The cellular scale showcases basins of attraction, where a region canalizes cells to an attractor. ASC are metastable attractors, differentiating in multiple specialized cell types 415 (Fig. ). Landscapes are useful to find genes guiding differentiation 416 or reprogramming factors . At the molecular scale, a free-energy landscape models biomolecules conformation and self-assembly 418 , it supports discoveries 419 and enables microstructure engineering 420 (Fig. ). This representation should not be seen as static, some hills and valleys can dynamically change, influenced by GRNs and surrounding microenvironments 421 . For instance, cell reprograming factors may "invert the slope" during dedifferentiation 417 (Fig. ). Such hierarchical landscapes offer a simple and generalizable representation of complex selfmicrofabrication systems. Modeling complex tissues Beyond a conceptual tool, an epigenetic landscape enables computational modeling 415 , using General Systems Theory 422 . For instance, at the cellular scale a cell type's unique gene expression profile can be viewed as a point in multidimensional space, with each coordinate corresponding the expression level of each particular gene 423 (Fig. ). It is commonly used in single-cell sequencing, with the gene expression matrix containing all information and dimensionality reduction techniques enabling clustering and other data processing . Different biological processes can be modeled depending on how the coordinates, or parameters, are defined. For instance, at the molecular scale, using chromatin modifications as variables for the landscape give insights about epigenetics . If the topography is built using DNA sequences, it may represent GRNs , protein conformations 428 , chromosome interaction 429 or phylogenic relations . Similarities between these modalities may help to interconnect landscapes (Fig. ). Landscapes have been defined at many biological scales 431 , including the tissular layer describing developmental trajectories during morphogenesis . We suggest that interconnecting them can allow the flow of information across scales (Fig. ). As cells are easier to characterize than organs, tissular landscapes may be inferred from more accessible cell measurements, such as single-cell transcriptomics used in cellular landscapes , even in non-model species 434 . To support the idea of vertical interconnections, we propose a theoretical demonstration of its feasibility. Consider a space X representing a molecular landscape with biomolecules x, Y as the cellular landscape, and Z tissular. P(x) is the probability of a molecule x to be in a particular state, the same applies to P(y) for a cell, and P(z) for a tissue. Using experimental data, the density distributions of P(x), P(y) and P(z) can be computed (e.g. gaussian mixture models, normalization flows, variational autoencoders or other methods 435 ). Similarly, conditional probabilities can be derived from empirical data, such as P(y|z), the probability of a cell type y being in a tissue z, and P(z|y) that a tissue z contains a given cell y. To carry the contribution from one scale to the next, we need to connect them hierarchically (X→Y→Z). As an example, we focus solely on two scales (Y→Z). Equation expresses P(z), the probability of a tissue state, as a marginal distribution derived from P(y,z), the joint probability of a cell state y and a tissue state z: