ArticleScientific reports2017
Discovering novel phenotypes with automatically inferred dynamic models: a partial melanocyte conversion in Xenopus.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
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- Information Theory as an Experimental Tool for Integrating Disparate Biophysical Signaling Modules.International journal of molecular sciences · 2022Article
- A Computational Approach to Explaining Bioelectrically Induced Persistent, Stochastic Changes of Axial Polarity in Planarian Regeneration.Bioelectricity · 2022Article
- Formalizing Phenotypes of Regeneration.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Computational Systems Biology of Morphogenesis.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Bistability of somatic pattern memories: stochastic outcomes in bioelectric circuits underlying regeneration.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2021Review
- Beyond Neurons: Long Distance Communication in Development and Cancer.Frontiers in cell and developmental biology · 2021Review
- Fluxer: a web application to compute, analyze and visualize genome-scale metabolic flux networks.Nucleic acids research · 2020Article
- Post-SSRI Sexual Dysfunction: A Bioelectric Mechanism?Bioelectricity · 2020Article
- Biophysics and the Genomic Sciences.Biophysical journal · 2019Review
- Continuous Dynamic Modeling of Regulated Cell Adhesion: Sorting, Intercalation, and Involution.Biophysical journal · 2019Article
- Planarian regeneration as a model of anatomical homeostasis: Recent progress in biophysical and computational approaches.Seminars in cell & developmental biology · 2019Review
- EDEn-Electroceutical Design Environment: Ion Channel Tissue Expression Database with Small Molecule Modulators.iScience · 2019Article
- The body electric 2.0: recent advances in developmental bioelectricity for regenerative and synthetic bioengineering.Current opinion in biotechnology · 2018Review
Corrections and comments
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3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Progress in regenerative medicine requires reverse-engineering cellular control networks to infer perturbations with desired systems-level outcomes. Such dynamic models allow phenotypic predictions for novel perturbations to be rapidly assessed in silico. Here, we analyzed a Xenopus model of conversion of melanocytes to a metastatic-like phenotype only previously observed in an all-or-none manner. Prior in vivo genetic and pharmacological experiments showed that individual animals either fully convert or remain normal, at some characteristic frequency after a given perturbation. We developed a Machine Learning method which inferred a model explaining this complex, stochastic all-or-none dataset. We then used this model to ask how a new phenotype could be generated: animals in which only some of the melanocytes converted. Systematically performing in silico perturbations, the model predicted that a combination of altanserin (5HTR2 inhibitor), reserpine (VMAT inhibitor), and VP16-XlCreb1 (constitutively active CREB) would break the all-or-none concordance. Remarkably, applying the predicted combination of three reagents in vivo revealed precisely the expected novel outcome, resulting in partial conversion of melanocytes within individuals. This work demonstrates the capability of automated analysis of dynamic models of signaling networks to discover novel phenotypes and predictively identify specific manipulations that can reach them.
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