Cell-Graphs

The structure-function relationship is fundamental to our understanding of biological systems at all levels, and drives most, if not all, techniques for detecting, diagnosing, and treating a disease. The predominant means of collecting structure/function data in biomedicine is reductionist and has thus led to a proliferation of complex data (for example, gene expression arrays, digital images) that captures only a fraction of the structure/function relationship. Gene sequence and expression data illustrates the structure and activities of individual genes but does not explain how these genes collaborate to control cellular and tissue-scale functions. As a result, despite the abundance of molecular details known about wound healing, for example, it is virtually impossible to accurately predict the final functional state of a healing wound.36 This illustrates a need to build models that represent the structural organization at the organ, tissue, cellular, and molecular levels. Furthermore, such models must capture relationships between these scales and relate them to the underlying functional state. Data-driven network/graph analysis is primed to decipher cellular interactions in the intricate relationship between protein-protein interactions, genetic changes, metabolic pathways, and chemical secretions, which comprise cellular events. When extended to the organ level, the key challenge would be to link the local and global structural properties of tissues to the overall morphology and function of a tissue. Only a systems-level understanding of the various cellular processes encompassing multiple biological levels will take into account the multidimensional complexity of these processes. If the principles governing biological organization on a morphological, spectral, local, and global scale can be deduced, the correlation between structural and molecular signaling within the tissue can be understood and applied to inform and accelerate studies of organ development and tissue regeneration. The cell-graph technique11,12,20 aims to learn structure-function relationship by modeling structural organization of a tissue/organ sample using graph theory. Its main hypothesis is that cells in a tissue/organ organize to perform a specific function.