Signaling and the Single Cell

Stuart Sealfon

Translational Systems Biology, Mount Sinai School of Medicine

 
Individual cells in complex organisms are computational machines.  Input instructions are transmitted and processed by an intracellular signaling network.  Examples of inputs are information from another cell represented by a soluble chemical transmitter or information indicating the presence of virus infection represented by double stranded RNA. These inputs lead to transient data transmitted through the network nodes, largely represented by changes in protein conformation, which cause longer time scale changes in cell state, such as altered rates of synthesis of specific genes and proteins.  We and our collaborators use experiment and computational approaches to understand how this cellular network recognizes different inputs and controls specific cell state outputs.  It is increasingly evident that the averaging inherent in expermental studies using cell populations obscures some important features of cell responses, particularly the stimulus-response function, cell-to-cell variations and noise.  Recent experimental and modeling results from two experimental systems, pituitary cells and immune cells, will be presented.  In pituitary cells we studied the concentration-respone and noise characteristics of single cell regulation of a MAP kinase phosphorylation cascade by extracellular hormone.  In immune dendritic cells we studied the noise involved in activation of the interferon beta gene in respones to virus infection.  These results suggest both problems and potential benefits of noise for cellular data transmission.