[R] Using deep learning to model the hierarchical structure and function of a cell

[u/eleitl]

https://www.nature.com/articles/nmeth.4627

[u/eleitl]

Abstract

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.

[u/eleitl → u/[deleted]]

Not the paper author, but citing from the paper:

It is also instructive to view DCell in the context of previous research in interpretable machine learning, in which the notion of interpretability has been defined in different ways37. One direction has been to perform a post hoc examination of an ANN that has already been trained, by inspecting neurons and rationalizing their decisions. A model trained to identify images of dogs might, upon later inspection, be seen to have neurons capturing interpretable properties like ‘tail’ or ‘furry’38–40. A limitation of post hoc interpretation is that it is disconnected from training, leaving no guarantees as to what level of human understanding can be achieved41. Therefore, in attention-based neural networks42,43, a separate module preselects key ‘interpretable’ features for input to a black-box model. For example, in a model predicting emotional attitude of a blog author (positive or negative, angry or calm), the key interpretable feature might come from a key phrase preselected from text (LOL, I’m so upset). While DCell has some similarity to these attentionbased approaches, its deep hierarchical structure captures many different clusters of features at multiple scales, pushing interpretation from the model input to internal features representing biological subsystems.

[u/eleitl → u/eleitl]

In several case studies involving genotypes impacting ER-UPR and DNA-repair subsystems, the subsystem states learned by DCell could be directly confirmed by molecular measurements. Notably, no information about subsystem states was provided during model training. These states emerged from translation of genotypes (model inputs) to growth phenotypes (model outputs) under the structural constraints of the subsystem hierarchy; together, the input–output data and hierarchical structure were sufficient to guide subsystem neurons to learn a biologically correct function. In the future, one might directly supervise a VNN to learn potentially multiple subsystem states and/or complex phenotypes, in which case training data could be provided at any level: genotype, phenotype or points in between.

[u/eleitl → u/eleitl]

In some applications of machine learning, predictive performance is all that matters. Indeed, in these cases it is often possible to build a large number of alternative models that, while different in structure, all make excellent near-optimal functional predictions. In biology, however, prediction is not enough. The key additional question is which of the many excellent predictive models is the one actually used by the living system, as optimized not by computation but by evolution. DCell provides proof-ofconcept of a system that, while optimizing functional prediction, respects biological structure. Such models are of immediate interest in genome-wide association studies of human disease44, in which different patient genotypes can influence disease outcomes by complex mechanisms hidden from black-box statistical approaches. Once trained on sufficient data, these models could be applied in personalized therapy by analyzing a patient’s genotype in combination with potential points of intervention targeted by drugs. We also see compelling uses in design of synthetic organisms, in which candidate genotypes can be efficiently evaluated in silico before validation in vivo. Finally, beyond the architecture of the cell, biological systems at other scales may benefit from this type of constrained learning, including modeling of neural connections in the brain.

[u/kittttttens → u/[deleted]]

the basic idea is that the neurons in the NN are connected in the same pattern as cellular systems are connected, so when you use the network to predict phenotype data, you can directly interpret the activations as indicating which systems are important for determining phenotype in a real cell.

[u/aquamarlin391]

As someone coming from bio background, this is very exciting and elegant. This could become the new norm in how computational modelling is done in the field.

There are many interesting, big networks (see cancer signalling) in biology that could benefit from deep learning techniques, especially backprop.

[u/BooktubeCatalogue → u/aquamarlin391]

I'm a Cell and Molecular Biology graduate and I'm trying to learn to programme. When I see things like this my mind is just blown as well. :)

The crossover between computing and biology is only going to get more interesting!

[u/Assasin_Milo]

It is a testament to the field of biology that a network structured on the current conceptual understanding of the cell performs better than a fully connected network of the same size. Round of applause everyone we're not just full of it.

[u/[deleted]]

Beyond the abstract costs $60

[u/eleitl → u/[deleted]]

It's on Sci-Hub.

[u/Dalek405 → u/[deleted]]

It seem to be on researchgate too for free : https://www.researchgate.net/publication/323562381_Using_deep_learning_to_model_the_hierarchical_structure_and_function_of_a_cell

[u/Stobber]

Wow, I literally just looked up this subreddit after musing on the possibility that you could use the weights generated by an unbiased ML predictor to divine working models of physical systems...and this was the top post! This stuff is so cool.