Positional memory models of pattern regeneration

Lobo, Feldman, Shah, Malone and Levin (2014) described a formalization and database of published regeneration experiments. Human scientists could curate and mine it. Computers could simulate mechanistic models of regeneration and test their validity against the experiments stored in the database.¹ The group said it was working on artificial intelligence tools to discover mechanistic models of pattern regeneration and check them against known data on limb regeneration.¹ Manicka and Levin (2019) listed non-neural models of patterning, including cellular automata that combine local and global communication to model pattern regeneration.⁷

Bessonov, Levin, Morozova, Reinberg, Tosenberger and Volpert (2015) proposed a model of pattern regeneration based on cell memory. They noted that regeneration with memory cells acts locally and does not require interaction of signals from distant organs.² Wound healing, where only one tissue can be involved, is their example of local regeneration.² They argued that the same approach applies to wound healing and to regeneration of one organ or several organs at once.² If regeneration rests on memory in the remaining tissue, they reasoned, changing that memory should change regeneration.³ Their stated next step is a test with reagents known to modify cellular memories in the nervous system.⁴ The authors describe that test as future work.

Law and Levin (2015) modeled resting potential bistability in amphibia. They reported that bistable switches may be constructed by overexpressing Kir2.1 in amphibian oocytes and Nav1.6 in mammalian cells.⁶ They also argued that cells with strongly expressed fast channels tend to have memory loci near the reversal potentials of those channels. Slow channels can still form memory elements, but these should not be expected to have contiguous basins of attraction.⁶ They concluded that, under the right conditions, many types of somatic cells should be able to act as physiological memory elements, and said predictions would be tested in vivo.⁵ Silic and Zhang (2023) describe ion-channel bioelectricity as a new component of patterning control. They set it beside the morphogen gradient and transcription network theories, which they call the mainstay.⁹

Timsit and Grégoire (2021) discuss cell memory at the molecular level. They write that memory is still poorly understood at the neuronal level. At the molecular level, they say, it has been shown to rely on post-translational modification of proteins.⁸ Their examples are the methylation of bacterial chemoreceptors and the phosphorylation of receptors, both thought to be involved in cell memory and signalling.⁸