Agent-based modeling of morphogenetic systems: advantages and challenges.
Open accessAbstract
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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Agent-based models are flexible in their execution and permit the aggregation of processes across time scales.
“These models are flexible in their execution and permit the aggregation of processes across time scales (Glen et al., 2019).”
From Thoroughly Calibrated Modular Agent-Based Model of the Human Cardiovascular and Renal Systems for Blood Pressure Regulation in Health and Disease (2021) · cites this paper as Glen et al., 2019
Agent-based models are unique in their ability to integrate combinations of heterogeneous processes and investigate their respective dynamics.
“Agent-based models are unique in their ability to integrate combinations of heterogeneous processes and investigate their respective dynamics.”
From Thoroughly Calibrated Modular Agent-Based Model of the Human Cardiovascular and Renal Systems for Blood Pressure Regulation in Health and Disease (2021) · cites this paper as Glen et al., 2019
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Cited by
Sorted by citations
Tissue Biology: In Search of a New Paradigm
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- Authors
- Glen, C. M., Kemp, M. L., & Voit, E. O.
- Affiliations
- Georgia Institute of Technology (US)
- Corresponding author
- Kemp, Melissa L.
- Voit, Eberhard O.
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2019-03-28
- Volume
- 15
- Issue
- 3
- Pages
- e1006577
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC6438454
- OpenAlex
- W2923643932
- Cited by
- 116
- References
- 157
- Citations by year
- 2026: 10 · 2025: 17 · 2024: 14 · 2023: 19 · 2022: 15 · 2021: 15 · 2020: 17 · 2019: 8 · 2017: 1
- FWCI
- 1.21
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Science Foundation (0939511, 1517588)
- Emergent Behaviors of Integrated Cellular Systems (0939511)
- Natural Sciences and Engineering Research Council of Canada
- Division of Chemical, Bioengineering, Environmental, and Transport Systems (CBET-0939511, 0939511)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Developmental Biology and Gene Regulation
- Physical Sciences › Mathematics › Modeling and Simulation › Mathematical Biology Tumor Growth
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › Cellular Mechanics and Interactions
- Keywords
- morphogenesis, agent-based modeling, reaction-diffusion systems, positional information, Turing patterns, biological pattern formation, morphogenetic systems, tissue engineering, computational modeling of development
- MeSH terms
- Models, Biological, Morphogenesis, Systems Analysis, Apoptosis, Cell Proliferation
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.
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