Quantifying biomarker ambiguity using metabolic network analysis

Abstract

Molecular biomarkers preserved in rocks provide evidence about ancient life but interpreting them requires inference through multiple stages of information loss arising from phylogenetic, biosynthetic, and diagenetic ambiguity. However, biomarker specificity is typically assessed qualitatively rather than quantitatively. Here we formalize biosynthetic ambiguity as entropy over metabolic networks. We introduce three metrics that quantify pathway-level information content: retrobiosynthetic complexity ({psi}), normalized branch depth ({lambda}), and fraction shared ({sigma}). Analysis of 9,140 MetaCyc metabolites defines a three-dimensional specificity space for biomarker evaluation. Only 13% of multi-pathway compounds exhibited low complexity, distal divergence, and high pathway consensus. Lipid biomarkers span this specificity space heterogeneously: hopanoids cluster near the high-specificity region while sterols occupy intermediate territory. Diagnostic quality and lipophilicity are approximately independent, so the constraint on molecular paleontology is the limited chemical diversity among preservable compound classes rather than their biosynthetic properties. This framework supports probabilistic biomarker interpretation by explicitly incorporating biosynthetic, phylogenetic, and diagenetic constraints.