A Probabilistic Approach to Explore Signal Execution Mechanisms With Limited Experimental Data.

Carlos F Lopez,Michael A Kochen

doi:10.3389/fgene.2020.00686

Abstract

Mathematical models of biochemical reaction networks are central to the study of dynamic cellular processes and hypothesis generation that informs experimentation and validation. Unfortunately, model parameters are often not available and sparse experimental data leads to challenges in model calibration and parameter estimation. This can in turn lead to unreliable mechanistic interpretations of experimental data and the generation of poorly conceived hypotheses for experimental validation. To address this challenge, we evaluate whether a Bayesian-inspired probability-based approach, that relies on expected values for quantities of interest calculated from available information regarding the reaction network topology and parameters can be used to qualitatively explore hypothetical biochemical network execution mechanisms in the context of limited available data. We test our approach on a model of extrinsic apoptosis execution to identify preferred signal execution modes across varying conditions. Apoptosis signal processing can take place either through a mitochondria independent (Type I) mode or a mitochondria dependent (Type II) mode. We first show that in silico knockouts, represented by model subnetworks, successfully identify the most likely execution mode for specific concentrations of key molecular regulators. We then show that changes in molecular regulator concentrations alter the overall reaction flux through the network by shifting the primary route of signal flow between the direct caspase and mitochondrial pathways. Our work thus demonstrates that probabilistic approaches can be used to explore the qualitative dynamic behavior of model biochemical systems even with missing or sparse data.

Highlights

The complex dynamics of biochemical networks, stemming from numerous interactions and pathway crosstalk, render signal execution mechanisms difficult to characterize (Bhalla and Iyengar, 1999; Kitano, 2002; Loscalzo and Barabasi, 2011)
We investigate whether a Bayesian-inspired probabilistic approach can identify network signal execution mechanisms in extrinsic apoptosis restricted only by experimental observations
We take a probabilistic approach, similar to those used in Bayesian evidence-based model selection and multimodel inference, to compare model subnetworks and pathways with respect to apoptotic signal execution under various in silico experimental conditions and enable the generation of hypotheses regarding the underlying mechanisms of signal processing

Summary

Introduction

The complex dynamics of biochemical networks, stemming from numerous interactions and pathway crosstalk, render signal execution mechanisms difficult to characterize (Bhalla and Iyengar, 1999; Kitano, 2002; Loscalzo and Barabasi, 2011). Probabilistic Signal Execution Mechanism Exploration mechanics with knowledge garnered from years or even decades of experimentation (Albeck et al, 2008; Lopez et al, 2013) These models have yielded important predictions and insights about biochemical network processes, they depend on kinetic rate parameters and protein concentrations that are often poorly characterized or unavailable. The data needed for parameter optimization is often scarce, leading to the possibility of multiple parameter sets that fit the model to that data well but exhibit different dynamics (Lopez et al, 2013; Shockley et al, 2018) This poses a challenge for the study of dynamic network processes as the mode of signal execution can be highly dependent on a specific parameter set and could in turn lead to inadequate modelbased interpretation. A computational approach that enables the exploration of biochemical signal execution mechanisms from a probabilistic perspective, constrained only by available data, would facilitate a rigorous exploration of network dynamics and accelerate the generation of testable mechanistic hypotheses (Wrede and Hellander, 2018)

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Discussion

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