Modeling the nonlinear dynamics of cellular signal transduction

During the past decades the components involved in cellular signal transduction from membrane receptors to gene activation in the nucleus have been studied in detail. Based on the qualitative biochemical knowledge, signalling pathways are drawn as static graphical schemes. However, the dynamics and control of information processing through signalling cascades is not understood. Here we show that based on time resolved measurements it is possible to quantitatively model the nonlinear dynamics of signal transduction. To select an appropriate model we performed parameter estimation by maximum likelihood and statistical testing. We apply this approach to the JAK-STAT signalling pathway that was believed to represent a feed-forward cascade. We show by comparison of different models that this hypothesis is insufficient to explain the experimental data and suggest a new model including a delayed feedback.