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"Our ultimate goal is to create a microfluidic system that converts a continuous input of cells into a real-time and quantitative output of protein concentrationand modification state, and thereby directly monitor the dynamics of biomolecular networds as they unfold."
K. F. Jensen

Data

Below you will find links to primary and processed data that has been generated by CDP investigators. We aim to provide a visually appealing presentation while providing downloadable spreadsheets of the actual raw data. In this we have been influenced by the work of Edward Tufte (1).

To be useful, informational graphics must answer the question "compared to what?" When viewing a complex set of data we typically seek to visualize relationships between specific pieces of information within the context of the data set as a whole. The choice of scaling and normalization methods is critical to the appearance of the data. A linear scale tends to give our eyes the best grasp on absolute, literal values, but absolute values are not necessarily very helpful. Logarithmic scales adjusted to the overall variance in the data are often more meaningful. In the case of high-dimensional data, the question arises as to which dimension should be chosen for normalization.

Gaudet, S., Janes, K. A., Albeck, J. G., Pace, E. A., Lauffenburger, D. A., and Sorger, P. K. (2005). A compendium of signals and responses triggered by prodeath and prosurvival cytokines.

B. Aldridge, G. Haller. P. Sorger, D. Lauffenburger. Direct Lyapunov Exponent Analysis Enables Parametric Study of Transient Signaling Governing Cell Behavior. Systems Biology (2006) 6, 425-432.

1. Tufte, E.R. (1983). The Visual Display of Quantitative Information. The Graphics Press, Cheshire, CT.