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Identification of dynamic driver sets controlling phenotypical landscapes
Silke D Werle1, Nensi Ikonomi1, Julian D Schwab1
1Institute of Medical Systems Biology, Ulm University, 89081 Ulm, Baden-Wuerttemberg, Germany.
Computational and Structural Biotechnology Journal
|April 25, 2022
Summary
Researchers identified small compound sets to control entire cellular phenotypes by analyzing biological networks. This dynamic approach aids in understanding complex cellular decisions and guides experimental interventions.
Area of Science:
- Systems biology
- Computational biology
- Network science
Background:
- Controlling cellular phenotypes is crucial in modern biology.
- Cellular decisions arise from complex, interacting biological networks.
- Existing methods can control single phenotypes but lack a dynamic approach for whole-network control.
Purpose of the Study:
- To develop a dynamic method for identifying minimal compound sets that control entire phenotypical landscapes.
- To analyze biological networks to find drivers of network dynamics.
- To provide a framework for transitioning computational findings to experimental validation.
Main Methods:
- Analysis of 35 biologically motivated Boolean networks.
- Development of a method to identify dynamic driver sets.
- Application of the method to a colorectal cancer model.
Main Results:
- Identified small sets of compounds sufficient to control the entire phenotypical landscape.
- Discovered that these driver compounds do not necessarily need to be highly related.
- Found that these sets have a smaller impact on the stability of the attractor landscape.
- Dynamic driver sets encompass numerous intervention targets and cellular reprogramming drivers in human networks.
Conclusions:
- The developed method enables precise control over complex cellular phenotypes.
- This approach offers a powerful tool for biological research and therapeutic development.
- The study provides a practical workflow for implementing computational predictions in vitro, demonstrated with a colorectal cancer model.
Keywords:
Boolean networkCellular phenotype controlDynamic driverImplicantsIntervention targetsNetwork dynamicsMore Related Videos
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