Signal execution modes emerge in biochemical reaction networks calibrated to experimental data.

Mathematical models of biomolecular networks are commonly used to study cellular processes; however, their usefulness to explain and predict dynamic behaviors is often questioned due to the unclear relationship between parameter uncertainty and network dynamics. In this work, we introduce PyDyNo (Python dynamic analysis of biochemical networks), a non-equilibrium reaction-flux based analysis to identify dominant reaction paths within a biochemical reaction network calibrated to experimental data. We first show, in a simplified apoptosis execution model, that despite the thousands of parameter vectors with equally good fits to experimental data, our framework identifies the dynamic differences between these parameter sets and outputs three dominant execution modes, which exhibit varying sensitivity to perturbations. We then apply our methodology to JAK2/STAT5 network in colony-forming unit-erythroid (CFU-E) cells and provide previously unrecognized mechanistic explanation for the survival responses of CFU-E cell population that would have been impossible to deduce with traditional protein-concentration based analyses.

Interactive Multiresolution Visualization of Cellular Network Processes.

Visualization plays a central role in the analysis of biochemical network models to identify patterns that arise from reaction dynamics and perform model exploratory analysis. To facilitate these analyses, we developed PyViPR, a visualization tool that generates static and dynamic representations of biochemical network processes within a Python-based environment. PyViPR embeds network visualizations within Jupyter notebooks, thus enabling integration with modeling, simulation, and analysis workflows. To present the capabilities of PyViPR, we explore execution mechanisms of extrinsic apoptosis in HeLa cells. We show that community-detection algorithms identify groups of molecular species that capture key biological functions and ease exploration of the apoptosis network. We then show how different kinetic parameter sets that fit the experimental data equally well exhibit significantly different signal-execution dynamics as the system progresses toward mitochondrial outer-membrane permeabilization. Therefore, PyViPR aids the conceptual understanding of dynamic network processes and accelerates hypothesis generation for further testing and validation.

Arrestin-3 scaffolding of the JNK3 cascade suggests a mechanism for signal amplification.

Scaffold proteins tether and orient components of a signaling cascade to facilitate signaling. Although much is known about how scaffolds colocalize signaling proteins, it is unclear whether scaffolds promote signal amplification. Here, we used arrestin-3, a scaffold of the ASK1-MKK4/7-JNK3 cascade, as a model to understand signal amplification by a scaffold protein. We found that arrestin-3 exhibited >15-fold higher affinity for inactive JNK3 than for active JNK3, and this change involved a shift in the binding site following JNK3 activation. We used systems biochemistry modeling and Bayesian inference to evaluate how the activation of upstream kinases contributed to JNK3 phosphorylation. Our combined experimental and computational approach suggested that the catalytic phosphorylation rate of JNK3 at Thr-221 by MKK7 is two orders of magnitude faster than the corresponding phosphorylation of Tyr-223 by MKK4 with or without arrestin-3. Finally, we showed that the release of activated JNK3 was critical for signal amplification. Collectively, our data suggest a "conveyor belt" mechanism for signal amplification by scaffold proteins. This mechanism informs on a long-standing mystery for how few upstream kinase molecules activate numerous downstream kinases to amplify signaling.