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Stencil Micropatterning of Human Pluripotent Stem Cells for Probing Spatial Organization of Differentiation Fates
Published on: June 17, 2016
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Modeling signaling-dependent pluripotency with Boolean logic to predict cell fate transitions
Ayako Yachie-Kinoshita1,2,3, Kento Onishi1,2, Joel Ostblom1,2
1Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada.
Molecular Systems Biology
|January 31, 2018
Summary
This study models pluripotent stem cell (PSC) gene regulatory networks to predict cell states and transitions. The developed platform accurately simulates cell fate and heterogeneity, aiding developmental biology research.
Area of Science:
- Developmental Biology
- Stem Cell Biology
- Computational Biology
Background:
- Pluripotent stem cells (PSCs) exhibit diverse stable states crucial for development.
- Mechanisms governing pluripotency maintenance and destabilization remain incompletely understood.
- Understanding PSC state transitions is key to developmental and regenerative medicine.
Purpose of the Study:
- To develop a computational model for predicting stabilized pluripotent stem cell (PSC) gene regulatory network (GRN) states.
- To simulate single-cell fate transitions and population heterogeneity in PSCs.
- To identify signaling pathway combinations that control PSC fate decisions and differentiation.
Main Methods:
- Utilized random asynchronous Boolean simulations (R-ABS) for single-cell fate transition modeling.
- Employed strongly connected components (SCCs) to represent population heterogeneity.
- Applied the framework to a curated core GRN of mouse embryonic stem cells (mESCs) and simulated responses to five signaling pathways.
Main Results:
- The model successfully predicted experimentally verified cell population compositions.
- Identified specific input signal combinations that drive distinct cell fate transitions.
- Successfully predicted a signaling combination for robust generation of Cdx2+Oct4- cells from naïve mESCs.
Conclusions:
- The developed platform offers novel strategies for simulating cell fate transitions and heterogeneity in PSCs.
- This computational approach can advance understanding of developmental processes and guide differentiation protocols.
- The model provides a powerful tool for predicting and controlling PSC behavior in response to signaling cues.
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