Biology subjects

Holland, M. A.

Publications and source records attributed to Holland, M. A..

5 recordsLinked to original sources

bioRxiv2026

Inference of self-limiting neutrophil swarming dynamics using Bayesian physics-informed neural networks

Neutrophil swarming is a critical immune response in mammals and fish, in which neutrophils are recruited to inflammatory sites where they coordinate into a swarm that neutralizes pathogens. While excessive swarming can drive prolonged inflammation, a quantitative understanding of swarming dynamics remains limited. We developed a one-dimensional radial reaction-diffusion model of neutrophil swarming with two kinetic parameters, in order to capture the self-limiting swarming dynamics in both murine and human neutrophils in response to different inflammatory stimulus sizes. To ensure that the inverse problem is well-posed, we first performed sensitivity and identifiability analyses. We then developed a physics-informed neural network (PINN) to infer the key parameters governing swarm expansion and self-limitation. To account for uncertainty in noisy experimental measurements, we further extended this framework to a Bayesian PINN (B-PINN), which provides credible intervals for the inferred parameters. Both models were validated against synthetic data generated by numerical simulation and subsequently applied to in vitro experimental data from human and murine neutrophils in response to three bioparticle cluster sizes. The PINN-inferred dynamics show that larger bioparticle clusters are associated with greater cumulative recruitment and larger swarms in both species. The models further reveal species-specific differences in both the amplitude of initial recruitment and the timescale on which it self-limits. Additionally, the B-PINN posterior distributions quantify uncertainty in these species- and cluster size-dependent trends and identify where additional measurements would be most informative. To our knowledge, this is the first application of physics-informed machine learning to model neutrophil swarming dynamics. This framework provides a starting point for systematically comparing recruitment dynamics between human and murine neutrophils and offers guidance for future experimental design.

systems biology↗

bioRxiv2026

A Biophysical Model of Human Colonic Motor Pattern Generation in Health and Disease

PurposeColonic motility disorders, including diarrhea-predominant irritable bowel syndrome and slow-transit constipation, impose a major clinical burden. Although high-resolution colonic manometry reveals characteristic spatiotemporal motor patterns, such as high-amplitude propagating contractions and cyclic motor pattern in healthy individuals, these patterns are often altered or absent in disease. Understanding how these patterns arise from underlying pacemaker, neural, and mechanical mechanisms is essential for improving treatment strategies. MethodsWe developed a biophysical whole-colon model that integrates an Interstitial Cells of Cajal-inspired oscillator network, enteric nervous system reflexes, a pressure-gated modulation element motivated by rectosigmoid brake behavior, and a nonlinear tube law describing colon wall mechanics. The model simulates spatiotemporal pressure patterns along the colon and allows systematic variation of physiological parameters associated with pacemaker activity, neural reflex control, and distal gating. ResultsA small set of parameters reproduces three illustrative motility patterns corresponding to healthy motility, diarrhea-predominant irritable bowel syndrome, and slow-transit constipation. The simulated pressure maps recapitulate key features observed in high-resolution manometry, including propagation direction, regional patterning of contractions, and case-specific changes in amplitude and coordination. Sensitivity analysis suggests that proximal excitation strength and waveform morphology strongly influence global motility metrics. ConclusionOur study presents a simple, biophysical framework for reproducing clinically observed colonic motor patterns and exploring their disruption in disease. More broadly, the model may help interpret clinical manometry in mechanistic terms and support hypothesis-driven in silico studies of colonic motility disorders.

biophysics↗

bioRxiv2026

Modeling Inflammation-Driven Colon Hypertrophy and Motility Changes in Gulf War Illness

Gastrointestinal (GI) symptoms are a prominent feature of Gulf War Illness (GWI). Animal models attribute them to pyridostigmine bromide (PB) exposure, which induces smooth muscle hypertrophy, neuroinflammation, and motility impairment. However, animal studies only provide static snapshots of disease progression and can only partially resolve how inflammatory, neuronal, and biomechanical processes interact dynamically over time. To address this gap, we developed a computational model that couples cytokine kinetics, macrophage activation, and an excitatory-inhibitory neuronal imbalance to predict smooth muscle hypertrophy and colonic motility changes in GWI. The model was calibrated using data from mice exposed to PB under acute (7-day exposure and measurement) and chronic (7-day exposure and 30-day measurement) conditions, reproducing measured cytokine IL-6 elevations, macrophage accumulation, circular muscle thickening, and shifts in excitatory and inhibitory gene expression. Simulations captured reduced excitatory stress, and sustained loss of inhibitory relaxation, consistent with organ-bath recordings. Sensitivity analyses identified macrophage persistence as a dominant regulator of chronic inhibitory dysfunction, whereas excitatory pathways exhibited relative robustness and recovery. Thus, our model provides a systems-level view of how acute PB-induced inflammation evolves into chronic dysmotility and establishes a first step towards a virtual platform for testing hypotheses and interventions translatable to neuroimmune GI disorders. HighlightsO_LINeuroinflammation model predicts colon hypertrophy and motility in GWI C_LIO_LICalibrated to acute (day 7) and chronic (day 30) PB-exposed mouse data C_LIO_LIReproduced IL-6 rise, CD40+ persistence, colon thickening, and ChAT/Nos1 shifts C_LIO_LIPredicted excitatory stress rebound but sustained inhibitory relaxation loss C_LI

bioengineering↗

bioRxiv2025

Astrocytes in white matter respond to tensile cues during cortical folding: a numerical study

Our understanding of the process of formation of gyri (ridges) and sulci (furrows) in the cerebrum during development is moving beyond the role of neurons. Glial cells such as astrocytes, which are the most common cell type in the brain, are especially prominent under the gyri and have been shown to be essential for gyrification in ferrets. Their dysfunction has been linked to a host of neurodevelopmental diseases and disorders in humans, leading to abnormal folding patterns, hence, it is crucial to understand their role in the mechanics of folding. In this work, we propose two hypotheses of how astrocytes affect cortical folding. Our previous study demonstrated that astrocytes proliferate in the white matter (subcortex), and that inhibiting this process impairs gyrification. This leads to the pushing hypothesis, where astrocytes push up the cortex outwards, leading to formation of folds. On the other hand, ex vivo studies demonstrate areas of the cortex and subcortex that experience tension, due to the differential growth between materials in the brain tissue. This leads to the pulling hypothesis, where astrocytes experience tension from the surrounding tissue, leading to their proliferation, distribution of tensile stresses, and initiation of growth in those regions. Using the theory of finite growth, we implement these hypotheses via morphogenetic growth (pushing) and stress-driven (pulling) growth criteria. We find that morphological trends during development between the pushing and pulling effects are not dissimilar, with a comparable gyrification index. The stress distributions from both models also show common features, but the pulling effect shows tension in the subcortex, which matches trends observed in experiments. Therefore, it is more likely that the astrocytes affect gyrification by proliferating as a response to tensile cues and decrease the tension they experience, leading to deeper folds, rather than astrocytes independently proliferating under a gyri and then pushing the cortex up.

bioengineering↗

bioRxiv2025

Compensating Cortical Thickness for Cortical Folding-Related Variation

Cortical thickness is a widely used biomarker of brain morphology and health, yet it is dependent on local cortical folding. Because gyral crowns are consistently thicker than sulcal fundi and cortical folds vary widely across individuals, these fluctuations introduce unmodeled nuisance variance that can obscure meaningful biological effects of interest. Previous global methods of folding compensation incompletely compensate for folding effects on cortical thickness. Spatial smoothing is commonly used to reduce these effects in the literature, but this markedly degrades spatial localization precision. To address these limitations, we developed a novel method for folding-compensated cortical thickness estimation that uses nonlinear local multiple regression with five folding measures to model and more completely remove folding-related variance from cortical thickness. This approach estimates what cortical thickness would have been in the absence of folding, yielding a more biologically interpretable measure of cortical architecture. We applied this new approach to data from the Young Adult Human Connectome Project (HCP-YA) and Aging Human Connectome Project (HCA), demonstrating substantial reductions in intra-areal and inter-individual variability, substantially increasing standardized effect sizes of age on cortical thickness (41% increase) while preserving neurobiologically expected patterns, and avoiding the loss of spatial precision that occurs with the spatial smoothing that has traditionally been used in the literature. The method has been integrated into the HCP pipelines, facilitating its widespread use. By attenuating folding-induced variability, this technique enhances cortical thickness as a structural phenotype and may support more accurate cortical parcellation, longitudinal tracking, and biomarker discovery in brain health and disease.

neuroscience↗