Relating biomarkers and phenotypes using dynamical trap spaces

TL;DR

This paper introduces a scalable, model-based framework using dynamical trap spaces in Boolean models to connect biomolecular network dynamics with cell phenotypes, aiding biomarker discovery and understanding cell states.

Contribution

It defines dynamical phenotypes as trap spaces in Boolean models, enabling efficient identification without full attractor enumeration and proposing a method to select informative biomarkers.

Findings

Dynamical phenotypes recover known cell types and states.

Method efficiently identifies phenotype-determining nodes.

Framework links model structure, inputs, and phenotypes.

Abstract

Connecting the dynamics of biomolecular networks to experimentally measurable cell phenotypes remains a central challenge in systems biology. Here we introduce a model-based definition of phenotype as a partial steady state that is committed to a certain dynamical outcome while otherwise being minimally constrained. We focus on Boolean models and define \emph{dynamical phenotypes} as complete trap spaces that maximally specify a chosen set of phenotype-determining nodes that correspond to biomarkers while keeping external inputs unconstrained. We show that dynamical phenotypes can be efficiently identified without full attractor enumeration. Using four published models, including a 70-node Boolean model of T cell differentiation, we show that dynamical phenotypes recover known cell types and activation states, and indicate the environmental conditions ensuring their existence. We also propose a method to identify informative phenotype-determining nodes based on the canalization of the Boolean functions. This method reveals biologically relevant cell state information that is complementary to the phenotypes manually defined by model creators and is validated by two attractor-based approaches. Our results demonstrate that dynamical phenotypes provide a scalable framework for linking model structure, external inputs, and phenotypic outcomes, and offer a principled tool for model-guided biomarker selection.