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Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies

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Dynamic Sensor Selection for Biomarker Discovery.

Joshua Pickard¹, Cooper Stansbury¹, Amit Surana²

¹Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI 48109.

|June 3, 2024

Summary

This summary is machine-generated.

Selecting optimal biomarkers is crucial for interpreting large biological datasets. This study introduces a novel observability theory approach to identify the most informative biological sensors for diverse applications, improving data analysis.

Keywords:

biomarkers data driven observability dynamic sensor selection observability sensor selection

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Area of Science:

Systems Biology
Biotechnology
Computational Biology

Background:

Biological data collection methods are rapidly advancing, generating large, comprehensive datasets.
Biomarkers are essential for monitoring biological systems, but selecting the most informative ones from vast datasets is challenging.
Existing methods struggle to identify optimal biomarkers for dynamic and changing biological systems.

Purpose of the Study:

To establish a general methodology for biomarker selection using principles of observability theory.
To introduce a dynamic sensor selection (DSS) method to maximize observability over time, adapting to changing system dynamics.
To demonstrate the broad applicability of this observability framework across diverse biological data types and systems.

Main Methods:

Applied observability theory to time series transcriptomics data to identify biologically meaningful sensors.
Developed the dynamic sensor selection (DSS) method to enhance observability in systems with changing dynamics.
Modeled gene expression dynamics and incorporated auxiliary data, such as chromosome conformation, for biomarker selection.

Main Results:

Observability measures effectively identified key biological sensors in transcriptomics data.
The dynamic sensor selection (DSS) method successfully maximized observability, even when system dynamics shifted.
The framework demonstrated flexibility by integrating gene expression and chromosome conformation data.

Conclusions:

Observability theory provides a robust framework for guided biomarker selection in complex biological datasets.
The dynamic sensor selection (DSS) method offers a powerful tool for identifying dynamic biomarkers across various biological systems.
This approach is applicable beyond genomics, including neural activity analysis, agriculture, and biomanufacturing.