BACKGROUND:
Signaling networks mediate responses to different stimuli using a multitude of feed-forward, feedback and cross-talk mechanisms, and malfunctions in these mechanisms have an important role in various diseases. To understand a disease and to help discover novel therapeutic approaches, we have to reveal the molecular mechanisms underlying signal transduction and use that information to design targeted perturbations.
RESULTS:
We have pursued this direction by developing an efficient computational approach, Sorad, which can estimate the structure of signal transduction networks and the associated continuous signaling dynamics from phosphoprotein time-course measurements. Further, Sorad can identify experimental conditions that modulate the signaling toward a desired response. We have analyzed comprehensive phosphoprotein time-course data from a human hepatocellular liver carcinoma cell line and demonstrate here that Sorad provides more accurate predictions of phosphoprotein responses to given stimuli than previously presented methods and, importantly, that Sorad can estimate experimental conditions to achieve a desired signaling response. Because Sorad is data driven, it has a high potential to generate novel hypotheses for further research. Our analysis of the hepatocellular liver carcinoma data predict a regulatory connection where AKT activity is dependent on IKK in TGFα stimulated cells, which is supported by the original data but not included in the original model.
The method presented and implemented in this study is available as a MATLAB routine
Tarmo Äijö
Aalto University School of Science,
Department of Information and Computer Science,
P.O. Box 15400, FI-00076 Aalto, Finland
firstname.lastname@aalto.fi
http://users.ics.aalto.fi/tare/
Updated: July 21, 2013