Title: Remembrance of Experiments Past: Analyzing Time Course Datasets to Discover Complex Temporal Invariants


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.


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

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.