#1 · Systems biology

Noise, feedback and network structure shape emergent states in biological models

The dominant thread is simulation- and theory-based analysis of how noise, feedback and coupling in nonlinear biological networks generate organized states. Noisy developmental dynamics in an extended classical gene-regulatory evolution model favour robust, motif-enriched networks. A parsimonious protocol refines Boolean gene-regulatory models by removing spurious attractors, applied to an osteogenesis network. A minimal regulatory grammar on a spatial lattice yields emergent multicellular morphology, with autoencoders used for inference. Evolutionary Kuramoto dynamics on directed, weighted connectomes explain chimera states in neural populations. Not every item shares this emphasis: the chronology preprint instead links oscillatory and linear epigenomic trajectories. What is new is the combination of evolutionary, Boolean and spatial formalisms applied to model-based inference of emergent organization, particularly the explicit treatment of spurious attractors and cell-to-cell variability.