Decoding the principle of cell-fate determination for its reverse control.

Area of Science:

• Cellular and Molecular Biology
• Systems Biology
• Computational Biology

Background:

• Cell fate determination is crucial in biology, driven by complex molecular interactions.
• Mathematical modeling is essential for understanding cell fate, but requires dynamic experimental data, which has been a challenge.
• Advances in omics technologies, like single-cell RNA sequencing (scRNA-seq), now provide the necessary data for model development.

Purpose of the Study:

• To present a conceptual control framework for dynamic molecular regulatory network modeling using scRNA-seq data.
• To identify and manipulate core regulatory circuits and master regulators that govern cell fate.
• To enable the control of cellular state transitions for biological research and therapeutic applications.

Main Methods:

• Leveraging single-cell RNA-seq data to build dynamic molecular regulatory network models.
• Developing a conceptual control framework to analyze and predict cell fate trajectories.
• Applying the framework to model and manipulate lung cancer cell state reversion.

Main Results:

• The framework successfully models dynamic regulatory networks from scRNA-seq data.
• It enables the identification of core regulatory circuits and master regulators controlling cell fate.
• Demonstrated application in guiding the reversion of lung cancer cell states.

Conclusions:

• The proposed framework offers a powerful approach to quantitatively analyze and control cell fate determination.
• It utilizes readily available scRNA-seq data to uncover and manipulate key regulatory circuits.
• This methodology has broad applicability in understanding and directing various cell-fate processes, including cancer research.