Alladi Ramakrishnan Hall

Understanding Cell Signaling: Model, Mechanism & Inference

Sayak Mukherjee

Ohio State University, Columbus OH, USA

Cells generate decisive functional outcomes to diverse stimuli. Much of current biological research has been directed at extracting mechanisms underlying cell signaling from experimental data. To this end efforts have been made to construct predictive models that are often built using a few effective variables. In the first part of my talk I will give an example involving an important mammalian immune cell (B cell) where spatial clustering of B cell receptors (BCRs) initiates a positive feedback mechanism to help discriminate between multiple antigens. However, the intuitive construction of reduced models using few effective variables in cell signaling becomes difficult in modeling systems where prior knowledge is sparse. Recent developments in high throughput experiments make it possible to characterize the kinetics with detailed data devoid of much mechanistic insights. Using temporal changes in pair correlations between different molecular species I will illustrate how to side step
this problem and how to extract biologically significant time scales and groups of correlated chemical species in the second part of my talk. For nearly all biological systems, in spite of growing body of data, our ignorance is profound and our knowledge is only partial. Incomplete knowledge renders any attempt to uniquely pin point one mechanism with certainty a theoretical impossibility. Under these circumstances we have to rely on inferences based on limited data. For the last part of my talk I will use Maximum Entropy based inference as a means of understanding cell-to-cell variation in protein abundance in context of Escherichia coli chemotaxis.