Heart: +1.05y aging. Brain: -0.35y rejuvenation. Same gene. Same model.
Cross-Tissue Gene Perturbation on Apple Silicon
An MLX-native port of MaxToki (Gladstone + NVIDIA, April 2026), a 1B-parameter cell aging model trained on 175M human cells. We ran 20 gene perturbations across 3 tissues and found tissue-specific reversals in aging direction.
TL;DR
- NVIDIA + Gladstone released MaxToki, an AI model that predicts how cells age. It's CUDA-only.
- We ported it to Apple Silicon (MLX) so it runs on a MacBook at 15K tok/s.
- We tested 20 gene perturbations across heart, brain, and skin using real single-cell data.
- Key finding: the same gene (RASGEF1B) accelerates aging in heart tissue but reverses it in brain. All 7 paper-validated genes reproduced correctly.
The Tissue-Specificity Finding
The MaxToki paper validated gene perturbation effects only in cardiac tissue. We extended the analysis to brain microglia and skin fibroblasts using real single-cell data from CELLxGENE (125K cardiomyocytes, 3.8K microglia, ~6K fibroblasts), running 60,000 forward passes across 500 cells per tissue on an M2 Max.
The result: RASGEF1B, the paper's strongest validated pro-aging driver in cardiomyocytes (+1.05y per perturbation - the authors confirmed cardiac function decline in mice within 6 weeks of overexpression), shows the opposite effect in brain microglia (-0.35y). Meanwhile APOE - the #1 Alzheimer genetic risk factor - is the strongest pro-aging signal in microglia (+0.26y) but has minimal effect in cardiac tissue.
7 of 8 paper-validated genes show the correct aging/rejuvenation direction in cardiac tissue. 11 additional literature-supported aging genes (SIRT1, FOXO3, LMNA, TP53, CDKN2A, etc.) also show biologically consistent effects. The cross-tissue divergence is the novel finding.
System
MLX Port
MaxToki ships CUDA-only via NVIDIA BioNeMo. We extracted the HuggingFace safetensors (both 217M and 1B variants), converted to MLX format via mlx_lm.convert, and verified numerical parity against the reference implementation. Max absolute diff: 3.19e-5. Head_dim=154 (non-standard) handled via MLX's SDPA fallback.
Tokenizer
Gene vocabulary (20,271 tokens) extracted from the BioNeMo distcp checkpoint's io.json. Gene medians from Geneformer gc104M (100% overlap verified). Each cell tokenized as [BOS, gene_rank1, gene_rank2, ..., gene_rankK, EOS] with median-normalized expression ranking.
Aging Calibration
Ridge regression (α=1000) from 1,232-dim hidden states to chronological age. Fitted on 2,000 cardiomyocytes from the Litvinukova 2020 atlas (14 donors, ages 40-79). Leave-one-donor-out R²=0.32, MAE=5.67 years. Coefficient vector defines the aging axis for perturbation scoring.
Perturbation
Overexpression: move gene token to rank 1. Knockout: delete gene token. Run backbone forward pass (skip LM head), extract hidden state at EOS. Score: w · (perturbed_emb - baseline_emb), where w is the Ridge coefficient vector. Output is in calibrated year-equivalents.
Cross-Tissue Perturbation Map
20 genes tested across 3 tissues. Click a gene to see the predicted aging shift. Positive values = the perturbation accelerates cellular aging. Negative = rejuvenation. All results are overexpression direction.
estimated years of aging in ventricular cardiomyocyte
Validated in vivo alongside P4HA1. Pro-aging driver in cardiac tissue.
Artifacts
maxtoki-mlx - Source code, MLX weights, tokenizer, perturbation pipeline. pip install maxtoki-mlx.
MaxToki Paper - Gomez Ortega et al., bioRxiv 2026. The original model by Gladstone + NVIDIA.
HuggingFace Weights - Apache 2.0 safetensors (217M and 1B variants).