摘要：SummaryInferring genome-scale metabolic networks in emerging model organisms is challenged by incomplete biochemical knowledge and partial conservation of biochemical pathways during evolution. Therefore, specific bioinformatic tools are necessary to infer biochemical reactions and metabolic structures that can be checked experimentally. Using an integrative approach combining genomic and metabolomic data in the red algal modelChondrus crispus, we show that, even metabolic pathways considered as conserved, like sterols or mycosporine-like amino acid synthesis pathways, undergo substantial turnover. This phenomenon, here formally defined as “metabolic pathway drift,” is consistent with findings from other areas of evolutionary biology, indicating that a given phenotype can be conserved even if the underlying molecular mechanisms are changing. We present a proof of concept with a methodological approach to formalize the logical reasoning necessary to infer reactions and molecular structures, abstracting molecular transformations based on previous biochemical knowledge.Graphical AbstractDisplay OmittedHighlights•Combination of metabolite profiling and genome-scale metabolic networks analysis•New method to infer biochemical reactions and metabolites in promiscuous pathways•Red algal model for sterol and mycosporine-like amino acid biosynthesis pathways•Metabolic drift if pathway variation despite conserved initial and final metabolitesBioinformatics; Genomics; Metabolomics
