Articles 290 | VOL.15 NO.4 | APRIL 2018 | nAture methods Although artificial neural networks are powerful classifiers, their internal structures are hard to interpret. in the life sciences, extensive knowledge of cell biology provides an opportunity to design visible neural networks (Vnns) that couple the model's inner workings to those of real systems. here we develop dcell, a Vnn embedded in the hierarchical structure of 2,526 subsystems comprising a eukaryotic cell (http://d-cell.ucsd.edu/). trained on several million genotypes, dcell simulates cellular growth nearly as accurately as laboratory observations. during simulation, genotypes induce patterns of subsystem activities, enabling in silico investigations of the molecular mechanisms underlying genotype-phenotype associations. these mechanisms can be validated, and many are unexpected; some are governed by Boolean logic. cumulatively, 80% of the importance for growth prediction is captured by 484 subsystems (21%), reflecting the emergence of a complex phenotype. dcell provides a foundation for decoding the genetics of disease, drug resistance and synthetic life. Deep learning has revolutionized the field of artificial intelligence by enabling machines to perform human activities like seeing, listening and speaking 1-6 . Such systems are constructed from many-layered, 'deep' , artificial neural networks (ANNs) inspired by actual neural networks in the brain and how they process patterns. The function of the ANN is created during a training phase, in which the model learns to capture as accurately as possible the correct answer, or output, that should be returned for each example input pattern. In this way, machine vision learns to recognize objects like dogs, people and faces, and machine players learn to distinguish good from bad moves in games like chess and Go 7 . In modern ANN architectures, the connections between neurons as well as their strengths are subject to extensive mathematical optimization, leading to densely entangled network structures that are neither tied to an actual physical system nor based on human reasoning. Consequently, it is typically difficult to grasp how any particular set of neurons relates to system function. For instance, AlphaGo beats top human players 7 , but examination of its underlying network yields little insight into the rules behind its moves or how these are encoded by neurons. These are so-called black boxes 8 , in which the input-output function accurately models an actual system, but the internal structure does not (Fig. 1a) . Such models, while undoubtedly useful, are insufficient in cases where simulation is needed not only of system function but also of system structure. In particular, many applications in biology and medicine seek to model both functional outcome and the mechanisms leading to that outcome so that these can be understood and manipulated through drugs, genes or environment. Here we report DCell, an interpretable or 'visible' neural network (VNN) simulating a basic eukaryotic cell. The structure of this model is formulated from extensive prior knowledge of the cell's hierarchy of subsystems documented for the budding yeast Saccharomyces cerevisiae. This knowledge is drawn from either of two sources: the Gene Ontology (GO), a literature-curated reference database from which we extracted 2,526 cellular subsystems (intracellular components, processes or functions) 9 ; or the Clique-eXtracted Ontology (CliXO), an alternative ontology of similar size inferred from large-scale molecular data sets rather than literature curation 10, 11 . While CliXO and GO overlap in 38% of subsystems, some in CliXO are apparent in large-scale data sets but not yet characterized in literature, whereas some in GO are documented in the literature but difficult to identify in big data. Subsystems in these ontologies are interrelated through hierarchical parent-child relationships of membership or containment. Such hierarchies form a natural bridge from variations in genotype, at the scale of nucleotides and genes, to variations in phenotype, at the scale of cells and organisms 12, 13 . The function of DCell is learned during a training phase, in which perturbations to genes propagate through the hierarchy to impact parent subsystems that contain them, giving rise to functional changes in protein complexes, biological processes, organelles and, ultimately, a predicted response at the level of a cell
DCell constructs a multilayer neural network based on extensive prior biological knowledge to simulate the growth of a eukaryotic cell REF .
Using deep learning to model the hierarchical structure and function of a cell
Nature Methods
Nature Methods
Biology Medicine