Using deep learning to model the hierarchical structure and function of a cell
Open accessAbstract
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.
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Understanding how a cellular subsystem or protein complex is disrupted in a diseased tissue can aid development of therapeutic interventions.
“Every AIVC prediction could be substantiated with the corresponding multi-scale interactions that determine resulting states, e.g., understanding how a cellular subsystem or protein complex is disrupted in a diseased tissue can aid development of therapeutic interventions.118,119”
From How to build the virtual cell with artificial intelligence: Priorities and opportunities (2024) · cites this paper as [119]
The Results section (‘Explaining a Genotype-Phenotype Association’; page not provided) shows that the model can identify disrupted cellular subsystems. The Discussion (page not provided) proposes using such models to analyze patient genotypes and potential drug targets for personalized therapy. It does not examine a diseased tissue or demonstrate that identifying a disrupted protein complex led to a therapeutic intervention. (LLM, conf=0.85)
Experimental — accuracy not yet measuredXAI is useful in basic research, as in efforts to create "visible" deep neural networks that provide automatic explanations of the impact of a genotypic change on cellular phenotypic states.
“XAI is also useful in basic research, for instance, efforts in creating “visible” deep neural networks that provide automatic explanations of the impact of a genotypic change on cellular phenotypic states110.”
From Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare (2020) · cites this paper as [110]
Applied to a genotype-phenotype association study of yeast genetic screening data, an AI/ML network could map mutated genes to cellular growth rates.
“Applied to a genotype-phenotype association study of yeast genetic screening data, it was shown that the AI/ML network could map mutated genes to cellular growth rates.184”
From Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence (2023) · cites this paper as [184]
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- Authors
- Ma, J., Yu, M. K., Fong, S., Ono, K., Sage, E., Demchak, B., Sharan, R., & Ideker, T.
- Affiliations
- University of California San Diego (US)
- Tel Aviv University (IL)
- Corresponding author
- Ideker, Trey
- Venue
- Nature Methods
- Published
- 2018-03-05
- Volume
- 15
- Issue
- 4
- Pages
- 290-298
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC5882547
- OpenAlex
- W2790179710
- Cited by
- 435
- References
- 51
- Citations by year
- 2026: 39 · 2025: 46 · 2024: 62 · 2023: 51 · 2022: 55 · 2021: 68 · 2020: 54 · 2019: 34 · 2018: 26
- FWCI
- 16.27
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature Methods (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Nature
- National Institutes of Health (OT3TR002026)
- National Human Genome Research Institute (R01HG009979)
- National Cancer Institute (U54CA209891)
- National Institute of General Medical Sciences (R01GM084279, P41GM103504)
- National Institute of Environmental Health Sciences (R01ES014811)
- National Center for Advancing Translational Sciences
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- eukaryotic cells, genotype-phenotype correlation, Boolean logic, synthetic biology, drug resistance, disease genetics, synthetic life, DCell
- MeSH terms
- Deep Learning, Cell Physiological Phenomena, Computer Simulation, Gene Expression Regulation, Genotype, Humans, Neural Networks, Computer
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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