A computational model of melanocyte regulatory pathway revealed state-space decision points explaining all-or-none conversion (Lobikin et al. 2015)

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• Model-predicted intervention (two drugs and a dominant negative construct) broke concordance among melanocytes, producing first partially-pigmented animals (Lobo et al. 2017)finding0.778
  Demonstrates that breaking collective decision-making can be achieved, separating the decision from its coordination across cells.
• Brief chloride ion channel activator drug exposure converts normal melanocytes to melanoma-like phenotype in an all-or-none population-level fashion (Blackiston et al. 2011, Lobikin et al. 2015)finding0.765
  Reveals that melanocytes across the whole animal make a coordinated, stochastic decision to convert or remain normal.
• Gene regulatory network models exhibit associative learning and pattern completion.finding0.765
  Analysis of GRN models shows they can perform several kinds of learning, supporting the view of cellular networks as agents on a cognitive continuum.
• The two-dimensional subspace reported by Burger et al. (2024) seems to reflect a stage of transition in the model's processing, rather than a universal property of truth directions.quote0.760
  Load-bearing interpretive claim about the layer-specificity of Burger et al.'s finding.
• Gene regulatory networks can evolve associative memory, storing and recalling multiple phenotypes from partial selective cues (Watson et al. 2010)finding0.751
  Demonstrates information integration in evolutionary systems with system-level selection
• Turing's morphogenesis dynamics, D'Arcy Thompson's physical constraints, and Waddington's epigenetic landscape all describe the same phenomenon in different substrates as Alexander and Levin.claim0.747
• Bioelectric coordination creates stochastic concordance in melanocyte fate decisions across an organismfinding0.746
• The spatial memory pipeline: A model of egocentric to allocentric understanding in mammalian brains (Uria et al., 2020)concept0.743
  Alternative neuroscience model analogous to TEM; the mathematical relationship to transformers also holds for this model.