Project Summary/Abstract Despite recent experimental advances in single cell techniques and a concurrent development in statistical methods, our ability to predict single cell dynamics and identify the biochemical processes that dictate cell-to-cell variability remains rudimentary. We have identified the key roadblock in achieving mechanistic understanding of single cell behavior: we do not have computational methods to integrate single cell data with mechanistic signaling network models. Building upon our previous work and leveraging cutting-edge developments in neural networks, we propose a comprehensive research program to bridge this gap. The central problem in integration of single cell data with mechanistic models is that even large- scale data only partially constrain the models, leading to a family of models that fit the data equally well. How do we then choose from the models? Our strategy is to use the Maximum Entropy (Max Ent) approach which infers the least complex model: one that does not disfavor any outcome unless warranted by the data and the mechanistic constraints. Over the past decade, we have pioneered the novel use of Max Ent to model dynamics of biological networks. In the next five years, we plan to have two main research goals; (1) to build and validate the computational architecture required to integrate single cell data with models and (2) in close collaboration with experimentalists, use the developed framework to study the variability in two important pathways; the mitogen activated protein kinase (MAPK) pathway and mechanotransduction. We envision that this framework will be indispensable in exploring the mechanistic origins of cell-to- cell variability across a broad range of signaling networks. Notably, under-constrained models are ubiquitous in many areas of quantitative biology, including two of the laboratory’s other research foci: metabolism and microbiome dynamics. The program proposed here will directly benefit integration of large-scale data with mechanistic models and a principled exploration of otherwise hidden hypotheses.