A systematic approach for developing mechanistic models for realistic simulation of cancer cell motion and deformation

Lattice Models: Non-Conventional simulation methods for mechanobiology

Computational methods represent a powerful tool to explore biophysical phenomena occurring at small scales and hence difficult to observe through experimental setups. In detail, they can provide a support to mechanobiology, with the aim of understanding the behavior of living cells interacting with the surrounding environment. To this end, lattice models can provide a simulation framework that is highly reliable and easy to implement, even for simulations involving large deformations and topological changes during time evolution. In this review article, elastic network models for studying biological molecules are described, several lattice spring models for investigating cell behaviors are discussed, and the adoption of lattice beam models for biomimetic structures design is presented. The lattice modelling approaches could be regarded as a valuable option to conduct in-silico experiments and consolidate the emergent mechanobiology research field.

Modeling the dynamics of circulating tumor cell clusters inside a microfluidic channel

Circulating tumor cells are central to metastasis, a particularly malign spread of cancer beyond its original location. While rare, there is growing evidence that the clusters of circulating tumor cells are significantly more harmful than individual cells. Microfluidic platforms constitute the core of circulating tumor cell cluster research, allowing cluster detection, analysis, and treatment. In this work, we propose a new mathematical model of circulating tumor cell clusters and apply it to simulate the dynamics of the aggregates inside a microfluidic channel with the external flow of a fluid. We leverage our previous model of the interactions of circulating tumor cells with varying clustering affinities and introduce explicit bonds between the cells that makeup a cluster. We show that the bonds have a visible impact on the cluster dynamics and that they enable the reproduction of known cluster flow and deformation patterns. Furthermore, we demonstrate that the dynamics of these aggregates are sensitive to bond properties, as well as initialization and flow conditions. We believe that our modeling framework represents a valuable mesoscopic formulation with an impact beyond circulating tumor cell clusters, as cell aggregates are common in both nature and applications.

Numerical study of ultra-large von Willebrand factor multimers in coagulopathy

An excessive von Willebrand factor (VWF) secretion, coupled with a moderate to severe deficiency of ADAMTS13 activity, serves as a linking mechanism between inflammation to thrombosis. The former facilitates platelet adhesion to the vessel wall and the latter is required to cleave VWF multimers. As a result, the ultra-large VWF (UL-VWF) multimers released by Weibel-Palade bodies remain uncleaved. In this study, using a computational model based on first principles, we quantitatively show how the uncleaved UL-VWF multimers interact with the blood cells to initiate microthrombosis. We observed that platelets first adhere to unfolded and stretched uncleaved UL-VWF multimers anchored to the microvessel wall. By the end of this initial adhesion phase, the UL-VWF multimers and platelets make a mesh-like trap in which the red blood cells increasingly accumulate to initiate a gradually growing microthrombosis. Although high-shear rate and blood flow velocity are required to activate platelets and unfold the UL-VWFs, during the initial adhesion phase, the blood velocity drastically drops after thrombosis, and as a result, the wall shear stress is elevated near UL-VWF roots, and the pressure drops up to 6 times of the healthy condition. As the time passes, these trends progressively continue until the microthrombosis fully develops and the effective size of the microthrombosis and these flow quantities remain almost constant. Our findings quantitatively demonstrate the potential role of UL-VWF in coagulopathy.

Experimental measurement and numerical modeling of deformation behavior of breast cancer cells passing through constricted microfluidic channels

During the multistep process of metastasis, cancer cells encounter various mechanical forces which make them deform drastically. Developing accurate in-silico models, capable of simulating the interactions between the mechanical forces and highly deformable cancer cells, can pave the way for the development of novel diagnostic and predictive methods for metastatic progression. Spring-network models of cancer cell, empowered by our recently proposed identification approach, promises a versatile numerical tool for developing experimentally validated models that can simulate complex interactions at cellular scale. Using this numerical tool, we presented spring-network models of breast cancer cells that can accurately replicate the experimental data of deformation behavior of the cells flowing in a fluidic domain and passing narrow constrictions comparable to microcapillary. First, using high-speed imaging, we experimentally studied the deformability of breast cancer cell lines with varying metastatic potential (MCF-7 (less invasive), SKBR-3 (medium-high invasive), and MDA-MB-231 (highly invasive)) in terms of their entry time to a constricted microfluidic channel. We observed that MDA-MB-231, that has the highest metastatic potential, is the most deformable cell among the three. Then, by focusing on this cell line, experimental measurements were expanded to two more constricted microchannel dimensions. The experimental deformability data in three constricted microchannel sizes for various cell sizes, enabled accurate identification of the unknown parameters of the spring-network model of the breast cancer cell line (MDA-MB-231). Our results show that the identified parameters depend on the cell size, suggesting the need for a systematic procedure for identifying the size-dependent parameters of spring-network models of cells. As the numerical results show, the presented cell models can simulate the entry process of the cell into constricted channels with very good agreements with the measured experimental data.

Impact of extra-anatomical bypass on coarctation fluid dynamics using patient-specific lumped parameter and Lattice Boltzmann modeling

Accurate hemodynamic analysis is not only crucial for successful diagnosis of coarctation of the aorta (COA), but intervention decisions also rely on the hemodynamics assessment in both pre and post intervention states to minimize patient risks. Despite ongoing advances in surgical techniques for COA treatments, the impacts of extra-anatomic bypass grafting, a surgical technique to treat COA, on the aorta are not always benign. Our objective was to investigate the impact of bypass grafting on aortic hemodynamics. We investigated the impact of bypass grafting on aortic hemodynamics using a patient-specific computational-mechanics framework in three patients with COA who underwent bypass grafting. Our results describe that bypass grafting improved some hemodynamic metrics while worsened the others: (1) Doppler pressure gradient improved (decreased) in all patients; (2) Bypass graft did not reduce the flow rate substantially through the COA; (3) Systemic arterial compliance increased in patients #1 and 3 and didn't change (improve) in patient 3; (4) Hypertension got worse in all patients; (5) The flow velocity magnitude improved (reduced) in patient 2 and 3 but did not improve significantly in patient 1; (6) There were elevated velocity magnitude, persistence of vortical flow structure, elevated turbulence characteristics, and elevated wall shear stress at the bypass graft junctions in all patients. We concluded that bypass graft may lead to pseudoaneurysm formation and potential aortic rupture as well as intimal hyperplasia due to the persistent abnormal and irregular aortic hemodynamics in some patients. Moreover, post-intervention, exposures of endothelial cells to high shear stress may lead to arterial remodeling, aneurysm, and rupture.

Data integration and mechanistic modelling for breast cancer biology: Current state and future directions

Breast cancer is one of the most common cancers threatening women worldwide. A limited number of available treatment options, frequent recurrence, and drug resistance exacerbate the prognosis of breast cancer patients. Thus, there is an urgent need for methods to investigate novel treatment options, while taking into account the vast molecular heterogeneity of breast cancer. Recent advances in molecular profiling technologies, including genomics, epigenomics, transcriptomics, proteomics and metabolomics data, enable approaching breast cancer biology at multiple levels of omics interaction networks. Systems biology approaches, including computational inference of ‘big data’ and mechanistic modelling of specific pathways, are emerging to identify potential novel combinations of breast cancer subtype signatures and more diverse targeted therapies.

Reducing Morbidity and Mortality in Patients With Coarctation Requires Systematic Differentiation of Impacts of Mixed Valvular Disease on Coarctation Hemodynamics

Background Despite ongoing advances in surgical techniques for coarctation of the aorta (COA) repair, the long-term results are not always benign. Associated mixed valvular diseases (various combinations of aortic and mitral valvular pathologies) are responsible for considerable postoperative morbidity and mortality. We investigated the impact of COA and mixed valvular diseases on hemodynamics. Methods and Results We developed a patient-specific computational framework. Our results demonstrate that mixed valvular diseases interact with COA fluid dynamics and contribute to speed up the progression of the disease by amplifying the irregular flow patterns downstream of COA (local) and exacerbating the left ventricular function (global) (N=26). Velocity downstream of COA with aortic regurgitation alone was increased, and the situation got worse when COA and aortic regurgitation coexisted with mitral regurgitation (COA with normal valves: 5.27 m/s, COA with only aortic regurgitation: 8.8 m/s, COA with aortic and mitral regurgitation: 9.36 m/s; patient 2). Workload in these patients was increased because of the presence of aortic stenosis alone, aortic regurgitation alone, mitral regurgitation alone, and when they coexisted (COA with normal valves: 1.0617 J; COA with only aortic stenosis: 1.225 J; COA with only aortic regurgitation: 1.6512 J; COA with only mitral regurgitation: 1.3599 J; patient 1). Conclusions Not only the severity of COA, but also the presence and the severity of mixed valvular disease should be considered in the evaluation of risks in patients. The results suggest that more aggressive surgical approaches may be required, because regularly chosen current surgical techniques may not be optimal for such patients.