Automated particle tracking and stochastic modeling of molecular motion in submicron living systems

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This proposal outlines our plan to build a 'discovery pipeline'-encompassing data, modeling, and prediction-to study problems in mucosal immunology and cell biology. In particular, we study stochastic motion of 'particles' (i.e., molecules, pathogens, cells, etc) in micron-scale, and in intracellular and extracellular environments. This pipeline will produce answers to fundamental questions about how crowding, viscosity and elasticity, and hidden molecular kinetics impact biological processes. While the focus of the work is using mathematical modeling to gain mechanistic insight into biological systems, it will also include development of tools from statistics, machine learning, and computer science. The motion of particles is sometimes the direct focus of the research (e.g., Salmonella motion in mucus barriers). However, we also use particles as molecular 'probes' to infer hidden properties of the particle's local environment, such as viscosity, flow, crowding, elasticity, and confinement. In either case, stochastic modeling can mechanistically link particle motion from data (video recordings of motion) to predictions across time and space scales and for inference of hidden properties. We will first build the foundation of the discovery pipeline: a particle tracker that uses a neural network for automation and Bayesian networks to connect to models. Testing and validation of the approach will be conducted in coordination with multiple projects involving existing experimental data sets. The outcomes are (i) quantifying and elucidating answers to specific biological questions, and (ii) developing and validating our discovery pipeline. The long-term goal is to make the pipeline accessible to and adopted by research teams world-wide.