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

被引:0
|
作者
Manchel, Alexandra [1 ]
Gee, Michelle [1 ,2 ]
Vadigepalli, Rajanikanth [1 ]
机构
[1] Thomas Jefferson Univ, Daniel Baugh Inst Funct Genom Computat Biol, Dept Pathol & Genom Med, Philadelphia, PA 19144 USA
[2] Univ Delaware, Dept Chem & Biomol Engn, Newark, DE USA
基金
美国国家卫生研究院; 美国国家科学基金会;
关键词
GENOME-WIDE EXPRESSION; MASS-SPECTROMETRY; GENE-EXPRESSION; SEQ; NETWORK; RECONSTRUCTION; PROTEOMICS; PROTEINS; DATABASE; OMICS;
D O I
10.1016/j.isci.2024.111322
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
As single-cell omics data sampling and acquisition methods have accumulated at an unprecedented rate, various data analysis pipelines have been developed for the inference of cell types, cell states and their distribution, state transitions, state trajectories, and state interactions. This presents a new opportunity in which single-cell omics data can be utilized to generate high-resolution, high-fidelity computational models. In this review, we discuss how single-cell omics data can be used to build computational models to simulate biological systems at various scales. We propose that single-cell data can be integrated with physiological information to generate organ-specific models, which can then be assembled to generate multi-organ systems pathophysiological models. Finally, we discuss how generic multi-organ models can be brought to the patient-specific level thus permitting their use in the clinical setting.
引用
收藏
页数:22
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