  Biomarkers enable objective monitoring of a given cell or state in a
biological system and are widely used in research, biomanufacturing, and
clinical practice. However, identifying appropriate biomarkers that are both
robustly measurable and capture a state accurately remains challenging. We
present a framework for biomarker identification based upon observability
guided sensor selection. Our methods, Dynamic Sensor Selection (DSS) and
Structure-Guided Sensor Selection (SGSS), utilize temporal models and
experimental data, offering a template for applying observability theory to
unconventional data obtained from biological systems. Unlike conventional
methods that assume well-known, fixed dynamics, DSS adaptively select
biomarkers or sensors that maximize observability while accounting for the
time-varying nature of biological systems. Additionally, SGSS incorporates
structural information and diverse data to identify sensors which are resilient
against inaccuracies in our model of the underlying system. We validate our
approaches by performing estimation on high dimensional systems derived from
temporal gene expression data from partial observations.
