Data-driven inference of Boolean networks from transcriptomes to predict cellular differentiation and reprogramming.

Area of Science:

• Computational Biology
• Systems Biology
• Genomics

Background:

• Boolean networks are valuable for modeling cellular dynamics, but their construction is complex.
• Integrating prior knowledge and transcriptome data is crucial for accurate modeling.

Purpose of the Study:

• To develop a general methodology for automatically generating ensembles of Boolean networks.
• To integrate transcriptome data and gene regulatory network knowledge for robust models.
• To identify candidate models and predict cellular reprogramming targets.

Main Methods:

• Utilized the BoNesis software for automatic Boolean network construction.
• Transformed transcriptome data into qualitative specifications for model generation.
• Employed ensemble modeling to predict robust reprogramming factors.

Main Results:

• Demonstrated the methodology's scalability and versatility with hematopoiesis and stromal cell differentiation models.
• Identified robust reprogramming factor combinations for trans-differentiation.
• Performed in silico assessment and preliminary experimental validation of reprogramming strategies.

Conclusions:

• The presented methodology enables automated construction of Boolean networks from diverse biological data.
• Ensemble modeling enhances the prediction of robust cellular reprogramming targets.
• This approach offers a powerful tool for understanding and manipulating cellular fate decisions.