Constructing a holistic map of cell fate decision by hyper solution landscape
Zhang, X.; Li, Z.; Zhang, L.
Show abstract
The Waddington landscape metaphor has inspired extensive quantitative studies of cell fate decisions using dynamical systems. While these approaches provide valuable insights, the intrinsic nonlinear complexity and the parameter dependence limits systematic analysis of fate transitions. Here, we introduce the Hyper Solution Landscape (HSL), a minimally parameter-dependent methodology showing a comprehensive structure of all possible landscape configurations for gene regulatory networks. HSL connects different solution landscapes to reflect dynamic changes of the landscapes associated with bifurcations. Applied to the Cross-Inhibition with Self-activation motif, HSL analysis identifies key hyperparameters driving distinct directional changes in cell fate propensity. Different routes through the HSL between the same initial and final states can produce markedly different fate distributions. This enables rational design of transition strategies. We validate HSLs utility in the seesaw model of cellular reprogramming, establishing a powerful framework for understanding and engineering cell fate decisions. A record of this papers Transparent Peer Review process is included in the Supplemental Information.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- A mathematical framework for understanding the spontaneous emergence of complexity applicable to growing multicellular systems 96%
- Non-asymptotic transients away from steady states determine cellular responsiveness to dynamic spatial-temporal signals 95%
- Associative memory networks for graph-based abstraction 94%
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.