ReviewPLoS computational biology2023
Bridging the gap between mechanistic biological models and machine learning surrogates.
Review in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
27 citing papers in PubMed, 63 citations in OpenAlex.
- Active learning-guided mechanistic modeling reveals context-specific regulators of CXCL9 expression in pancreatic cancer cells.Molecular systems biology · 2026Article
- Pulmonary Drug Delivery in the Era of Nanomedicine: From Biological Barriers to Artificial Intelligence-Driven Optimization.Pharmaceuticals (Basel, Switzerland) · 2026Review
- From Prediction to Creation: Generative Plant Design.Plants (Basel, Switzerland) · 2026Review
- Integrating Genome-Scale Metabolic Modeling with Machine Learning Improves Gene Essentiality Prediction in Triple-Negative Breast Cancer.International journal of molecular sciences · 2026Article
- Knowledge preservation in the era of big science and AI: strategies for sustainable scientific research.Nature communications · 2026Review
- From genes to germ layers: virtual twins of gastruloids.NPJ systems biology and applications · 2026Review
- A hybrid ML-PBPK digital twin framework for clinically interpretable readmission risk and drug exposure stratification in diabetes.Frontiers in digital health · 2026Article
- Navigating the landscape of personalized oncology: overcoming challenges and expanding horizons with computational modeling.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- SIMBA-GNN: mechanistic graph learning for microbiome prediction.NPJ systems biology and applications · 2025Article
- Advances in surrogate modeling for biological agent-based simulations: trends, challenges, and future prospects.Journal of mathematical biology · 2025Review
- LDBT instead of DBTL: combining machine learning and rapid cell-free testing.Nature communications · 2025Article
- Constraint based modeling of drug induced metabolic changes in a cancer cell line.NPJ systems biology and applications · 2025Article
- Article
- Multitask deep learning for the emulation and calibration of an agent-based malaria transmission model.PLoS computational biology · 2025Article
- The dawn of a new era: can machine learning and large language models reshape QSP modeling?Journal of pharmacokinetics and pharmacodynamics · 2025Review
- The Evolution of Systems Biology and Systems Medicine: From Mechanistic Models to Uncertainty Quantification.Annual review of biomedical engineering · 2025Review
- Modeling with uncertainty quantification reveals the essentials of a non-canonical algal carbon-concentrating mechanism.Plant physiology · 2025Article
- Emerging trends in genome integration tools for precision engineering of diverse bacterial species.Synthetic biology (Oxford, England) · 2025Review
- Bridging systems biology and tissue engineering: Unleashing the full potential of complex 3DBiophysics reviews · 2024Review
- A review of mechanistic learning in mathematical oncology.Frontiers in immunology · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
Mechanistic models have been used for centuries to describe complex interconnected processes, including biological ones. As the scope of these models has widened, so have their computational demands. This complexity can limit their suitability when running many simulations or when real-time results are required. Surrogate machine learning (ML) models can be used to approximate the behaviour of complex mechanistic models, and once built, their computational demands are several orders of magnitude lower. This paper provides an overview of the relevant literature, both from an applicability and a theoretical perspective. For the latter, the paper focuses on the design and training of the underlying ML models. Application-wise, we show how ML surrogates have been used to approximate different mechanistic models. We present a perspective on how these approaches can be applied to models representing biological processes with potential industrial applications (e.g., metabolism and whole-cell modelling) and show why surrogate ML models may hold the key to making the simulation of complex biological systems possible using a typical desktop computer.
Indexed as
Identifiers
What OpenQuestion holds
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.