A regulatory circuit motif dictates whether protein turnover fluxes are more predictive as biomarkers than protein abundances

Abstract

The identification of prognostic biomarkers fuels personalized medicine. Here we tested two underlying, but often overlooked assumptions: 1) measurements at the steady state are sufficient for predicting the response to drug action, and 2) specifically, measurements of molecule abundances are sufficient. It is not clear that these are justified, as 1) the response results from non-linear molecular relationships, and 2) the steady state is defined by both abundance and orthogonal flux information. An experimentally validated mathematical model of the cellular response to the anti-cancer agent TRAIL was our test case. We developed a mathematical representation in which abundances and fluxes (static and kinetic network features) are largely independent, and simulated heterogeneous drug responses. Machine learning revealed predictive power, but that kinetic, not static network features were most informative. Analytical treatment of the underlying network motif identified kinetic buffering as the relevant circuit design principle. Our work suggests that network topology considerations ought to guide biomarker discovery efforts. Graphic abstract Highlights – Biomarkers are usually molecule abundances but underlying networks are dynamic – Our method allows separate consideration of heterogeneous abundances and fluxes – For the TRAIL cell death network machine learning reveals fluxes as more predictive – Network motif analyses could render biomarker discovery efforts more productive eTOC blurb Precision medicine relies on discovering which measurements of the steady state predict therapeutic outcome. Loriaux et al show – using a new analytical approach – that depending on the underlying molecular network, synthesis and degradation fluxes of regulatory molecules may be more predictive than their abundances. This finding reveals a flaw in an implicit but hitherto untested assumption of biomarker discovery efforts and suggests that dynamical systems modeling is useful for directing future clinical studies in precision medicine.