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A Practical Approach to Genetic Inducible Fate Mapping: A Visual Guide to Mark and Track Cells In Vivo

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A deterministic map of Waddington's epigenetic landscape for cell fate specification.

Sudin Bhattacharya¹, Qiang Zhang, Melvin E Andersen

¹Division of Computational Biology, Program in Chemical Safety Sciences, The Hamner Institutes for Health Sciences, Research Triangle Park, NC 27709, USA. sbhattacharya@thehamner.org

BMC Systems Biology

|May 31, 2011

Summary

Scientists quantitatively mapped the epigenetic landscape to predict cell differentiation. This model reveals the forces guiding cell fate decisions and reprogramming, offering insights into developmental biology.

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Area of Science:

Systems Biology
Developmental Biology
Computational Biology

Background:

The epigenetic landscape metaphor visualizes cell states and lineage specification.
Cellular reprogramming highlights the dynamic nature of differentiated cells.
Quantitative mapping of the epigenetic landscape remains a challenge.

Purpose of the Study:

To quantitatively map the epigenetic landscape.
To develop a predictive model for cell differentiation and lineage specification.
To understand the forces driving cell fate decisions.

Main Methods:

Derived a deterministic path-integral quasi-potential from gene network kinetics.
Used quasi-potential as an "elevation" measure to map the landscape.
Employed stochastic simulations to validate landscape predictions.

Main Results:

Developed a quantitative map of the epigenetic landscape.
Demonstrated that trajectories flow "downhill" on this landscape.
Showed landscape elevation correlates with cell fate likelihood and stability.

Conclusions:

The quantitative map provides mechanistic insights into cell differentiation and reprogramming.
The approach is applicable to complex, high-dimensional gene networks.
Mapping the epigenetic landscape can optimize cell fate reprogramming strategies.