Waddingtons Epigenetic Landscape Explained
A Ball on a Hill
In 1942, the British biologist Julian Huxley gave a name to what looked like biology’s final triumph. [1] He called it “the modern synthesis” — the merger of Charles Darwin’s theory of evolution by natural selection with the young science of genetics. Genes governing an organism’s traits pass from parent to offspring. Random mutations supply the variation. Natural selection picks the fittest. When James Watson and Francis Crick revealed in 1953 that genes are encoded in DNA, the remaining work of biology looked like little more than filling in details. The blueprint was found; the rest was bookkeeping.
Conrad Hal Waddington, a British biologist known to friends and colleagues as Wad, was not satisfied. He granted that genes mattered. What troubled him was something the synthesis could not explain: how genes actually shape the forms and features of organisms. How does a single fertilized cell reliably produce all the different tissues of the body, in all the right places, at the right times, during embryonic development? The modern synthesis described the inheritance of traits. It said almost nothing about how a trait gets built.
Around the same time Huxley unveiled the modern synthesis, Waddington presented a different picture. He imagined development as a landscape of hills and valleys that split every so often, like the branching channels of a river network. At the very top of the highest hill he placed a ball. The ball stood for a population of cells in their earliest embryonic form — what researchers today call stem cells or pluripotent cells, cells with the potential to become any type. As the ball rolls downhill, it reaches branching points. At each fork, the model’s “gravity” pulls it onto one track or the other. In scientific terms, the cell must differentiate. Through a particular sequence of such branching decisions, cells specialize into their mature types, each expressing a particular set of genes. Waddington called this “canalization” — the process by which the sides of the valley trap and channel the ball, restricting cells to a small number of well-defined states. A limited number of cell types reliably arise from the activity of an army of genes.
A Metaphor Without Mathematics
James Briscoe, a developmental biologist at the Francis Crick Institute in London, has called Waddington’s landscape “remarkably useful as a conceptual scaffold.” [2] The core ideas survive, he says: development is progressive, cell states are distinct, bifurcations represent cell fate decisions, and canalization implies robustness.
The drawings had “no grounding in physical reality,” wrote Scott Gilbert, a developmental biologist at Swarthmore College in Pennsylvania, in a 1991 essay in Biology & Philosophy. They were useful schematics for thinking about development, and nothing more. “Without mathematical content,” Briscoe said, the metaphor “could not distinguish between alternative mechanisms, make quantitative predictions, or be falsified.” As a result, he said, Waddington’s landscape “has sometimes become a cliché rather than a meaningful explanation
What carves the hills and valleys in the first place? In his 1957 book The Strategy of the Genes, Waddington offered a partial answer. He depicted the landscape as it might look from underneath — a sheet tugged into shape by a network of ropes attached to pegs. The pegs represented individual genes. The ropes represented their effects on development. Expressing a particular gene — turning it into its corresponding protein — is like pulling on its ropes to change the shape of the sheet.
It is more complex than that, Waddington realized. The ropes are connected in a network. Any one rope might influence the landscape at several points. Any given point on the landscape might be attached to several ropes. These hidden connections correspond to what researchers now call gene regulatory networks: interactions among genes in which an increase in the expression of one gene can change, or regulate, the activity of others. Here lies the cryptic complexity of development. A single process, such as the formation of the neural tube from a layer of embryonic tissue called ectoderm — which ultimately develops into the central nervous system — might be influenced by many interacting genes. There is no simple relationship between genes (genotype) and the developmental landscape that determines form (phenotype). Gene regulatory networks mediate between them.
It makes no sense, then, to look for genes dedicated to creating specific tissues, organs, or structures in a whole organism. This is one reason Waddington’s metaphor proved useful. The landscape image, Briscoe says, was “valuable in providing an alternative to a purely gene-centric view of development.” [2] By emphasizing the landscape’s shape, it pointed “toward system-level properties that cannot be read off from any single gene.” Gene regulation was not understood when Waddington first devised his landscape and rope network. A few years later, in the early 1960s, it became clear that one gene could turn another on or off. Researchers such as the complexity theorist Stuart Kauffman began to construct simple mathematical models of gene interaction networks. But too little was known about real gene networks to connect abstract theory to experiment. “Given that the channels and spheres had no physical reality,” Gilbert wrote, “what was an embryologist supposed to do with them?”
The Landscape Measured
The answer has come from an unexpected direction: not from theory, but from measurement. One way to define the state of a given cell in an embryo is by the expression levels of its genes. These levels can now be measured simultaneously in many cells using a technique called single-cell RNA sequencing. The technique supplies a snapshot of all the different RNA molecules each cell contains. An RNA molecule — or transcript — is a kind of copy of a gene used to translate it into a functional protein. Generally, more RNA transcripts means higher expression of that gene. Each cell type is characterized by particular gene-expression settings, which are inherited when a cell divides. This ensures that a liver cell will stay a liver cell as it progresses through an organism’s development.
In principle, RNA-sequencing data can be modeled as a high-dimensional mathematical space in which each axis denotes the number of RNA transcripts of a single gene. Such a plot — with maybe thousands of dimensions to represent thousands of genes — is impossible to visualize. But it can be mathematically projected onto a space with fewer dimensions, usually just two, to produce something like the shadow of a complex three-dimensional object. On such a plot, called a UMAP (uniform manifold approximation and projection), different cell types appear as clusters of points. Blood cells cluster together, as do cells of the muscle or forebrain, because they share a gene expression profile. Cell lineages follow paths through this map as development proceeds. When the data is mapped through time — in what is known as a network flow model — it looks very much like a population of cells rolling through a Waddington-like landscape, channeled along valleys that lead to basins corresponding to distinct cell types.
Julie Theriot, a biophysicist at the University of Washington, cautions against supposing that this gene-expression map is literally Waddington’s landscape. Single-cell RNA sequencing is not the only or even the best way to characterize cell states, she says. The technique was “mind-blowing” at first, but RNA transcripts do not map perfectly to gene expression. And when researchers collapse the data into low-dimensional maps, they often do so in ways that reflect prior assumptions about how cells cluster into different types. The map, in other words, is not the territory.
Still, it is clear that, as Waddington supposed, cells undergo changes in state — defined by which genes are active or which proteins are made — as they mature and develop. These changes have a trajectory through time that carries them toward a limited number of final states. So how is that landscape of possibilities shaped? What governs the paths it offers? In 2007, the biologist Sui Huang, now at the Institute for Systems Biology in Seattle, described a bifurcation in cell state that gave the metaphor its first mathematical footing. [1] The landscape was no longer just a drawing. It was a topology that could be measured, and perhaps eventually navigated.
This cartography of development is now helping researchers understand how identical cells in the early embryo develop into the distinct tissues and cell types of a complex organism. The stakes are not abstract. Briscoe puts it plainly: “If you understand the landscape topology and know where the bifurcations lie, you can in principle design methods that steer cell populations to desired states with precision, rather than by trial and error.” [2].” Waddington’s metaphor, then, could be the key to understanding and reshaping possibilities for what cells, tissues, and embryos can be. But what the landscape actually reveals — whether its valleys are fixed or flexible, whether its ridges can be crossed, whether the ball can ever be pushed back uphill — remains the question the metaphor has always raised and never answered.