{Sigma}0-EvoCell: An AI-Native Ontology that Unifies Evolutionary and Cell Biology in Latent Space

Foundation models for biology achieve impressive pattern recognition on molecular sequences and single-cell transcriptomics, yet they fail to outperform simple linear baselines for predicting genetic perturbation effects, exposing a gap between statistical correlation and mechanistic understanding. This gap is compounded by an interface problem: biological knowledge lives in human-readable formats (FASTA, SBML, ontology triples) that must be lossily re-encoded before a neural network can reason about them. Here we introduce the Evolutionary Cell Ontology (ECO), an AI-native formal language built on the {Sigma}0 substrate that represents biological knowledge directly as vector-encoded relational graphs. ECO uses 16 structural operators that serve simultaneously as knowledge glyphs, tensor operations, and--critically--carry a dual semantics spanning both evolutionary and cellular timescales, so the same operator that denotes speciation at the phylogenetic scale denotes irreversible APC/C commitment at the cell-cycle scale. We define a Latent Space Communication Protocol (LSCP) that maps ECO graphs into the residual stream of large language models, enabling systematic auditing of the biological knowledge a model actually contains. We illustrate ECO across three domains using controlled simulations: (i) globin protein-family evolution across roughly 1.5 billion years, where ECO epistatic attention recovers long-range coevolutionary couplings and ancestral-state reconstruction reaches 82-91% accuracy graded by conservation class; (ii) mammalian cell-cycle dynamics, where a CDK-cyclin attention graph with GATE checkpoints and FUSE commitment nodes reproduces two full oscillatory cycles with a four-attractor phase portrait emerging without explicit programming; and (iii) latent-space alignment, where ECO embedding distance tracks divergence across 60 protein families (Pearson r = 0.50) and an illustrative LSCP audit projects that relational, multi-scale concepts are encoded far more weakly than sequence-level facts. ECO replaces the human-readability constraint with an AI-processing constraint and, in doing so, turns the opacity of foundation models into a measurable, navigable coverage map.