Conference Agenda

The stomach plays a central role in digestion by accommodating ingested food, mechanically mixing its contents, and chemically breaking down nutrients. These functions are tightly orchestrated by gastric peristalsis—a complex process governed by an intricate electromechanical system that ensures the effective propulsion of food into the intestines. A detailed understanding of the mechanics and regulation of gastric motility is therefore crucial for diagnosing and treating prevalent gastrointestinal disorders such as gastroparesis, gastroesophageal reflux disease (GERD), and functional dyspepsia, all of which can significantly impair quality of life.

Despite its clinical relevance, gastric electromechanics remain poorly understood, even at a fundamental biophysical level. Recent advances in computational modeling offer promising opportunities to investigate gastric function. While experimental studies are indispensable, computational models offer a unique advantage: they enable the exploration of hypothetical physiological configurations and states that are difficult or impossible to replicate in the lab. This makes them powerful tools for efficiently testing and comparing competing biophysical hypotheses.

However, the evaluation of complex models across many different scenarios poses a significant challenge, as the computational cost can become prohibitively high. To address this, we introduce a framework for automated model evaluation in multi-query settings, encompassing parameter studies, sensitivity analysis, uncertainty quantification, and Bayesian parameter identification (i.e., model calibration). We demonstrate how even relatively simple methods—such as structured designs of experiments—can provide valuable insights and enable systematic comparisons of competing hypotheses. A key enabler of more advanced, large-scale studies is the use of surrogate modeling, which dramatically reduces computational costs and facilitates more sophisticated analyses such as global sensitivity analysis.

Using this framework, we for example show that replicating key features of gastric electromechanics—such as the extreme deformations observed experimentally in the fasted stomach—requires very specific conditions. In particular, our results highlight the need for a finely tuned coordination between contractions in the circumferential and longitudinal muscle layers. This emphasizes the critical coupling between electrical excitation and large-scale, nonlinear tissue deformation that underlies normal gastric function.

By integrating multi-query analysis techniques with advanced computational models—such as a patient-specific active-strain electromechanical model of the human stomach—we uncover novel insights into the biomechanics of gastric motility. This approach provides a powerful tool for deepening our understanding of both normal physiology and motility disorders. Ultimately, we envision this work as a bridge between computational modeling and clinical practice, paving the way for model-based diagnostics and treatment planning for disorders such as gastroparesis, GERD, and dyspepsia.

Introduction:
Biomechanical and electrochemical computational models of the stomach contraction have advanced significantly over the past two decades, progressing from single-cell simulations to whole-organ ones. More recently, chemo-electromechanical models have emerged, aiming to simulate the relationship between ion concentrations, electric potentials generated by the smooth muscles, and tissue contraction dynamics. The model presented here builds on these foundations by simulating slow wave generation and transmission across an anatomically inspired stomach scaffold, while independently visualizing smooth muscle contractions. A novel aspect of this work is the incorporation of a neural control mechanism that simulates the effect of neurotransmitters such as acetylcholine and nitric oxide on slow wave propagation and muscle contraction across the entire stomach.

Method:
The model couples a tridomain electrophysiological framework with a biomechanical spring-based model. The tridomain equations govern the propagation of slow waves through interstitial cells of Cajal, smooth muscle cells, and a fixed homogenous extracellular domain. Neural regulation pathways affecting specific ionic currents were incorporated based on recent physiological data, modulating excitatory and inhibitory effects through parameters such as Ano1 and NSCC channel conductances. The mechanical behavior was modeled by linking intracellular calcium concentration to tissue tension using Hill-type equations, and displacements were visualized through a damped harmonic oscillator analogy. The anatomical scaffold was derived from fitting CT images obtained from NIH’s SPARC database, and later processed into a finite element mesh in MATLAB, over which the model was solved using isotropic assumptions with Dirichlet and Neumann boundary conditions. Electrophysiological simulations were performed in MATLAB, while scaffold displacements were visualized in Python.

Results:
Simulation results demonstrated realistic propagation of slow waves originating at the gastric pacemaker region, traveling at physiological frequencies (~3 cycles per minute) towards the pylorus, with circumferential conduction velocities approximately double the longitudinal ones. The model also showed the expected frequency and amplitude modulation under neural stimulation: excitatory neural inputs increased wave amplitude by approximately 2.9 mV and raised contraction frequency to 4 cpm, while inhibitory inputs significantly reduced contractile tension by up to 29 kPa. Displacement simulations showed tissue deformations ranging from 0 mm at rest to approximately 2.9 mm during contraction, consistent with physiological observations. Validation was achieved through qualitative comparison with previous computational models of gastric motility, confirming correct slow wave directionality, frequency, and muscle deformation magnitude.

Conclusion and Future Work:

These results highlight the model's capability to simulate gastric motility with neural control on an anatomically inspired scaffold. However, in order for this model to serve as a platform for the design and in-silica testing of pharmaceutical drugs among other clinical applications, it requires further enhancements. Main aspects that will require future attention is the implementation of an anisotropic framework, a more accurate biomechanical model compared to the current Hill model and the incorporation of more neural control pathways.

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.