Metabolic network discovery through reverse engineering of metabolome data

Abstract

Reverse engineering of high-throughput omics data to infer underlying biological networks is one of the challenges in systems biology. However, applications in the field of metabolomics are rather limited. We have focused on a systematic analysis of metabolic network inference from in silico metabolome data based on statistical similarity measures. Three different data types based on biological/environmental variability around steady state were analyzed to compare the relative information content of the data types for inferring the network. Comparing the inference power of different similarity scores indicated the clear superiority of conditioning or pruning based scores as they have the ability to eliminate indirect interactions. We also show that a mathematical measure based on the Fisher information matrix gives clues on the information quality of different data types to better represent the underlying metabolic network topology. Results on several datasets of increasing complexity consistently show that metabolic variations observed at steady state, the simplest experimental analysis, are already informative to reveal the connectivity of the underlying metabolic network with a low false-positive rate when proper similarity-score approaches are employed. For experimental situations this implies that a single organism under slightly varying conditions may already generate more than enough information to rightly infer networks. Detailed examination of the strengths of interactions of the underlying metabolic networks demonstrates that the edges that cannot be captured by similarity scores mainly belong to metabolites connected with weak interaction strength.

Sparsity as Cellular Objective to Infer Directed Metabolic Networks from Steady-State Metabolome Data: A Theoretical Analysis

“…(5) and the covariance matrix of in silico data generated for this system in another study [6], we obtained a value of 0.998; indicating very high overlap. However, in silico data generation using stochastic differential equations (SDE) of type Eqn.…”

“…(2) can also be expressed by a Langevin-type equation to explicitly account for small fluctuations [17].where D i shows the extent of fluctuations, and η i is a random number from unit normal distribution. Note that internal metabolites can show true, albeit small, natural fluctuations over time due to complex regulatory patterns in the cell [6], [17]–[19], or such fluctuations can be induced externally by eg. introducing small time-dependent fluctuations on temperature, pH or external glucose concentration of microbial growth systems.…”

“…These approaches, termed top-down systems biology [1], have been more widely applied to transcriptome data to infer gene-regulatory or signaling networks [2]–[5] whereas applications to metabolome data to discover metabolic networks are rather limited [6]–[8].…”

The best models of metabolism

“…While most reverse‐engineering methods and applications have targeted gene networks, some have been applied to metabolic networks as well. Depending on the data, these inferences have addressed static networks or dynamic systems …”

ADEMA: An Algorithm to Determine Expected Metabolite Level Alterations Using Mutual Information

“…In gene expression analysis, MI has been frequently used to find dependencies among gene expression profiles [33]–[36]. There are only a few mutual information-based techniques in the context of metabolomics analysis, targeting different problems such as reverse engineering of metabolic networks [37] or measuring correlations within the network [38].…”