Conference Agenda

Background:

Gastric slow waves, generated and propagated by interstitial cells of Cajal (ICC), coordinate stomach motility and are essential for healthy digestion. Dysrhythmic slow wave activity is increasingly recognized as a biomarker for motility disorders such as gastroparesis and functional dyspepsia. Multiscale modeling of gastric electrophysiology linking structural, tissue, and organ-level dynamics enhances our understanding of gastric motility mechanisms and offers a pathway toward elucidating pathological changes and enabling non-invasive diagnostics.

Methods:

A combination of multiscale experimental and computational techniques was employed to investigate how ICC microstructure affects slow wave dynamics, and how these dynamics manifest at the organ and far-field levels. Detailed ICC networks in murine gastric tissues were imaged using multiphoton confocal microscopy. Region-specific continuum models of ICC networks in the antrum and corpus were constructed using machine learning-based segmentation and ICC density and morphological metrics were quantified. The impact of ICC structures on slow wave propagation was investigated using a self-excitatory ICC cell model. At the organ level, anatomically realistic stomach models were developed, and simulations of normal and dysrhythmic slow wave patterns informed by in-vivo high-resolution gastric mapping were performed. Additionally, forward simulations of biomagnetic fields were conducted using volume current source formulations. Simulated biomagnetic signals were compared against experimental recordings from porcine models undergoing simultaneous high-resolution serosal mapping and biomagnetic acquisition. Finally, an anatomically-constrained source imaging method was developed to map biomagnetic signals back to the stomach surface and reconstruct slow wave activation sequences.

Results:

Microscopic analysis revealed significant regional differences in ICC density and thickness across the stomach. The antrum exhibited the highest ICC density (13.0±5.1%), followed by the corpus (6.0±1.9%) and fundus (1.5±0.5%), which lacked myenteric plexus ICCs. These variations produced marked effects on slow wave speed and synchrony, with the antrum supporting faster, more coherent propagation. In the forward modeling analysis, simulated biomagnetic signals based on serosal activation patterns reproduced key features of experimental data, including waveform morphology and spatial distribution. Anatomically-based source imaging accurately reconstructed slow wave propagation patterns including simultaneous ectopic and normal wavefronts with mean localization errors within 6–14% of the longitudinal axis of the stomach.

Conclusion:

This multiscale framework, linking ICC structure to tissue-level slow wave behavior and far-field biomagnetic signatures, provides a foundation for non-invasive diagnosis of gastric dysrhythmias. The validated source imaging approach enables spatially resolved estimation of slow wave propagation from surface biomagnetic recordings offering a novel non-invasive clinical biomarker for the diagnosis of gastric motility disorders.

The stomach plays a fundamental role in digestion through accommodation, mechanical mixing, and chemical breakdown of food. Gastric peristalsis, a coordinated process of muscular contractions, ensures the efficient mixing and propulsion of food for subsequent digestion and absorption in the intestines. These contractions are governed by a complex electromechanical system, making a detailed understanding of gastric motility essential for diagnosing and treating gastrointestinal disorders such as gastroparesis, gastroesophageal reflux disease, and dyspepsia.

Despite its physiological importance, gastric biomechanics remains poorly understood, both in terms of biophysical mechanisms and computational modeling. While computational modeling has emerged as a promising, non-invasive tool for studying biomechanical systems, particularly when informed by medical imaging, there remains a scarcity of robust, physiologically accurate models that comprehensively capture the biomechanical complexity of gastric motility. Whole-stomach electromechanical models will provide valuable insights. However, whole-stomach models face critical computational challenges, including large deformations in nonlinear domains and patient-specific anatomical variability.

To address these challenges, we present a robust computational multiphysics framework for modeling human gastric motility using patient-specific geometries. Our approach integrates key biomechanical factors, including prestress, regional material properties, and non-uniform boundary conditions. Additionally, it incorporates an anisotropic active strain formulation within a homogenized constrained mixture framework, enabling more accurate simulations of structural and material heterogeneities in gastric biomechanics. Although patient-specific geometries can be reconstructed from medical imaging, crucial biomechanical properties—such as smooth muscle and collagen fiber orientations and the regional distribution of material parameters—are challenging to obtain directly. To overcome this, we introduce a novel workflow leveraging Laplace-Dirichlet-based reparameterization to infer physiological principal fiber orientations and spatial material distributions.

The proposed computational model successfully replicates essential features of gastric electromechanics, including slow wave entrainment and the physiologically accurate propagation of ring-shaped peristaltic contraction waves with large deformations. These findings emphasize the intricate coupling between electrical excitation and large-scale nonlinear tissue deformation. Moreover, the model’s adaptability to patient-specific geometries allows for precise representation of anatomical and physiological variations, which is crucial for understanding individual differences in gastric motility.

By integrating computational techniques with patient-specific imaging data, our framework enables large-scale in-silico studies of gastric motility. This approach provides a powerful tool for improving diagnostic accuracy, advancing the understanding of gastric motility disorders, and developing personalized treatment strategies. Ultimately, this work bridges the gap between computational modeling and clinical practice, offering a pathway toward more precise, patient-tailored interventions for disorders such as gastroparesis, gastroesophageal reflux disease, and dyspepsia.

The intestine plays a fundamental role in the digestive process, facilitating the transport and absorption of nutrients through complex motility patterns such as peristalsis and segmentation. Despite its importance, the precise mechanisms governing these movements remain only partially understood, making their mathematical modeling particularly challenging. Over the years, researchers have developed a variety of multiphysics and multiscale models to describe the intestine’s electrophysiology, mechanical behavior, and electromechanical interactions [1-2]. However, a crucial aspect that has been largely overlooked in existing models is the role of contact and self-contact during intestinal contractions.

In physiological conditions, different sections of the intestine may come into contact due to its natural motility. This phenomenon is further exacerbated in certain pathological conditions, where abnormal contractions or changes in tissue stiffness can lead to excessive compression or folding.This may lead to pathological conditions such as herniation or adherent syndromes causing obstructions. Moreover, during peristaltic contractions or segmentation, the inner surface of the intestine can collapse onto itself, leading to a form of internal self-contact that influences mechanical stress distribution and chyme transport. To the best of our knowledge, no existing model explicitly accounts for these contact phenomena in the simulation of intestinal motility.

To address this limitation, we propose a novel electromechanical model tailored for patient-specific simulations of intestinal motility, explicitly incorporating self-contact. Our approach builds upon the framework introduced in [1], utilizing the active strain approach to couple the electrophysiological and mechanical components of intestinal contractions. Additionally, we implement an unbiased contact formulation based on the Nitsche method [3], which eliminates the traditional master-slave hierarchy, ensuring a more robust and symmetric treatment of contact interactions. The proposed methodology is validated through benchmark tests before being applied to investigate self-contact phenomena in the duodenum and colon, providing new insights into the mechanical behavior of intestinal motility.

Keywords: gastrointestinal motility, unbiased contact method, electromechanics, patient-specific

References

[1] R. T. Djoumessi, P. Lenarda, A. Gizzi, S. Giusti, P. Alduini, and M. Paggi, In silico model of colon electromechanics for manometry prediction after laser tissue soldering. Comput. Methods Appl. Mech. Eng. (2024) 426: 116989.

[2] P. Du, S. Calder, T. R. Angeli, S. Sathar, N. Paskaranandavadivel, G. O'Grady, and L. K. Cheng, Progress in mathematical modeling of gastrointestinal slow wave abnormalities. Front. Physiol. (2018) 8:113

[3] R. Mlika, Y. Renard, and F. Chouly, An unbiased Nitsche’s formulation of large deformation frictional contact and self-contact. Comput. Methods Appl. Mech. Eng. (2017) 325: 265–288.

A properly functioning gastrointestinal (GI) tract plays a vital role in digestion, nutrient absorption, microbial homeostasis, and motility. GI pathologies can severely impair these functions, often necessitating surgical intervention involving resection, excision, or anastomosis, depending on the specific pathology. Post-surgical healing of GI tissues proceeds through the canonical phases of hemostasis, inflammation, proliferation, and remodeling. Even though, recent years have seen significant advances in electromechanical modeling [1] of GI tissues, and 3D bioprinting-based scaffolds have emerged to support both tissue repair and inflammation control, challenges persist in controlling and optimizing GI healing process.

Anastomotic leakage and dehiscence remain critical postoperative complications, associated with high morbidity and mortality. Although robust, clinically validated models exist for skin wound healing [2], analogous models for GI tissue are lacking due to the anatomical and physiological complexities of the GI environment. In recent developments, hydrogel-based biomaterials [3] are increasingly replacing traditional staples and sutures for GI tissue repair. However, the healing mechanisms associated with such materials also remain poorly understood. To the best of our knowledge, no existing model integrates self-healing and scaffold-based regeneration to elucidate the GI healing process.

We couple an electro-mechanical model of GI tissue with a scaffold-based healing framework to investigate both intrinsic and biomaterial-assisted regeneration. The model is used to optimize scaffold parameters for accelerated healing by modulating key biological markers such as cell density, cytokine concentration, collagen fibers alignment, and tissue contraction. The problem will be formulated as a dynamical system in which the control variables are the material and chemo-diffusive parameters of the 3D bioprinted construct evolving in time, and the target state is the restored GI physiological motility. Optimization algorithms will be used to minimize the loss function and find the best set of material parameters that the new biomaterial requires to restore the integrity of the host tissue. This study will enable a mechanistic understanding of scaffold-mediated healing, offering a computational tool for guiding biomaterial design in GI surgery.

References:

[1] Djoumessi RT, Lenarda P, Gizzi A, Giusti S, Alduini P, Paggi M. In silico model of colon electromechanics for manometry prediction after laser tissue soldering. Comput Methods Appl Mech Eng 2024;426.

[2] Sohutskay OP, Tepole AB,Voytik-Harbin SL. Mechanobiological wound model for improved design and evaluation of collagen dermal replacement scaffolds. Acta Biomater 2021;135.

[3] Su Y, Ju J, Shen C, Li Y, Yang W, Luo X, et al. In situ 3D bioprinted GDMA/Prussian blue nanozyme hydrogel with wet adhesion promotes macrophage phenotype modulation and intestinal defect repair. Mater Today Bio 2025;31:101636.