A mechanistic understanding of cellular processes continues to be an elusive goal of quantitative biology. Measurements across multiple spatiotemporal resolutions are routinely collected but how these data impact our understanding of biological processes is often not clear. In this presentation, I will address the link between data and knowledge across multiple data types and explore the predictive power of models given different types of data. I will first introduce various classifications of data and how they relate to mechanistic explanations of cellular processes. We used a Bayesian inference formalism to probe how different data types at different resolutions can constrain model output and provide a probabilistic explanation for network mechanisms. Finally, I will demonstrate this probabilistic approach to understand network-driven processes in apoptosis signal execution, to identify network execution modes and the impact of increased noise in these predictions.

Zoom link: https://us02web.zoom.us/j/86289755333