Reconstructing data-driven governing equations for cell phenotypic transitions: integration of data science and systems biology.
Jianhua XingPublished in: Physical biology (2022)
Cells with the same genome can exist in different phenotypes and can change between distinct phenotypes when subject to specific stimuli and microenvironments. Some examples include cell differentiation during development, reprogramming for induced pluripotent stem cells and transdifferentiation, cancer metastasis and fibrosis progression. The regulation and dynamics of cell phenotypic conversion is a fundamental problem in biology, and has a long history of being studied within the formalism of dynamical systems. A main challenge for mechanism-driven modeling studies is acquiring sufficient amount of quantitative information for constraining model parameters. Advances in quantitative experimental approaches, especially high throughput single-cell techniques, have accelerated the emergence of a new direction for reconstructing the governing dynamical equations of a cellular system from quantitative single-cell data, beyond the dominant statistical approaches. Here I review a selected number of recent studies using live- and fixed-cell data and provide my perspective on future development.
Keyphrases
- single cell
- high throughput
- rna seq
- electronic health record
- high resolution
- cell therapy
- big data
- induced pluripotent stem cells
- healthcare
- induced apoptosis
- density functional theory
- gene expression
- dna methylation
- data analysis
- signaling pathway
- mass spectrometry
- current status
- finite element
- artificial intelligence
- deep learning
- endoplasmic reticulum stress
- lymph node metastasis