The redundancy and pleiomorphy of most physiologies implies any single set of processes and genes are unlikely to capture the entirety of the physiology operating in all contexts. Models of physiology that are defined from the bottom-up do not work very well for this reason, even if the mountains of data required to define these models are obtained. Also, purely statistical or data driven perspectives fail to capitalize on the enormous trove of prior knowledge gleaned in the biological sciences. Macrobiology leverages this prior knowledge in the interpretation of whole physiologies (and pathologies) using empiircal grounding offered by high-throughput, comprehensive measurements.

Developmental Signature in Cancer. The remarkable recapitulation of embryogenesis in oncogenesis has been recognized for over a century. A macrobiology approach has allowed futher characterizaton of these parallels and use them for highly robust human prognoses in cancer.

Localized Genomic Regulation : Most research of the regulatory control of genes assume a wide-ranging free-for-all across the entire genome. A macrobiology approach reveals subtle but reproducible evidence of highly localized control in the chromosomal neighborhood of the controlling elements (i.e microRNA). Another example of global control through local action

A comprehensive view of dysregulation in diabetes. Inidividual mouse models, human population studies, the study of individual signalling pathways, or even whole transcriptome expression studies do not embrace the full multi-organ multidisorder that this growing health threat consitutes. By triangulating across multiple measurement modalities we have characterized a much broader set of disordered physiologies than reported in prior single studies.

Reclassifying human disease. The current classification of human disease now reflects organ or insittutional boundaries rather than shared or distinguishing physiologies. By sampling genome-scale measurements across all available tissues and pathophysiologies, we have taken the first steps towards a macrobiology-evidenced nosology of human pathophysiology.

Kho, A. T., Zhao, Q., Cai, Z., Butte, A. J., Kim, J. Y., Pomeroy, S. L., Rowitch, D. H., and Kohane, I. S. (2004). Conserved mechanisms across development and tumorigenesis revealed by a mouse development perspective of human cancers. Genes Dev 18, 629-640.

Liu, H., Kho, A. T., Kohane, I. S., and Sun, Y. (2006). Predicting Survival within the Lung Cancer Histopathological Hierarchy Using a Multi-Scale Genomic Model of Development. PLoS Med 3, e232.

Inaoka, H., Fukuoka, Y., and Kohane, I. S. (2007). Evidence of spatially bound gene regulation in Mus musculus: decreased gene expression proximal to microRNA genomic location. Proc Natl Acad Sci U S A 104, 5020-5025.

Liu, M., Liberzon, A., Kong, S. W., Lai, W. R., Park, P. J., Kohane, I. S., and Kasif, S. (2007). Network-Based Analysis of Affected Biological Processes in Type 2 Diabetes Models. PLoS Genet 3, e96

Butte, A. J., and Kohane, I. S. (2006). Creation and implications of a phenome-genome network. Nat Biotechnol 24, 55-62.

Carter, S. L., Eklund, A. C., Kohane, I. S., Harris, L. N., and Szallasi, Z. (2006). A signature of chromosomal instability inferred from gene expression profiles predicts clinical outcome in multiple human cancers. Nat Genet 38, 1043-1048.

Computational Genomics Lab at Boston Univerisity

Informatics for Integrating Biology to the Bedside: A National Center for Biomedical Computing