Integration of probabilistic regulatory networks into constraint-based models of metabolism with applications to Alzheimer\u2019s disease

Han Yu,Rachael Hageman Blair

doi:10.1186/s12859-019-2872-8

Abstract

BackgroundMathematical models of biological networks can provide important predictions and insights into complex disease. Constraint-based models of cellular metabolism and probabilistic models of gene regulatory networks are two distinct areas that have progressed rapidly in parallel over the past decade. In principle, gene regulatory networks and metabolic networks underly the same complex phenotypes and diseases. However, systematic integration of these two model systems remains a fundamental challenge.ResultsIn this work, we address this challenge by fusing probabilistic models of gene regulatory networks into constraint-based models of metabolism. The novel approach utilizes probabilistic reasoning in BN models of regulatory networks serves as the “glue” that enables a natural interface between the two systems. Probabilistic reasoning is used to predict and quantify system-wide effects of perturbation to the regulatory network in the form of constraints for flux variability analysis. In this setting, both regulatory and metabolic networks inherently account for uncertainty. Applications leverage constraint-based metabolic models of brain metabolism and gene regulatory networks parameterized by gene expression data from the hippocampus to investigate the role of the HIF-1 pathway in Alzheimer’s disease. Integrated models support HIF-1A as effective target to reduce the effects of hypoxia in Alzheimer’s disease. However, HIF-1A activation is far less effective in shifting metabolism when compared to brain metabolism in healthy controls.ConclusionsThe direct integration of probabilistic regulatory networks into constraint-based models of metabolism provides novel insights into how perturbations in the regulatory network may influence metabolic states. Predictive modeling of enzymatic activity can be facilitated using probabilistic reasoning, thereby extending the predictive capacity of the network. This framework for model integration is generalizable to other systems.

Highlights

Mathematical models of biological networks can provide important predictions and insights into complex disease
The integrated model consists of a signaling pathway represented by a Bayesian Network (BN) and a constraint-based model of cellular metabolism in the brain
These models are interfaced through belief propagation (Fig. 1), which enables prediction for the network under perturbation, and is used to constrain the Flux Variability Analysis (FVA) for the steady state estimation of fluxes in the metabolic model

Summary

Introduction

Mathematical models of biological networks can provide important predictions and insights into complex disease. Constraint-based models of cellular metabolism and probabilistic models of gene regulatory networks are two distinct areas that have progressed rapidly in parallel over the past decade. Gene regulatory networks and metabolic networks underly the same complex phenotypes and diseases. Systematic integration of these two model systems remains a fundamental challenge. Mathematical and probabilistic models of networks have become instrumental in elucidating complex relationships among molecular traits from high-throughput data, e.g., [1,2,3,4]. Data integration remains a major challenge for systems biology, especially at the network level, thereby limiting our ability to take full advantage of the wealth of post-genomics data. This work describes a novel approach to network integration that aims to understand how gene regulatory networks influence metabolism. Our approach interfaces two network-based approaches that have evolved

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