Carcinogenesis Advance Access published online on October 27, 2009
Carcinogenesis, doi:10.1093/carcin/bgp261
Cancer Systems Biology: A Network Modeling Perspective
1 Department of Biomedical Engineering, University of Wisconsin-Madison, Madison WI 53706
2 Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge MA 02139
* Corresponding author: D.A. Lauffenburger, Department of Biological Engineering, Building 16, Room 343, 77 Massachusetts Avenue, Cambridge, MA 02139, 617-252-1629 (telephone), 617-258-0204 (fax), lauffen{at}mit.edu (email)
Cancer is now appreciated as not only a highly heterogeneous pathology with respect to cell type and tissue origin, but also as a disease involving dysregulation of multiple pathways governing fundamental cell processes such as death, proliferation, differentiation, and migration. Thus, the activities of molecular networks that execute metabolic or cytoskeletal processes, or regulate these by signal transduction, are altered in a complex manner by diverse genetic mutations in concert with the environmental context. A major challenge therefore is how to develop actionable understanding of this multi-variate dysregulation, with respect both to how it arises from diverse genetic mutations and to how it may be ameliorated by prospective treatments. While high-throughput experimental platform technologies ranging from genomic sequencing to transcriptomic, proteomic, and metabolomic profiling are now commonly used for molecular-level characterization of tumor cells and surrounding tissues, the resulting data sets defy straight-forward intuitive interpretation with respect to potential therapeutic targets or the effects of perturbation. In this review article we will discuss how significant advances can be obtained by applying computational modeling approaches to elucidate the pathways most critically involved in tumor formation and progression, impact of particular mutations on pathway operation, consequences of altered cell behavior in tissue environments, and effects of molecular therapeutics.
Key Words: cell signaling • proteomics • transcriptomics
Received August 19, 2009; revised October 17, 2009; accepted October 18, 2009.