The Cellular Signal Integration Group (C-SIG) at CBS conducts computational and quantitative biology with the aim of understanding biological signaling systems. We explore biological systems by developing and deploying computational algorithms aimed to predict cell behavior similar to weather or aircraft models.
Cellular signaling networks are the foundation of cell fate and behavior and their aberrant activity is a key mechanism underlying the pathological behavior of cells, such as during tumor development. However, signaling networks are highly complex, involving a vast ensemble of dynamic interactions that flux in space and time. Thus, to understand how normal signal integration and aberrant cell decisions arise requires a global view of cell signaling networks. We and others have demonstrated that to obtain predictive insight into a biological system a combination of experimen-tal and computational exploration is needed.
Thus a major aim of our lab is to continue to develop computational tools (such as our flagship algorithms NetworKIN and NetPhorest) and to deploy these on quantitative mass-spectrometry, genetic and phenotypic data to understand at a systems-level the principles of how spatio and temporal assembly of mammalian signaling networks transmits and processes/integrates information (molecular logic) in order to alter cell behavior (cellular logic). We have previously deployed these algorithms on quantitative proteomics data to model
stem-cell differentiation, cell-cell communication, signalling evolution and to compare model organisms.
A major current activity is the search for signaling networks that drive regulatory diseases such as cancer, diabetes and neurological disorders. We hypothesize that these networks are powerful therapeutic targets, and we are motivated by the opportunity to perform systems based targeting of complex human diseases while simultaneously gaining insight into the fundamental principles behind cellular information processing and decision making.
For further information, please visit the Cell Signal Integration Group's own website