ReviewProgress in biophysics and molecular biology2011
Evolving homeostatic tissue using genetic algorithms.
Review in Progress in biophysics and molecular biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Dissecting the Spatially Restricted Effects of Microenvironment-Mediated Resistance on Targeted Therapy Responses.Cancers · 2024Article
- Embryos assist morphogenesis of others through calcium and ATP signaling mechanisms in collective teratogen resistance.Nature communications · 2024Article
- Homeostasis Back and Forth: An Ecoevolutionary Perspective of Cancer.Cold Spring Harbor perspectives in medicine · 2017Review
- The superior fault tolerance of artificial neural network training with a fault/noise injection-based genetic algorithm.Protein & cell · 2016Article
- Article
- Exploiting ecological principles to better understand cancer progression and treatment.Interface focus · 2013Review
- A deterministic model for the occurrence and dynamics of multiple mutations in hierarchically organized tissues.Journal of the Royal Society, Interface · 2013Article
- A Systems Engineering Perspective on Homeostasis and Disease.Frontiers in bioengineering and biotechnology · 2013Review
- Systems biology and cancer.Progress in biophysics and molecular biology · 2011Article
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Authors and funding
3 authors.
Funding
Abstract
Multicellular organisms maintain form and function through a multitude of homeostatic mechanisms. The details of these mechanisms are in many cases unknown, and so are their evolutionary origin and their link to development. In order to illuminate these issues we have investigated the evolution of structural homeostasis in the simplest of cases, a tissue formed by a mono-layer of cells. To this end, we made use of a 3-dimensional hybrid cellular automaton, an individual-based model in which the behaviour of each cell depends on its local environment. Using an evolutionary algorithm (EA) we evolved cell signalling networks, both with a fixed and an incremental fitness evaluation, which give rise to and maintain a mono-layer tissue structure. Analysis of the solutions provided by the EA shows that the two evaluation methods gives rise to different types of solutions to the problem of homeostasis. The fixed method leads to almost optimal solutions, where the tissue relies on a high rate of cell turnover, while the solutions from the incremental scheme behave in a more conservative manner, only dividing when necessary. In order to test the robustness of the solutions we subjected them to environmental stress, by wounding the tissue, and to genetic stress, by introducing mutations. The results show that the robustness very much depends on the mechanism responsible for maintaining homeostasis. The two evolved cell types analysed present contrasting mechanisms by which tissue homeostasis can be maintained. This compares well to different tissue types found in multicellular organisms. For example the epithelial cells lining the colon in humans are shed at a considerable rate, while in other tissue types, which are not as exposed, the conservative type of homeostatic mechanism is normally found. These results will hopefully shed light on how multicellular organisms have evolved homeostatic mechanisms and what might occur when these mechanisms fail, as in the case of cancer.
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