Reversing pathological cell states: the road less travelled can extend the therapeutic horizon

Area of Science:

• Systems biology
• Computational biology
• Genomics

Background:

• Omics data acquisition is rapidly advancing, but translating this data into actionable control of cell phenotypes and reversal of disease states remains a challenge.
• Cellular states are governed by complex core networks that influence cell-wide regulatory systems.
• Understanding the genotype-phenotype connection is crucial for steering cell fate decisions.

Purpose of the Study:

• To introduce and validate a novel method, cell state transition assessment and regulation (cSTAR), for reconstructing and understanding core cellular networks.
• To demonstrate the capability of cSTAR models to mechanistically link genotype to phenotype and control cell states.
• To explore the application of cSTAR in reversing pathological cellular transformations, specifically oncogenic transformation.

Main Methods:

• Utilizing perturbation biology to quantitatively assess causal relationships within cellular networks.
• Developing mechanistic models based on cSTAR to represent digital cell twins.
• Applying cSTAR to model and understand the dynamics of oncogenic transformation.

Main Results:

• cSTAR successfully quantifies causal connections within cell-wide networks.
• Mechanistic models derived from cSTAR act as predictive digital cell twins.
• Demonstrated the potential of cSTAR to guide the conversion of pathological cell states to physiological ones.

Conclusions:

• cSTAR provides a powerful framework for understanding and manipulating cellular networks.
• The digital cell twin approach enables targeted interventions to restore normal cell function.
• cSTAR holds significant promise for therapeutic applications, particularly in reversing oncogenic transformation and other diseases.