ReviewPLoS computational biology2019
Agent-based modeling of morphogenetic systems: Advantages and challenges.
Review in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.
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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.
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Who cites it
54 citing papers in PubMed.
- A Controlled in Silico Benchmark for GNN Prediction of Tissue Dynamics.Research square · 2026Article
- Multiscale predictive cellular modeling: integrating hypothesis grammars, digital twins, and multi-omics for In silico oncology and precision theranostics.Functional & integrative genomics · 2026Review
- Emergent stable tissue shapes from the regulatory feedback between morphogens and cell growth.Journal of theoretical biology · 2026Article
- Format-Preserving Reduction of Canonical Nonlinear Models.Bulletin of mathematical biology · 2026Article
- Evolvoid: a genetic algorithm for shaping optimal cellular constructs.Journal of biological engineering · 2026Article
- Bioinks in systems biology: integrating biomaterial design with cellular network modelling for predictive biofabrication.Frontiers in bioengineering and biotechnology · 2026Review
- Engineering development: From the repressilator and toggle switch to synthetic developmental biology.Developmental biology · 2025Review
- Temporal dynamics of angiogenesis: the emerging role of mechanoregulated pathways.Biochemical Society transactions · 2025Review
- Article
- Emergent Stable Tissue Shapes from the Regulatory Feedback between Morphogens and Cell Growth.bioRxiv : the preprint server for biology · 2025Article
- Mechanistic computational modeling of sFLT1 secretion dynamics.PLoS computational biology · 2025Article
- Personalizing computational models to construct medical digital twins.Journal of the Royal Society, Interface · 2025Article
- Internal cohesion gradient as a novel mechanism of collective cell migration.PLoS computational biology · 2025Article
- Deciphering the interplay between biology and physics with a finite element method-implemented vertex organoid model: A tool for the mechanical analysis of cell behavior on a spherical organoid shell.PLoS computational biology · 2025Article
- Agent-based modeling in cancer biomedicine: applications and tools for calibration and validation.Cancer biology & therapy · 2024Review
- Personalizing computational models to construct medical digital twins.bioRxiv : the preprint server for biology · 2024Article
- Exploring the Role of Genetic and Environmental Features in Colorectal Cancer Development: An Agent-Based Approach.Entropy (Basel, Switzerland) · 2024Article
- Engineering a computable epiblast for in silico modeling of developmental toxicity.Reproductive toxicology (Elmsford, N.Y.) · 2024Article
- Modeling the roles of cohesotaxis, cell-intercalation, and tissue geometry in collective cell migration of Xenopus mesendoderm.Biology open · 2024Article
- Inspiring a convergent engineering approach to measure and model the tissue microenvironment.Heliyon · 2024Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The complexity of morphogenesis poses a fundamental challenge to understanding the mechanisms governing the formation of biological patterns and structures. Over the past century, numerous processes have been identified as critically contributing to morphogenetic events, but the interplay between the various components and aspects of pattern formation have been much harder to grasp. The combination of traditional biology with mathematical and computational methods has had a profound effect on our current understanding of morphogenesis and led to significant insights and advancements in the field. In particular, the theoretical concepts of reaction-diffusion systems and positional information, proposed by Alan Turing and Lewis Wolpert, respectively, dramatically influenced our general view of morphogenesis, although typically in isolation from one another. In recent years, agent-based modeling has been emerging as a consolidation and implementation of the two theories within a single framework. Agent-based models (ABMs) are unique in their ability to integrate combinations of heterogeneous processes and investigate their respective dynamics, especially in the context of spatial phenomena. In this review, we highlight the benefits and technical challenges associated with ABMs as tools for examining morphogenetic events. These models display unparalleled flexibility for studying various morphogenetic phenomena at multiple levels and have the important advantage of informing future experimental work, including the targeted engineering of tissues and organs.
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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.