- Field
- researcher
- In the library
- 5 works, named in 12 passages elsewhere
Inferring networks from phenotypes
In a 2015 paper with Levin, Lobo argued that human scientists struggle to invent a model that fits every patterning result, and that each new dataset makes this harder1. The method tests each candidate network in a virtual worm under simulated experiments2. Its error score compares simulated outcomes with real planarian results. In the search, error falls and network complexity rises until a network with zero error appears3.
A primer on planarian modeling
The 2012 primer, written with Beane and Levin, set out what is known about planarian regeneration for modelers. It describes wound closure, blastema formation and re-patterning over one to two weeks. It also covers bioelectric signaling in planarians, including membrane voltage gradients and fluxes of hydrogen, potassium and calcium4. A 2017 review of bioelectricity cited it for the point that genetic and protein data still had to be linked to how regrowth emerges from cell activity5.
Use by later authors
Later papers by Levin and colleagues cite a Lobo et al. 2014 work for the inverse problem in regenerative medicine. The problem is that gene editing is limited because it is hard to know what to change in DNA and pathways to get a wanted anatomy6. A 2021 review cited Lobo et al. 2017 alongside work on ion channel mutations and bioelectric signaling in cancer7.
Thus, there is a clear need for automated tools to assist in the discovery of mechanistic models that explain the ever-increasing set of functional phenotypic results in the scientific literature
Lobo D, Levin M, 2015 · Inferring regulatory networks from experimental morphological phenotypes: a… · open at passage 4Thus, each candidate network model is tested in a virtual worm, under simulated experiments, to determine its patterning properties in each case.
Lobo D, Levin M, 2015 · Inferring regulatory networks from experimental morphological phenotypes: a… · open at passage 12Gradually over time, the error of the networks improves, while their complexity increases, until a network with zero error is found by the algorithm.
Lobo D, Levin M, 2015 · Inferring regulatory networks from experimental morphological phenotypes: a… · open at passage 19Recent work has begun to elucidate the bioelectric signaling that carries patterning information during planarian regeneration, including membrane voltage gradients, and fluxes of hydrogen, potassium, and calcium
Lobo D, Beane WS, Levin M, 2012 · Modeling planarian regeneration: a primer for reverse-engineering the worm · open at passage 18knowledge that has been acquired of genetic and protein data implicated in regeneration remains to be coupled to how regenerative growth emerges from cellular activities (Lobo et al., 2012)
Tyler SEB, 2017 · Nature's Electric Potential: A Systematic Review of the Role of Bioelectricity… · open at passage 38These ideas are part of a bigger effort to solve the inverse problem in regenerative medicine (Lobo et al., 2014): strategies such as CRISPR and genome editing cannot reach their full potential
Pio-Lopez L, Kuchling F, Tung A, Pezzulo G, Levin M, 2022 · Active inference, morphogenesis, and computational psychiatry · open at passage 100a mutation in an ion channel, such as Kir7.1 in zebrafish melanocytes, can block bioelectric signaling has interesting implications for cancer (Blackiston et al., 2011; Lobikin et al., 2015; Lobo et al., 2017)
McMillen P, Oudin MJ, Levin M, Payne SL, 2021 · Beyond Neurons: Long Distance Communication in Development and Cancer · open at passage 15
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