Immune B cells defend the human body from infections by quickly dividing to increase their numbers and mutating their immune receptors to adapt to new pathogens. However, such frequent division and mutation creates a high risk of blood cancers, like lymphomas and myelomas. Every B cell makes an important decision about its fate – to die, to divide a certain number of times, or to differentiate into an antibody-producing cell – based on the affinity of its receptor to an oncoming pathogen. Currently, it is not understood how the B cells’ receptor affinities influence their internal gene networks to determine their fates. By combining microscopy, genomics, and computational models, Dr. Narayanan aims to discover the precise mechanisms underlying the B cell immune response, so that we can predict, prevent, or alleviate B cell-related cancers without compromising immunity.

Dr. Narayanan is applying multi-scale dynamical systems modeling to understand mechanisms driving B cell immunity and lymphomagenesis. The challenge is to relate stochastic spatial interactions between cells to signaling and gene expression dynamics within each cell. Her strategy is to integrate ODE- and agent-based numerical methods to simulate B cell evolutionary lineages, which encode the mechanisms that generated them, and then compare predicted lineage trees to experimental observations. Using live microscopy and image analysis, she tracks B cell interactions, signals, and resulting lineages at single-cell resolution in vitro. In parallel, she uses statistical inference on genomic data to reconstruct in vivo lineages. By fusing both data streams, she will compare observed and simulated tree shape statistics to validate the mechanistic model predictions.