Nonlinear Dynamics and Pattern Formation

This paper analyzes a mechanochemical model to understand how curvature interactions influence steady-state solutions in tissue deformation.

The paper describes Turing pattern dynamics as a potential flow landscape, applies it to three-component systems and SOX9 expression during digit patterning.

The paper refines a reaction-diffusion model to study segmented wavetrains in mouse seminiferous tubules, finding they are stable, directionally unbiased, and influenced by tubule elongation.

This paper introduces a phase oscillator model to explain actomyosin oscillations, showing that cooperative behavior arises from mechanical coupling rather than molecular kinetics.

This paper investigates complex reaction-diffusion models to understand patterning constraints and introduces a software pipeline for simulating these systems.

This paper develops a phase model to explain self-organized concentric wave patterns in embryonic oscillators, showing that excitability and coupling function shape these patterns.

This study shows astrocytes can convert hyper-synchronized neural states into chimera states in Hindmarsh-Rose neurons, suggesting a mechanism for restoring normal brain function in neurological disorders.

The paper uses a numerical model to study information transmission in cell monolayers, identifying stochastic factors that affect wave propagation and determining optimal channel widths and wave frequencies for effective communication.

The paper develops a statistical framework to analyze how mobility affects synchronization and pattern robustness in mobile oscillators during vertebrate embryonic development.

This paper develops a mathematical model to explain how the tulip breaking virus causes striped patterns in tulips by combining activator-substrate and positional information mechanisms on a growing petal domain.

This study shows that Turing patterns are more likely to occur by chance in biological networks and identifies an optimal network size for robust Turing behavior.

The paper introduces a model where stochastic fluctuations drive morphological pattern formation, showing how fluctuations determine the dynamics of a phase transition in a one-dimensional tissue.

This study investigates how neuromodulatory changes affect network reorganization in coupled Kuramoto oscillators by altering phase response curves and plasticity.

This study observes synchronized growth oscillations in Serendipita indica fungi during sporulation using a microfluidic setup and links it to cell-cell communication.

The paper identifies non-reciprocal synchronization rules in embryonic oscillator ensembles using a novel experimental assay and develops a new coupling model called Rectified Kuramoto.

The paper designs and studies a synthetic optogenetic oscillator in E. coli, demonstrating complex spatiotemporal patterns including synchronization, resonance, and chaos under periodic light exposure.

The paper investigates trigger wave speed robustness in Xenopus extracts, finding it invariant despite cytoplasmic concentration changes and proposing a model explaining this behavior.

The paper studies how neuronal activity influences the spread of toxic proteins in neurodegenerative diseases through a multi-timescale model of spreading on adaptive networks.

The paper updates stability criteria for synchrony in pulse-coupled oscillator networks to include conduction delays and shows how alternating firing patterns can enforce cluster synchrony.

The paper analyzes Turing pattern formation in reaction-cross-diffusion systems with bilayer geometry, showing how coupling between layers can trigger or stabilize patterns.

This study investigates self-organised chloroplast waves in Caulerpa brachypus, showing their coupling to a biological oscillator and response to light patterns.

The paper demonstrates a method using self-organized patterns and machine learning to encode and decode information, showing scalability by mapping the English keyboard.

The paper shows that pattern formation in adjacent tessellated domains can be correlated through geometry, without direct communication, and validates this with biological examples.

This study compares continuous and discrete models of Turing patterns, finding discrete models more robust and suggesting deeper principles in Turing mechanisms.

The paper develops a reaction-diffusion model to explain Erk activity waves in zebrafish scale regeneration, showing how feedback loops control wave properties and propagation.

The paper modifies the Kuramoto model to include adaptive, phase-dependent delays in white matter networks, showing that these delays stabilize synchronization and enhance resilience to perturbations.

The paper uses a non-linear Kuramoto model to show that scale-free networks enable synchronization in biological systems, suggesting low expression genes are key for biofilm phase transitions.

This paper introduces a method to reduce complex biochemical systems to simpler ones for Turing instability analysis, demonstrating its effectiveness with examples.