Work

Pairwise causal discovery in biochemical networks: A survey on directionality inference within complex networks from stationary observations

Year: 2026

Type: article

Abstract: Metabolic networks map complex biochemical reactions within organisms, which is crucial for understanding cellular processes and metabolite flow. This study focuses on inferring the directionality of interactions in metabolomics networks. Given the challenge of using steady-state data, we benchmark various methods, including statistical scores and neural network approaches, on synthetic yet realistic biological models. Our findings highlight the relative success of a few methods in some cases where the interaction mechanism is known, whereas other methods show limited effectiveness.

Source: PLoS ONE

DOI: 10.1371/journal.pone.0349617

Authors: Nava Leibovich, Miroslava Cuperlovic-Culf

Institutions: National Research Council Canada, University of Ottawa, Ottawa Institute of Systems Biology

Language: English

Topic: Gene Regulatory Network Analysis

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $2,382

Funder: National Research Council Canada