Stochastic gradient descent on the epigenetic landscape: a unified framework for cellular plasticity, tumor heterogeneity, and the asymptotic irrelevance of fitness

arXiv:2609.37703 · q-bio.PE, physics.bio-ph, q-bio.CB · Submitted 2026-09-29 · Read on arXiv

q-bio.PE, physics.bio-ph, q-bio.CB

Submitted: 2026-09-29

Updated: 2026-09-29

Code: https://github.com/arturfassoni/sgd-epigenetic-landscape-review

Terminology

• Abundance — 6× in this paper · explained in 1 episode
• Adaptation — 5× in this paper · explained in 1 episode
• Gradient Flow — 5× in this paper · explained in 2 episodes
• Ornstein–Uhlenbeck Process — 5× in this paper · explained in 3 episodes
• Stochastic Dynamics — 5× in this paper · explained in 2 episodes
• Spectral Gap — 4× in this paper · explained in 5 episodes
• Computational Biology — 3× in this paper · explained in 2 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Unified Framework — 3× in this paper · explained in 5 episodes
• Core — 2× in this paper · explained in 2 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Stochastic Gradient Descent (SGD) — 2× in this paper · explained in 5 episodes
• Trace — 2× in this paper · explained in 2 episodes
• AGN — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Bayesian Inference — 1× in this paper · explained in 5 episodes
• Boltzmann Equation — 1× in this paper · explained in 2 episodes
• Diffusion Process — 1× in this paper · explained in 2 episodes
• Dynamic Modeling — 1× in this paper · explained in 3 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• In International Conference on Learning Representations — 1× in this paper
• Initial Data — 1× in this paper · explained in 2 episodes
• Langevin Dynamics — 1× in this paper · explained in 2 episodes
• Limit Cycles — 1× in this paper · explained in 2 episodes
• Non-Gaussianity — 1× in this paper · explained in 2 episodes
• Non-Markovian Dynamics — 1× in this paper · explained in 5 episodes

Sources

• A multiscale discrete-to-continuum framework for structured population models
• Stochastic gradient descent performs variational inference, converges to limit cycles for deep networks

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