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

PeerJ. 2018 Dec 6;6:e6034. doi: 10.7717/peerj.6034. eCollection 2018.

Abstract

Reverse engineering metabolome data to infer metabolic interactions is a challenging research topic. Here we introduce JacLy, a Jacobian-based method to infer metabolic interactions of small networks (<20 metabolites) from the covariance of steady-state metabolome data. The approach was applied to two different in silico small-scale metabolome datasets. The power of JacLy lies on the use of steady-state metabolome data to predict the Jacobian matrix of the system, which is a source of information on structure and dynamic characteristics of the system. Besides its advantage of inferring directed interactions, its superiority over correlation-based network inference was especially clear in terms of the required number of replicates and the effect of the use of priori knowledge in the inference. Additionally, we showed the use of standard deviation of the replicate data as a suitable approximation for the magnitudes of metabolite fluctuations inherent in the system.

Keywords: Intrinsic fluctuations; Jacobian matrix; Lyapunov equation; Metabolic network inference; Reverse engineering of metabolome data; Stochastic dynamical system.

Grant support

This work was supported by the Turkish Academy of Sciences- Distinguished Young Scientists Award Program (TÜBA-GEBIP) and by TUBITAK, The Scientific and Technological Research Council of Turkey (Project Code: 215M201). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.