An agent-based model of the response to angioplasty and bare-metal stent deployment in an atherosclerotic blood vessel

A predictive multiscale model of in-stent restenosis in femoral arteries: linking haemodynamics and gene expression with an agent-based model of cellular dynamics

In-stent restenosis (ISR) is a maladaptive inflammatory-driven response of femoral arteries to percutaneous transluminal angioplasty and stent deployment, leading to lumen re-narrowing as consequence of excessive cellular proliferative and synthetic activities. A thorough understanding of the underlying mechanobiological factors contributing to ISR is still lacking. Computational multiscale models integrating both continuous- and agent-based approaches have been identified as promising tools to capture key aspects of the complex network of events encompassing molecular, cellular and tissue response to the intervention. In this regard, this work presents a multiscale framework integrating the effects of local haemodynamics and monocyte gene expression data on cellular dynamics to simulate ISR mechanobiological processes in a patient-specific model of stented superficial femoral artery. The framework is based on the coupling of computational fluid dynamics simulations (haemodynamics module) with an agent-based model (ABM) of cellular activities (tissue remodelling module). Sensitivity analysis and surrogate modelling combined with genetic algorithm optimization were adopted to explore the model behaviour and calibrate the ABM parameters. The proposed framework successfully described the patient lumen area reduction from baseline to one-month follow-up, demonstrating the potential capabilities of this approach in predicting the short-term arterial response to the endovascular procedure.

Role of Animal Models in Coronary Stenting

Coronary angioplasty initially employed balloon dilatation only. This technique revolutionized the treatment of coronary artery disease, although outcomes were compromised by acute vessel closure, late constrictive remodeling, and restenosis due to neointimal proliferation. These processes were studied in animal models, which contributed to understanding the biology of endovascular arterial injury. Coronary stents overcome acute recoil, with improvements in the design and metallurgy since then, leading to the development of drug-eluting stents and bioresorbable scaffolds. These devices now undergo computer modeling and benchtop and animal testing before evaluation in clinical trials. Animal models, including rabbit, sheep, dog and pig are available, all with individual benefits and limitations. In smaller mammals, such as mouse and rabbit, the target for stenting is generally the aorta; whereas in larger animals, such as the pig, it is generally the coronary artery. The pig coronary stenting model is a gold-standard for evaluating safety; but insights into biomechanical properties, the biology of stenting, and efficacy in controlling neointimal proliferation can also be gained. Intra-coronary imaging modalities such as intravascular ultrasound and optical coherence tomography allow precise serial evaluation in vivo, and recent developments in genetically modified animal models of atherosclerosis provide realistic test beds for future stents and scaffolds.

Agent-based spatiotemporal simulation of biomolecular systems within the open source MASON framework

Agent-based modelling is being used to represent biological systems with increasing frequency and success. This paper presents the implementation of a new tool for biomolecular reaction modelling in the open source Multiagent Simulator of Neighborhoods framework. The rationale behind this new tool is the necessity to describe interactions at the molecular level to be able to grasp emergent and meaningful biological behaviour. We are particularly interested in characterising and quantifying the various effects that facilitate biocatalysis. Enzymes may display high specificity for their substrates and this information is crucial to the engineering and optimisation of bioprocesses. Simulation results demonstrate that molecule distributions, reaction rate parameters, and structural parameters can be adjusted separately in the simulation allowing a comprehensive study of individual effects in the context of realistic cell environments. While higher percentage of collisions with occurrence of reaction increases the affinity of the enzyme to the substrate, a faster reaction (i.e., turnover number) leads to a smaller number of time steps. Slower diffusion rates and molecular crowding (physical hurdles) decrease the collision rate of reactants, hence reducing the reaction rate, as expected. Also, the random distribution of molecules affects the results significantly.