak (1) Bud Mishra (1,3) (1) Courant Institute of Mathematical Sciences, New York University, New York, NY 10012, USA. (2) Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, USA. (3) NYU School of Medicine, New York, NY 10016, USA. Abstract: Motivation: 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 stresses. Results: We present GOALIE, 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. This model then supports various query, inference, and comparative assessment tasks, besides providing descriptive process-level summaries. An application of GOALIE to characterizing the yeast (S. cerevisiae) cell cycle is described. Availability: GOALIE runs on Windows XP platforms and is available on request from the authors.