ArticleNature communications2019
Massive computational acceleration by using neural networks to emulate mechanism-based biological models.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
41 citing papers in PubMed.
- Deciphering global transcriptional dynamics coordinated by gene-gene regulatory interactions using single-cell data.Molecular systems biology · 2026Article
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- Virtual screening and molecular dynamics studies of antivenom using flavonoid compounds derived from Andrographis echioides.Scientific reports · 2025Article
- Multitask deep learning for the emulation and calibration of an agent-based malaria transmission model.PLoS computational biology · 2025Article
- ML-enhanced peroxisome capacity enables compartmentalization of multienzyme pathway.Nature chemical biology · 2025Article
- AI-powered simulation-based inference of a genuinely spatial-stochastic gene regulation model of early mouse embryogenesis.PLoS computational biology · 2024Article
- DyMMM-LEAPS: An ML-based framework for modulating evenness and stability in synthetic microbial communities.Biophysical journal · 2024Article
- AI in cellular engineering and reprogramming.Biophysical journal · 2024Review
- Highly efficient modeling and optimization of neural fiber responses to electrical stimulation.Nature communications · 2024Article
- A perspective-driven and technical evaluation of machine learning in bioreactor scale-up: A case-study for potential model developments.Engineering in life sciences · 2024Article
- Mechanism-based organization of neural networks to emulate systems biology and pharmacology models.Scientific reports · 2024Article
- Incorporating temporal information during feature engineering bolsters emulation of spatio-temporal emergence.Bioinformatics (Oxford, England) · 2024Article
- Graph Structured Neural Networks for Perturbation Biology.bioRxiv : the preprint server for biology · 2024Article
- Systems Biology Approaches to Understanding COVID-19 Spread in the Population.Methods in molecular biology (Clifton, N.J.) · 2024Article
- A newcomer's guide to deep learning for inverse design in nano-photonics.Nanophotonics (Berlin, Germany) · 2023Review
- Review
- Deep Neural Networks for Predicting Single-Cell Responses and Probability Landscapes.ACS synthetic biology · 2023Article
- From genotype to phenotype: computational approaches for inferring microbial traits relevant to the food industry.FEMS microbiology reviews · 2023Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
For many biological applications, exploration of the massive parametric space of a mechanism-based model can impose a prohibitive computational demand. To overcome this limitation, we present a framework to improve computational efficiency by orders of magnitude. The key concept is to train a neural network using a limited number of simulations generated by a mechanistic model. This number is small enough such that the simulations can be completed in a short time frame but large enough to enable reliable training. The trained neural network can then be used to explore a much larger parametric space. We demonstrate this notion by training neural networks to predict pattern formation and stochastic gene expression. We further demonstrate that using an ensemble of neural networks enables the self-contained evaluation of the quality of each prediction. Our work can be a platform for fast parametric space screening of biological models with user defined objectives.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.