Metabolic Network Discovery by Top-Down and Bottom-Up Approaches and Paths for Reconciliation

Abstract

The primary focus in the network-centric analysis of cellular metabolism by systems biology approaches is to identify the active metabolic network for the condition of interest. Two major approaches are available for the discovery of the condition-specific metabolic networks. One approach starts from genome-scale metabolic networks, which cover all possible reactions known to occur in the related organism in a condition-independent manner, and applies methods such as the optimization-based Flux-Balance Analysis to elucidate the active network. The other approach starts from the condition-specific metabolome data, and processes the data with statistical or optimization-based methods to extract information content of the data such that the active network is inferred. These approaches, termed bottom-up and top-down, respectively, are currently employed independently. However, considering that both approaches have the same goal, they can both benefit from each other paving the way for the novel integrative analysis methods of metabolome data- and flux-analysis approaches in the post-genomic era. This study reviews the strengths of constraint-based analysis and network inference methods reported in the metabolic systems biology field; then elaborates on the potential paths to reconcile the two approaches to shed better light on how the metabolism functions.

Reconstruction of a generic genome-scale metabolic network for chicken: investigating network connectivity and finding potential biomarkers

“…Metabolism is an important cellular process in a living cell. Thus, a deep understanding of metabolic networks is required [1]. Collected biological data about metabolic pathways has led us to reconstruct a genome-scale metabolic network that can be mathematically represented [2].…”

JacLy: a Jacobian-based method for the inference of metabolic interactions from the covariance of steady-state metabolome data

“…Perturbations due to genetic/environmental alterations and diseases lead to changes in functionality due to change in cellular network structure, and network inference using the biomolecular data of the perturbation states uncovers the changes in network structure. When applied to the data of metabolite levels, the approach infers metabolic interaction (Srividhya et al, 2007; Çakır et al, 2009; Hendriks et al, 2011; Çakır & Khatibipour, 2014). The general trend is to use dynamic data to infer directed metabolic networks, and steady-state data to infer undirected networks.…”

“…However, they infer interactions only in undirected manner, and they have limited power in the detection of weak interactions (Çakır et al, 2009). A directed network inference approach from steady-state metabolome data is also available in the literature (Steuer et al, 2003; Öksüz, Sadıkoğlu & Çakır, 2013; Çakır & Khatibipour, 2014). The approach is based on the prediction of interaction strengths from the covariance of the data.…”

Peer Review #2 of "JacLy: a Jacobian-based method for the inference of metabolic interactions from the covariance of steady-state metabolome data (v0.1)"

“…However, they infer interactions only in undirected manner, and they have limited power in the detection of weak interactions (Çakır et al, 2009). A directed network inference approach from steady-state metabolome data is also available in the literature (Steuer et al, 2003;Öksüz, Sadıkoğlu & Çakır, 2013;Çakır & Khatibipour, 2014). The approach is based on the prediction of interaction strengths from the covariance of the data.…”