From transcriptomics to digital twins of organ function.

Area of Science:

• Systems biology
• Computational biology
• Genomics

Background:

• Cellular functions are fundamental to organ physiology, with gene expression patterns revealing cellular processes.
• Single-cell transcriptomics offers detailed insights into gene expression within various cell types and their proportions.
• Integrating gene expression with pathway knowledge identifies networks governing cell functions like growth and secretion.

Purpose of the Study:

• To develop a computational pipeline for integrating gene expression timelines with differential equation models.
• To generate cell-type-specific dynamical models for simulating physiological dynamics.
• To create predictive models linking genomic data to organ function for precision medicine.

Main Methods:

• Developed a computational pipeline integrating gene expression timelines and systems of coupled differential equations.
• Generated cell-type-selective dynamical models.
• Tested the pipeline on the eicosanoid biosynthesis network in macrophages.

Main Results:

• Successfully predicted prostaglandin and thromboxane synthesis and secretion dynamics in macrophages.
• Model predictions aligned with experimental lipidomics data.
• Demonstrated the potential for simulating whole-cell functional outputs from transcriptomic data.

Conclusions:

• The developed pipeline enables the creation of cell-level system biology simulations.
• Integration with knowledge graphs and multi-domain ontologies can link genomic determinants to organ phenotypes.
• These digital twin models can predict health and disease states for precision medicine applications.