Display Settings:


Send to:

Choose Destination
We are sorry, but NCBI web applications do not support your browser and may not function properly. More information
Cancer Res. 2011 Aug 15;71(16):5400-11. doi: 10.1158/0008-5472.CAN-10-4453. Epub 2011 Jul 8.

Comparing signaling networks between normal and transformed hepatocytes using discrete logical models.

Author information

  • 1Center for Cell Decision Processes, Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA.


Substantial effort in recent years has been devoted to constructing and analyzing large-scale gene and protein networks on the basis of "omic" data and literature mining. These interaction graphs provide valuable insight into the topologies of complex biological networks but are rarely context specific and cannot be used to predict the responses of cell signaling proteins to specific ligands or drugs. Conversely, traditional approaches to analyzing cell signaling are narrow in scope and cannot easily make use of network-level data. Here, we combine network analysis and functional experimentation by using a hybrid approach in which graphs are converted into simple mathematical models that can be trained against biochemical data. Specifically, we created Boolean logic models of immediate-early signaling in liver cells by training a literature-based prior knowledge network against biochemical data obtained from primary human hepatocytes and 4 hepatocellular carcinoma cell lines exposed to combinations of cytokines and small-molecule kinase inhibitors. Distinct families of models were recovered for each cell type, and these families clustered topologically into normal and diseased sets.

[PubMed - indexed for MEDLINE]
Free PMC Article

Images from this publication.See all images (7)Free text

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
PubMed Commons home

PubMed Commons

How to join PubMed Commons

    Supplemental Content

    Icon for HighWire Icon for PubMed Central
    Loading ...
    Write to the Help Desk