From sampling to simulating: Single-cell multiomics in systems pathophysiological modeling.

Area of Science:

• Computational biology
• Systems biology
• Genomics

Background:

• Rapid accumulation of single-cell omics data necessitates advanced analysis pipelines.
• Existing pipelines infer cell types, states, transitions, and interactions.
• This creates opportunities for high-fidelity computational modeling.

Purpose of the Study:

• To review the use of single-cell omics data for building computational models.
• To propose integration of single-cell data with physiological information for organ-specific and multi-organ models.
• To discuss the translation of these models to patient-specific clinical applications.

Main Methods:

• Review of existing single-cell omics data analysis pipelines.
• Conceptual framework for integrating single-cell data with physiological information.
• Discussion of multi-organ systems modeling and patient-specific model generation.

Main Results:

• Single-cell omics data can generate high-resolution computational models.
• Organ-specific models can be assembled into multi-organ pathophysiological models.
• Models can be personalized to the patient level for clinical use.

Conclusions:

• Single-cell omics data is a powerful resource for computational modeling.
• Integrated multi-organ models hold promise for understanding complex diseases.
• Patient-specific computational models can advance precision medicine.