Emergent signal execution modes in biochemical reaction networks calibrated to experimental data

Abstract

Mathematical models of biomolecular networks are commonly used to study mechanisms of cellular processes, but their usefulness is often questioned due to parameter uncertainty. Here, we employ Bayesian parameter inference and dynamic network analysis to study dominant reaction fluxes in models of extrinsic apoptosis. Although a simplified model yields thousands of parameter vectors with equally good fits to data, execution modes based on reaction fluxes clusters to three dominant execution modes. A larger model with increased parameter uncertainty shows that signal flow is constrained to eleven execution modes that use 53 out of 2067 possible signal subnetworks. Each execution mode exhibits different behaviors to in silico perturbations, due to different signal execution mechanisms. Machine learning identifies informative parameters to guide experimental validation. Our work introduces a probability-based paradigm of signaling mechanisms, highlights systems-level interactions that modulate signal flow, and provides a methodology to understand mechanistic model predictions with uncertain parameters.

Distinct execution modes of a biochemical necroptosis model explain cell type-specific responses and variability to cell-death cues

“…The rationale is that while different parameterizations of the model achieve cell death at approximately equal times, they may arrive there via significantly different sequences of molecular events. We utilized a computational tool 50 that identifies subnetworks of reactions that dominate the production or consumption of a target species, pMLKL in this case, at user-specified times along a time course. Each subnetwork is given an integer label and each time point is associated with a subnetwork.…”

“…53 It is of interest, therefore, to determine if modulating factors exist that are common across all modes of execution, which could represent novel therapeutic targets. Towards this end, we performed sensitivity analyses based on “representative” parameter sets for each mode (automatically generated by our dynamical systems analysis tool; 50 see Materials and Methods for details) over the 14 non-zero initial protein concentrations (Fig. 4A) and 40 rate constants (Fig.…”

“…Each time point is thus associated with an integer index and the entire simulated time course with a sequence of integers, i.e., the dynamical signature. We refer the reader to the original work 50 for further details on how PyDyNo identifies dominant subnetworks from ODE simulations of biochemical models. We repeated this procedure for all 10,628 unique parameter sets obtained from PyDREAM, with all simulations run at the highest TNF dose (100 ng/ml) for 16h simulated time, in line with experimental data (Fig.…”

“…89 Identifying modes of signal execution in a parameter set ensemble Modes of signal execution were identified using PyDyNo, a Python-based software package for dynamical systems analysis of biochemical models with uncertain parameters. 50 PyDyNo takes as input a model object (PySB 90 or SBML 91,92 formats), an input file with parameter sets, and a target species (pMLKL, in our case). ODE simulations are run 88,89 for all parameter sets and "digitized" into a sequence of integers, termed a "dynamical signature," based on "dominant" subnetworks of reactions identified at each time point.…”

Predictive uncertainty in mechanistic models of cellular processes calibrated to experimental data

“…These pathways trigger apoptosis by activating effector caspases 19 . We built an abridged Extrinsic Apoptosis Reaction Model (aEARM) 20 , which represents these extrinsic apoptosis execution mechanisms as biomolecular reactions (Figure 2A).…”