Speaker
Description
Waddington’s epigenetic landscape (EL) famously represents development as a ball rolling down a branching surface whose valleys correspond to cell fates, while the landscape itself is shaped by pegs and ropes representing gene interactions. Despite its enduring influence, which continues today in fields such as stem cell biology, aging and single-cell omics, the conceptual meaning of this metaphor remains ambiguous.
In this work we propose a systematic critical analysis of both Waddington’s original EL metaphor and its modern theoretical reinterpretations. At the metaphor level, a basic difficulty lies in the status of the “ball”, which is supposed to represent the evolving cell, while the landscape is shaped by the “ropes” (gene products). This creates a visual and conceptual separation between the cell and the regulatory system. At the theoretical level, several mathematical frameworks have been proposed to formalize the landscape idea, including geometric approaches based on dynamical systems and bifurcation theory \cite{Cislo2025}, potential–flux decompositions of non-equilibrium dynamics \cite{Wang2015} and stochastic quasi-potentials derived from large-deviation theory \cite{Newby2015}. We analyze the relevance of their underlying assumptions and show that these frameworks differ in essential ways from Waddington’s vision. Clarifying these conceptual differences helps assess the scope and limitations of the EL metaphor in contemporary theoretical biology
Bibliography
@article{cislo2025,
title = {Reconstructing {Waddington}’s landscape from data},
volume = {122},
issn = {0027-8424, 1091-6490},
url = {https://pnas.org/doi/10.1073/pnas.2521762122},
doi = {10.1073/pnas.2521762122},
abstract = {The development of a zygote into a functional organism requires that this single progenitor cell gives rise to numerous distinct cell types. Attempts to exhaustively tabulate the interactions within developmental signaling networks that coordinate these hierarchical cell fate transitions are difficult to interpret or fit to data. An alternative approach models the cellular decision-making process as a flow in an abstract landscape whose signal-dependent topography defines the possible developmental outcomes and the transitions between them. Prior applications of this formalism have built landscapes in low-dimensional spaces without explicit maps to gene expression. Here, we present a computational geometry framework for fitting dynamical landscapes directly to high-dimensional single-cell data. Our method models the time evolution of probability distributions in gene expression space, enabling landscape construction with minimal free parameters and precise characterization of dynamical features, including fixed points, unstable manifolds, and basins of attraction. We demonstrate the applicability of this framework to multicolor flow-cytometry and RNA-seq data. Applied to a stem cell system that models ventral neural tube patterning, we recover a family of morphogen-dependent landscapes whose valleys align with canonical neural progenitor types. Remarkably, simple linear interpolation between landscapes captures signaling dependence, and chaining landscapes together reveals irreversible behavior following transient morphogen exposure. Our method combines the interpretability of landscape models with a direct connection to data, providing a general framework for understanding and controlling developmental dynamics.},
language = {en},
number = {49},
urldate = {2026-03-13},
journal = {Proceedings of the National Academy of Sciences},
author = {Cislo, Dillon J. and Delás, M. Joaquina and Briscoe, James and Siggia, Eric D.},
month = dec,
year = {2025},
pages = {e2521762122},
}
@article{wang2015,
title = {Landscape and flux theory of non-equilibrium dynamical systems with application to biology},
volume = {64},
issn = {0001-8732, 1460-6976},
url = {http://www.tandfonline.com/doi/full/10.1080/00018732.2015.1037068},
doi = {10.1080/00018732.2015.1037068},
language = {en},
number = {1},
urldate = {2026-03-13},
journal = {Advances in Physics},
author = {Wang, Jin},
month = jan,
year = {2015},
pages = {1--137},
}
@article{newby2015,
title = {Bistable switching asymptotics for the self regulating gene},
volume = {48},
issn = {1751-8113, 1751-8121},
url = {https://iopscience.iop.org/article/10.1088/1751-8113/48/18/185001},
doi = {10.1088/1751-8113/48/18/185001},
number = {18},
urldate = {2026-03-13},
journal = {Journal of Physics A: Mathematical and Theoretical},
author = {Newby, Jay},
month = may,
year = {2015},
pages = {185001},
}
