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International Conference on Complex Systems (ICCS2006)

Dynamics of cellular level function and regulation derived from murine expression array data

Benjamin de Bivort
Harvard Molecular and Cellular Biology

Sui Huang
Vascular Biology Program, Departments of Pathology and Surgery, Children's Hospital and Harvard Medical School

Yaneer Bar-Yam
New England Complex Systems Institute

     Full text: Not available
     Last modified: April 24, 2006

A major open question of systems biology is how genetic and molecular components interact to create phenotypes at the cellular level, and it remains to be seen how the gap between coarse descriptions (e.g. “the nucleus”) and fine descriptions (e.g. “importin α-2”) will be bridged. We approached this question using genome-wide murine B lymphocyte expression data from the Alliance for Cell Signaling (AfCS). Although many recent efforts have used this and other large databases to infer effective regulatory influences within small to medium-sized networks of genes, microarray data has yet to be used to determine coarser functional influences, such as those at the cellular level.
The number of model parameters that can be estimated from a set of data is ultimately limited by the total number of observations made in that set. Despite advances in high-throughput methodologies, the vast number of regulatory influences between pairs of genes has made the availability of data the limiting factor in network inference.
Rather than infer parameters detailing the interactions of just a few genes, we chose a larger-scale analysis, so that the aggregate effects of all genetic interactions could be analyzed to identify dynamics at the cellular-level. By first placing genes into large groups with related behaviors (megamodules) using the Self Organizing Map algorithm (SOM), we were able to determine the effective regulatory influences between 12 major gene groups over a variety of time steps.
Intriguing observations about the behavior of these megamodules include: (i) a medium-term critical global transcriptional dependence on ATP-generating metabolism genes localized in the mitochondria, (ii) a longer-term dependence on glycolytic genes, (iii) antithetical dual roles of chromatin-organizing genes in transcriptional activation and repression, (iv) homeostasis-favoring influences, (v) the indication that as a group, G protein-mediated signaling is not concentration-dependent, and (vi) short-term-activating/long-term-repressing behavior of the cell-cycle system, perhaps reflecting its oscillatory behavior.

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