arXiv subjects

Michael Chiang

Publications and source records attributed to Michael Chiang.

11 recordsLinked to original sources

arXiv2026

Flow of deformable droplets: self-pinned glasses and string-like flow

We investigate, through numerical simulations, the rheology of a dry suspension of deformable droplets under pressure-driven flow. The system exhibits two force-driven dynamical transitions. At low forcing, the suspension behaves as a yield-stress material: below a critical force, droplets remain arrested in an amorphous solid-like state. Our simulations suggest that yielding is controlled by droplet contacts and predict that the critical force strongly depends on deformability. Above yielding, the suspension does not flow steadily but rather enters an intermittent, stick-slip regime characterised by long-lived caging and non-Gaussian velocity fluctuations. This state can be interpreted as a "self-pinned'' glass, in which slowly evolving droplet overlaps generate an effective rugged energy landscape that dynamically traps droplets and produces intermittent rearrangements reminiscent of near-critical dynamics in depinning models. At larger forcing, droplets deform sufficiently to continuously exchange neighbours, progressively annealing the overlap structure and driving a dynamic transition to a string-like, flowing state. Our results identify the restructuring of overlap networks as a generic mechanism which controls flow in driven suspensions of deformable particles.

cond-mat.soft↗

arXiv2024

Bridging-Induced Phase Separation and Loop Extrusion Drive Noise in Chromatin Transcription

Transcriptional noise, or heterogeneity, is important in cellular development and in disease. The molecular mechanisms driving it are, however, elusive and ill-understood. Here, we use computer simulations to explore the role of 3D chromatin structure in driving transcriptional noise. We study a simple polymer model where proteins - modeling complexes of transcription factors and polymerases - bind multivalently to transcription units - modeling regulatory elements such as promoters and enhancers. We also include cohesin-like factors which extrude chromatin loops that are important for the physiological folding of chromosomes. We find that transcription factor binding creates spatiotemporal patterning and a highly variable correlation time in transcriptional dynamics, providing a mechanism for intrinsic noise within a single cell. Instead, loop extrusion contributes to extrinsic noise, as the stochastic nature of this process leads to different networks of cohesin loops in different cells in our simulations. Our results could be tested with single-cell experiments and provide a pathway to understanding the principles underlying transcriptional plasticity in vivo.

physics.bio-ph↗

arXiv2024

Multiphase Field Model of Cells on a Substrate: From 3D to 2D

Multiphase field models have emerged as an important computational tool for understanding biological tissue while resolving single-cell properties. While they have successfully reproduced many experimentally observed behaviors of living tissue, the theoretical underpinnings have not been fully explored. We show that a two-dimensional version of the model, which is commonly employed to study tissue monolayers, can be derived from a three-dimensional version in the presence of a substrate. We also show how viscous forces, which arise from friction between different cells, can be included in the model. Finally, we numerically simulate a tissue monolayer, and find that intercellular friction tends to solidify the tissue.

cond-mat.soft↗

arXiv2023

Intercellular Friction and Motility Drive Orientational Order in Cell Monolayers

Spatiotemporal patterns in multicellular systems are important to understanding tissue dynamics, for instance, during embryonic development and disease. Here, we use a multiphase field model to study numerically the behavior of a near-confluent monolayer of deformable cells with intercellular friction. Varying friction and cell motility drives a solid-liquid transition, and near the transition boundary, we find the emergence of local nematic order of cell deformation driven by shear-aligning cellular flows. Intercellular friction contributes to the monolayer's viscosity, which significantly increases the spatial correlation in the flow and, concomitantly, the extent of nematic order. We also show that local hexatic and nematic order are tightly coupled and propose a mechanical-geometric model for the colocalization of +1/2 nematic defects and 5-7 disclination pairs, which are the structural defects in the hexatic phase. Such topological defects coincide with regions of high cell-cell overlap, suggesting that they may mediate cellular extrusion from the monolayer, as found experimentally. Our results delineate a mechanical basis for the recent observation of nematic and hexatic order in multicellular collectives in experiments and simulations and pinpoint a generic pathway to couple topological and physical effects in these systems.

cond-mat.soft↗

arXiv2023

Motility induced phase separation of deformable cells

Using a multi-phase field model, we examine how particle deformability, which is a proxy for cell stiffness, affects motility induced phase separation (MIPS). We show that purely repulsive deformable, i.e., squishy, cells phase separate more effectively than their rigid counterparts. This can be understood as due to the fact that deformability increases the effective duration of collisions. In addition, the dense regions become increasingly disordered as deformability increases. Our results contextualize the applicability of MIPS to biological systems and have implications for how cells in biological systems may self-organize

cond-mat.soft↗

arXiv2022

Yield Stress and Compliance in Active Cell Monolayers

The rheology of biological tissue plays an important role in many processes, from organ formation to cancer invasion. Here, we use a multi-phase field model of motile cells to simulate active microrheology within a tissue monolayer. When unperturbed, the tissue exhibits a transition between a solid-like state and a fluid-like state tuned by cell motility and deformability - the ratio of the energetic costs of steric cell-cell repulsion and cell surface tension. When perturbed, solid tissues exhibit yield-stress behavior, with a threshold force for the onset of motion of a probe particle that vanishes upon approaching the solid-to-liquid transition. This onset of motion is qualitatively different in the low and high deformability regimes. At high deformability, the tissue is amorphous when solid, it responds compliantly to deformations, and the probe transition to motion is smooth. At low deformability, the monolayer is more ordered translationally and stiffer, and the onset of motion appears discontinuous. Our results suggest that cellular or nanoparticle transport in different types of tissues can be fundamentally different, and point to ways in which it can be controlled.

cond-mat.soft↗

arXiv2020

Polymer Modelling Predicts Chromosome Reorganisation in Senescence

Lamina-associated domains (LADs) cover a large part of the human genome and are thought to play a major role in shaping the nuclear architectural landscape. Here, we perform polymer simulations, microscopy and mass spectrometry to dissect the roles played by heterochromatin- and lamina-mediated interactions in nuclear organisation. Our model explains the conventional organisation of heterochromatin and euchromatin in growing cells and the pathological organisation found in oncogene-induced senescence and progeria. We show that the experimentally observed changes in the locality of contacts in senescent and progeroid cells can be explained as arising due to phase transitions in the system. Within our simulations LADs are highly stochastic, as in experiments. Our model suggests that, once established, the senescent phenotype should be metastable even if lamina-mediated interactions were reinstated. Overall, our simulations uncover a generic physical mechanism that can regulate heterochromatin segregation and LAD formation in a wide range of mammalian nuclei.

physics.bio-ph↗

arXiv2019

Solid-Liquid Transition of Deformable and Overlapping Active Particles

Experiments and theory have shown that cell monolayers and epithelial tissues exhibit solid-liquid and glass-liquid transitions. These transitions are biologically relevant to our understanding of embryonic development, wound healing, and cancer. Current models typically consider purely two-dimensional monolayers with no overlaps between neighboring cells. In reality, overlaps are important, and they may be precursors of cell extrusion -- a key biophysical process to maintain homeostasis in epithelial tissues. Here, we use a multi-phase field model to study the solid-liquid transition in a confluent monolayer of deformable cells which can overlap. When cells overlap rather than deform, we find that the melting transition changes from continuous to discontinuous, and that there is an intermittent regime close to the transition, where solid and liquid states alternate over time. By studying the dynamics of $5$- and $7$-fold disclinations in the hexagonal lattice formed by the cell centers, we observe that these correlate with spatial fluctuations in the cellular overlap, and that cell extrusion tends to initiate near $5$-fold disclinations.

cond-mat.soft↗

arXiv2019

CaRENets: Compact and Resource-Efficient CNN for Homomorphic Inference on Encrypted Medical Images

Convolutional neural networks (CNNs) have enabled significant performance leaps in medical image classification tasks. However, translating neural network models for clinical applications remains challenging due to data privacy issues. Fully Homomorphic Encryption (FHE) has the potential to address this challenge as it enables the use of CNNs on encrypted images. However, current HE technology poses immense computational and memory overheads, particularly for high-resolution images such as those seen in the clinical context. We present CaRENets: Compact and Resource-Efficient CNNs for high performance and resource-efficient inference on high-resolution encrypted images in practical applications. At the core, CaRENets comprises a new FHE compact packing scheme that is tightly integrated with CNN functions. CaRENets offers dual advantages of memory efficiency (due to compact packing of images and CNN activations) and inference speed (due to the reduction in the number of ciphertexts created and the associated mathematical operations) over standard interleaved packing schemes. We apply CaRENets to perform homomorphic abnormality detection with 80-bit security level in two clinical conditions - Retinopathy of Prematurity (ROP) and Diabetic Retinopathy (DR). The ROP dataset comprises 96 x 96 grayscale images, while the DR dataset comprises 256 x 256 RGB images. We demonstrate over 45x improvement in memory efficiency and 4-5x speedup in inference over the interleaved packing schemes. As our approach enables memory-efficient low-latency HE inference without imposing additional communication burden, it has implications for practical and secure deep learning inference in clinical imaging.

cs.CR↗

arXiv2017

Shaping Epigenetic Memory via Genomic Bookmarking

Reconciling the stability of epigenetic patterns with the rapid turnover of histone modifications and their adaptability to external stimuli is an outstanding challenge. Here, we propose a new biophysical mechanism that can establish and maintain robust yet plastic epigenetic domains via genomic bookmarking (GBM). We model chromatin as a recolourable polymer whose segments bear non-permanent histone marks (or colours) which can be modified by "writer" proteins. The three-dimensional chromatin organisation is mediated by protein bridges, or "readers", such as Polycomb Repressive Complexes and Transcription Factors. The coupling between readers and writers drives spreading of biochemical marks and sustains the memory of local chromatin states across replication and mitosis. In contrast, GBM-targeted perturbations destabilise the epigenetic patterns. Strikingly, we demonstrate that GBM alone can explain the full distribution of Polycomb marks in a whole Drosophila chromosome. We finally suggest that our model provides a starting point for an understanding of the biophysics of cellular differentiation and reprogramming.

q-bio.SC↗

arXiv2014

POPE: Post Optimization Posterior Evaluation of Likelihood Free Models

In many domains, scientists build complex simulators of natural phenomena that encode their hypotheses about the underlying processes. These simulators can be deterministic or stochastic, fast or slow, constrained or unconstrained, and so on. Optimizing the simulators with respect to a set of parameter values is common practice, resulting in a single parameter setting that minimizes an objective subject to constraints. We propose a post optimization posterior analysis that computes and visualizes all the models that can generate equally good or better simulation results, subject to constraints. These optimization posteriors are desirable for a number of reasons among which easy interpretability, automatic parameter sensitivity and correlation analysis and posterior predictive analysis. We develop a new sampling framework based on approximate Bayesian computation (ABC) with one-sided kernels. In collaboration with two groups of scientists we applied POPE to two important biological simulators: a fast and stochastic simulator of stem-cell cycling and a slow and deterministic simulator of tumor growth patterns.

stat.ML↗