ArticleNature communications2022
A scalable, open-source implementation of a large-scale mechanistic model for single cell proliferation and death signaling.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed, 24 citations in OpenAlex.
- Review
- A standardized workflow for kinetic metabolic model curation and dissemination.PLoS computational biology · 2026Article
- From FAIR to CURE: guidelines for computational models of biological systems.NPJ systems biology and applications · 2026Review
- Probabilistic modeling of cell cycle dynamics in response to cell cycle targeting chemotherapy drugs to guide treatment strategies.PLoS computational biology · 2025Article
- Mechanistic modeling of cell viability assays with in silico lineage tracing.PLoS computational biology · 2025Article
- Mathematical Modeling and Inference of Epidermal Growth Factor-Induced Mitogen-Activated Protein Kinase Cell Signaling Pathways.International journal of molecular sciences · 2024Review
- Self-consistent signal transduction analysis for modeling context-specific signaling cascades and perturbations.NPJ systems biology and applications · 2024Article
- A comprehensive review of computational cell cycle models in guiding cancer treatment strategies.NPJ systems biology and applications · 2024Review
- Network-driven cancer cell avatars for combination discovery and biomarker identification for DNA damage response inhibitors.NPJ systems biology and applications · 2024Article
- Endothelial cells signaling and patterning under hypoxia: a mechanistic integrative computational model including the Notch-Dll4 pathway.Frontiers in physiology · 2024Article
- The rise of scientific machine learning: a perspective on combining mechanistic modelling with machine learning for systems biology.Frontiers in systems biology · 2024Review
- Deep Neural Networks for Predicting Single-Cell Responses and Probability Landscapes.ACS synthetic biology · 2023Article
- MOBILE pipeline enables identification of context-specific networks and regulatory mechanisms.Nature communications · 2023Article
- Predicting anti-cancer drug combination responses with a temporal cell state network model.PLoS computational biology · 2023Article
- Multi-scale models of whole cells: progress and challenges.Frontiers in cell and developmental biology · 2023Review
- MEMMAL: A tool for expanding large-scale mechanistic models with machine learned associations and big datasets.Frontiers in systems biology · 2023Article
- Computational speed-up of large-scale, single-cell model simulations via a fully integrated SBML-based format.Bioinformatics advances · 2023Article
Corrections and comments
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Authors and funding
11 authors at 6 institutions in 1 country.
Funding
Abstract
Mechanistic models of how single cells respond to different perturbations can help integrate disparate big data sets or predict response to varied drug combinations. However, the construction and simulation of such models have proved challenging. Here, we developed a python-based model creation and simulation pipeline that converts a few structured text files into an SBML standard and is high-performance- and cloud-computing ready. We applied this pipeline to our large-scale, mechanistic pan-cancer signaling model (named SPARCED) and demonstrate it by adding an IFNγ pathway submodel. We then investigated whether a putative crosstalk mechanism could be consistent with experimental observations from the LINCS MCF10A Data Cube that IFNγ acts as an anti-proliferative factor. The analyses suggested this observation can be explained by IFNγ-induced SOCS1 sequestering activated EGF receptors. This work forms a foundational recipe for increased mechanistic model-based data integration on a single-cell level, an important building block for clinically-predictive mechanistic models.
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What Socratic 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 Socratic graph.