Analysis and visualization of signal execution in network-driven biological processes

Abstract

Mathematical models are becoming increasingly used to describe biochemical processes and understand the systems behavior that arises from protein-protein interactions involved in a biochemical pathway. These complex mathematical models can yield useful insights about intracellular signal execution. However, identifying key species and reactions that drive signal execution within a large network remains a central challenge in quantitative biology. This challenge is compounded by the fact that networks are becoming increasingly large and model calibration yields parameter uncertainties with potentially different signal execution modes. In this work, we develop a model of the Jun N-terminal Kinase-3 (JNK3) activation cascade and used experimental data to quantify the role of scaffold proteins and determine the reactions that modulate JNK3 activation. We use Bayesian methods to calibrate the model to experimental data and analyzed the reactions that dictate the cascade activation. As a result of these experimental and computational analyses, we identified a potential “conveyor belt” mechanism for signal amplification by scaffold proteins. To further understand signal execution in biological processes, we developed a tool to visualize mode networks and simulated interactively and a method to characterize the effect of parameter uncertainty in signal execution. Our visualization tool employs community detection algorithms to group nodes and facilitates network exploration. It also maps concentration and reaction rate dynamics into nodes and edges to show the signal flow throughout a network. Finally, we develop a method to study parameter uncertainty that combines network analysis, clustering, and biochemistry to track signal execution under different conditions and identify modes of signal execution. In all, the analyses and tools developed in this work advance our understanding of complex biological networks and provide insights into the reactions that drive the systems behavior of biological processes.