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

PeerJ

article

Mohammad Jafar Khatibipour¹  Furkan Kurtoğlu¹  Tunahan Çakır¹ 

[1] Computational Systems Biology Group, Department of Bioengineering, Gebze Technical University;Department of Chemical Engineering, Gebze Technical University

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

DOI  :  10.7717/peerj.6034

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

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.

【 授权许可】

CC BY