BioDynaMo: a modular platform for high-performance agent-based simulation.

MOTIVATION: Agent-based modeling is an indispensable tool for studying complex biological systems. However, existing simulation platforms do not always take full advantage of modern hardware and often have a field-specific software design. RESULTS: We present a novel simulation platform called BioDynaMo that alleviates both of these problems. BioDynaMo features a modular and high-performance simulation engine. We demonstrate that BioDynaMo can be used to simulate use cases in: neuroscience, oncology and epidemiology. For each use case, we validate our findings with experimental data or an analytical solution. Our performance results show that BioDynaMo performs up to three orders of magnitude faster than the state-of-the-art baselines. This improvement makes it feasible to simulate each use case with one billion agents on a single server, showcasing the potential BioDynaMo has for computational biology research. AVAILABILITY AND IMPLEMENTATION: BioDynaMo is an open-source project under the Apache 2.0 license and is available at www.biodynamo.org. Instructions to reproduce the results are available in the supplementary information. SUPPLEMENTARY INFORMATION: Available at https://doi.org/10.5281/zenodo.5121618.

Combined anti-angiogenic and cytotoxic treatment of a solid tumour: in silico investigation of a xenograft animal model's digital twin.

Anti-angiogenic (AA) treatments have received significant research interest due to the key role of angiogenesis in cancer progression. AA agents can have a strong effect on cancer regression, by blocking new vessels and reducing the density of the existing vasculature. Moreover, in a process termed vascular normalisation, AA drugs can improve the abnormal structure and function of the tumour vasculature, enhancing the delivery of chemotherapeutics to the tumour site. Despite their promising potential, an improved understanding of AA treatments is necessary to optimise their administration as a monotherapy or in combination with other cancer treatments. In this work we present an in silico multiscale cancer model which is used to systematically interrogate the role of individual mechanisms of action of AA drugs in tumour regression. Focus is placed on the reduction of vascular density and on vascular normalisation through a parametric study, which are considered either as monotherapies or in combination with conventional/ metronomic chemotherapy. The model is specified to data from a mammary carcinoma xenograft in immunodeficient mice, to enhance the physiological relevance of model predictions. Our results suggest that conventional chemotherapy might be more beneficial when combined with AA treatments, hindering tumour growth without causing excessive damage on healthy tissue. Notably, metronomic chemotherapy has shown significant potential in stopping tumour growth with minimal toxicity, even as a monotherapy. Our findings underpin the potential of our in silico framework for non-invasive and cost-effective evaluation of treatment strategies, which can enhance our understanding of combined therapeutic strategies and contribute towards improving cancer treatment management.

Multimodal Structural Analysis of the Human Aorta: From Valve to Bifurcation.

OBJECTIVE: The aims of the present study were to assess the relative proportion of collagen and elastin in the arterial wall and to evaluate the collagen microstructure from the aortic root to the external iliac artery. METHODS: Arterial wall tissue samples sampled during post-mortem examination from 16 sites in 14 individuals without aneurysm disease were fixed and stained for collagen and elastin. Stained sections were imaged and analysed to calculate collagen and elastin content as a percentage of overall tissue area. Scanning electron microscopy was used to quantify the collagen microstructure at six specific arterial regions. RESULTS: From the aortic root to the level of the suprarenal aorta, the percentages (area fractions) of collagen (ascending, descending, and suprarenal aorta respectively with 95% confidence interval [CI] 37.5%, 31.7 - 43.2; 38.9%, 33.1 - 44.7; 44.8%, 37.4 - 52.1) and elastin (43.0%, 37.3 - 48.8; 40.3%, 34.8 - 46.1; 32.4%, 25.2 - 39.6) in the aortic wall were similar. From the suprarenal aorta to the internal iliac arteries, the percentage of collagen increased (abdominal aorta, common and internal iliac arteries and external iliac artery respectively with 95% CI 50.6%, 42.7 - 58.7; 51.2%, 45.5 - 56.9; 49.2%, 42.0 - 56.4) reaching a double percentage for elastin (23.6%, 15.7 - 31.6; 20.8%, 15.1 - 26.5; 22.2%, 14.9 - 29.5). Mean collagen fibre diameter (MFD) and average segment length (ASL) were significantly larger in the external iliac artery (MFD 6.03, 95% CI 5.95 - 6.11; ASL 22.21, 95% CI 20.80 - 23.61) than in the ascending aorta (MFD 5.81, 5.72 - 5.89; ASL 19.47, 18.07 - 20.88) and the abdominal aorta (MFD 5.92, 5.84 - 6.00; ASL 21.10, 19.69 - 22.50). CONCLUSION: In subjects lacking aneurysmal disease, the aorta and iliac arteries are not structurally uniform along their length. There is an increase in collagen percentage and decrease in elastin percentage progressing distally along the aorta. Mean collagen fibre diameter and average segment length are larger in the external iliac artery, compared with the ascending and the abdominal aorta.

From tumour perfusion to drug delivery and clinical translation of in silico cancer models.

In silico cancer models have demonstrated great potential as a tool to improve drug design, optimise the delivery of drugs to target sites in the host tissue and, hence, improve therapeutic efficacy and patient outcome. However, there are significant barriers to the successful translation of in silico technology from bench to bedside. More precisely, the specification of unknown model parameters, the necessity for models to adequately reflect in vivo conditions, and the limited amount of pertinent validation data to evaluate models' accuracy and assess their reliability, pose major obstacles in the path towards their clinical translation. This review aims to capture the state-of-the-art in in silico cancer modelling of vascularised solid tumour growth, and identify the important advances and barriers to success of these models in clinical oncology. Particular emphasis has been put on continuum-based models of cancer since they - amongst the class of mechanistic spatio-temporal modelling approaches - are well-established in simulating transport phenomena and the biomechanics of tissues, and have demonstrated potential for clinical translation. Three important avenues in in silico modelling are considered in this contribution: first, since systemic therapy is a major cancer treatment approach, we start with an overview of the tumour perfusion and angiogenesis in silico models. Next, we present the state-of-the-art in silico work encompassing the delivery of chemotherapeutic agents to cancer nanomedicines through the bloodstream, and then review continuum-based modelling approaches that demonstrate great promise for successful clinical translation. We conclude with a discussion of what we view to be the key challenges and opportunities for in silico modelling in personalised and precision medicine.

An in silico hybrid continuum-/agent-based procedure to modelling cancer development: Interrogating the interplay amongst glioma invasion, vascularity and necrosis.

This paper develops a three-dimensional in silico hybrid model of cancer, which describes the multi-variate phenotypic behaviour of tumour and host cells. The model encompasses the role of cell migration and adhesion, the influence of the extracellular matrix, the effects of oxygen and nutrient availability, and the signalling triggered by chemical cues and growth factors. The proposed in silico hybrid modelling framework combines successfully the advantages of continuum-based and discrete methods, namely the finite element and agent-based method respectively. The framework is thus used to realistically model cancer mechano-biology in a multiscale fashion while maintaining the resolution power of each method in a computationally cost-effective manner. The model is tailored to simulate glioma progression, and is subsequently used to interrogate the balance between the host cells and small sized gliomas, while the go-or-grow phenotype characteristic in glioblastomas is also investigated. Also, cell-cell and cell-matrix interactions are examined with respect to their effect in (macroscopic) tumour growth, brain tissue perfusion and tumour necrosis. Finally, we use the in silico framework to assess differences between low-grade and high-grade glioma growth, demonstrating significant differences in the distribution of cancer as well as host cells, in accordance with reported experimental findings.

Should the Proximal Part of a Bifurcated Aortic Graft be Kept as Short as Possible? A Computational Study Elucidates on Aortic Graft Hemodynamics for Various Main Body Lengths.

BACKGROUND: It is accepted that surgically placed bifurcated aortic grafts should be shaped as a short proximal main tube with two long distal limbs. We aim to investigate the hemodynamic effect of different main body lengths in bifurcated aortic grafts using 3D computer models. METHODS: Five different idealized models are generated to represent an aorto-bifemoral graft. Distance from renal to femoral arteries is set at 25cm and distance between the femoral arteries is set at 14cm. Values of the main body length taken into account to build the idealized models are 3cm, 6cm, 9cm, 12cm and 15cm. Blood flow resistance, Time Average Wall Shear Stress (TAWSS), Oscillatory Shear Index (OSI) and Relative Residence Time (RRT) are estimated using the constructed 3D models. RESULTS: The total resistance decreased monotonically by as far as 40% as the main body length increased. Appropriate hemodynamic simulations show a maximum TAWSS decrease and a corresponding maximum OSI and RRT increase with elongated main body configurations, indicating a hemodynamic benefit of the "Short" main body configuration. Nevertheless, the differences in these later variables are small, affecting a limited portion of the geometries. CONCLUSION: A long main body of a bifurcated aortic graft results in significantly reduced total resistance in idealized models designed to represent an aorto-bifemoral surgical graft, while the differences observed in TAWSS, OSI and RRT between models are small.

Correction: In-silico dynamic analysis of cytotoxic drug administration to solid tumours: Effect of binding affinity and vessel permeability.

[This corrects the article DOI: 10.1371/journal.pcbi.1006460.].

In-silico dynamic analysis of cytotoxic drug administration to solid tumours: Effect of binding affinity and vessel permeability.

The delivery of blood-borne therapeutic agents to solid tumours depends on a broad range of biophysical factors. We present a novel multiscale, multiphysics, in-silico modelling framework that encompasses dynamic tumour growth, angiogenesis and drug delivery, and use this model to simulate the intravenous delivery of cytotoxic drugs. The model accounts for chemo-, hapto- and mechanotactic vessel sprouting, extracellular matrix remodelling, mechano-sensitive vascular remodelling and collapse, intra- and extravascular drug transport, and tumour regression as an effect of a cytotoxic cancer drug. The modelling framework is flexible, allowing the drug properties to be specified, which provides realistic predictions of in-vivo vascular development and structure at different tumour stages. The model also enables the effects of neoadjuvant vascular normalisation to be implicitly tested by decreasing vessel wall pore size. We use the model to test the interplay between time of treatment, drug affinity rate and the size of the vessels' endothelium pores on the delivery and subsequent tumour regression and vessel remodelling. Model predictions confirm that small-molecule drug delivery is dominated by diffusive transport and further predict that the time of treatment is important for low affinity but not high affinity cytotoxic drugs, the size of the vessel wall pores plays an important role in the effect of low affinity but not high affinity drugs, that high affinity cytotoxic drugs remodel the tumour vasculature providing a large window for the normalisation of the vascular architecture, and that the combination of large pores and high affinity enhances cytotoxic drug delivery efficiency. These results have implications for treatment planning and methods to enhance drug delivery, and highlight the importance of in-silico modelling in investigating the optimisation of cancer therapy on a personalised setting.

A Validated Multiscale In-Silico Model for Mechano-sensitive Tumour Angiogenesis and Growth.

Vascularisation is a key feature of cancer growth, invasion and metastasis. To better understand the governing biophysical processes and their relative importance, it is instructive to develop physiologically representative mathematical models with which to compare to experimental data. Previous studies have successfully applied this approach to test the effect of various biochemical factors on tumour growth and angiogenesis. However, these models do not account for the experimentally observed dependency of angiogenic network evolution on growth-induced solid stresses. This work introduces two novel features: the effects of hapto- and mechanotaxis on vessel sprouting, and mechano-sensitive dynamic vascular remodelling. The proposed three-dimensional, multiscale, in-silico model of dynamically coupled angiogenic tumour growth is specified to in-vivo and in-vitro data, chosen, where possible, to provide a physiologically consistent description. The model is then validated against in-vivo data from murine mammary carcinomas, with particular focus placed on identifying the influence of mechanical factors. Crucially, we find that it is necessary to include hapto- and mechanotaxis to recapitulate observed time-varying spatial distributions of angiogenic vasculature.

Agent-based modeling in cancer biomedicine: applications and tools for calibration and validation.

Computational models are not just appealing because they can simulate and predict the development of biological phenomena across multiple spatial and temporal scales, but also because they can integrate information from well-established in vitro and in vivo models and test new hypotheses in cancer biomedicine. Agent-based models and simulations are especially interesting candidates among computational modeling procedures in cancer research due to the capability to, for instance, recapitulate the dynamics of neoplasia and tumor - host interactions. Yet, the absence of methods to validate the consistency of the results across scales can hinder adoption by turning fine-tuned models into black boxes. This review compiles relevant literature that explores strategies to leverage high-fidelity simulations of multi-scale, or multi-level, cancer models with a focus on verification approached as simulation calibration. We consolidate our review with an outline of modern approaches for agent-based models' validation and provide an ambitious outlook toward rigorous and reliable calibration.

The influence of internal pressure and neuromuscular agents on C. elegans biomechanics: an empirical and multi-compartmental in silico modelling study.

The function of a specific tissue and its biomechanics are interdependent, with pathologies or ageing often being intertwined with structural decline. The biomechanics of Caenorhabditis elegans, a model organism widely used in pharmacological and ageing research, has been established as biomarker for healthy ageing. However, the properties of the constituent tissues, and their contribution to the overall mechanical characteristics of the organism, remain relatively unknown. In this study we investigated the biomechanics of healthy C. elegans cuticle, muscle tissue, and pseudocoelom using a combination of indentation experiments and in silico modelling. We performed stiffness measurements using an atomic force microscope. To approximate the nematode's cylindrical body we used a novel three-compartment nonlinear finite element model, enabling us to analyse of how changes in the elasticity of individual compartments affect the bulk stiffness. We then fine-tuned the parameters of the model to match the simulation force-indentation output to the experimental data. To test the finite element model, we modified distinct compartments experimentally. Our in silico results, in agreement with previous studies, suggest that hyperosmotic shock reduces stiffness by decreasing the internal pressure. Unexpectedly, treatment with the neuromuscular agent aldicarb, traditionally associated with muscle contraction, reduced stiffness by decreasing the internal pressure. Furthermore, our finite element model can offer insights into how drugs, mutations, or processes such as ageing target individual tissues.

An adaptive semi-implicit finite element solver for brain cancer progression modeling.

Glioblastoma is the most aggressive and infiltrative glioma, classified as Grade IV, with the poorest survival rate among patients. Accurate and rigorously tested mechanistic in silico modeling offers great value to understand and quantify the progression of primary brain tumors. This paper presents a continuum-based finite element framework that is built on high performance computing, open-source libraries to simulate glioblastoma progression. We adopt the established proliferation invasion hypoxia necrosis angiogenesis model in our framework to realize scalable simulations of cancer, and has demonstrated to produce accurate and efficient solutions in both two- and three-dimensional brain models. The in silico solver can successfully implement arbitrary order discretization schemes and adaptive remeshing algorithms. A model sensitivity analysis is conducted to test the impact of vascular density, cancer cell invasiveness and aggressiveness, the phenotypic transition potential, including that of necrosis, and the effect of tumor-induced angiogenesis in the evolution of glioblastoma. Additionally, individualized simulations of brain cancer progression are carried out using pertinent magnetic resonance imaging data, where the in silico model is used to investigate the complex dynamics of the disease. We conclude by arguing how the proposed framework can deliver patient-specific simulations of cancer prognosis and how it could bridge clinical imaging with modeling.

Convection-Enhanced Delivery In Silico Study for Brain Cancer Treatment.

Brain cancer therapy remains a formidable challenge in oncology. Convection-enhanced delivery (CED) is an innovative and promising local drug delivery method for the treatment of brain cancer, overcoming the challenges of the systemic delivery of drugs to the brain. To improve our understanding about CED efficacy and drug transport, we present an in silico methodology for brain cancer CED treatment simulation. To achieve this, a three-dimensional finite element formulation is utilized which employs a brain model representation from clinical imaging data and is used to predict the drug deposition in CED regimes. The model encompasses biofluid dynamics and the transport of drugs in the brain parenchyma. Drug distribution is studied under various patho-physiological conditions of the tumor, in terms of tumor vessel wall pore size and tumor tissue hydraulic conductivity as well as for drugs of various sizes, spanning from small molecules to nanoparticles. Through a parametric study, our contribution reports the impact of the size of the vascular wall pores and that of the therapeutic agent on drug distribution during and after CED. The in silico findings provide useful insights of the spatio-temporal distribution and average drug concentration in the tumor towards an effective treatment of brain cancer.

Interrogating and Quantifying In Vitro Cancer Drug Pharmacodynamics via Agent-Based and Bayesian Monte Carlo Modelling.

The effectiveness of chemotherapy in cancer cell regression is often limited by drug resistance, toxicity, and neoplasia heterogeneity. However, due to the significant complexities entailed by the many cancer growth processes, predicting the impact of interference and symmetry-breaking mechanisms is a difficult problem. To quantify and understand more about cancer drug pharmacodynamics, we combine in vitro with in silico cancer models. The anti-proliferative action of selected cytostatics is interrogated on human colorectal and breast adenocarcinoma cells, while an agent-based computational model is employed to reproduce experiments and shed light on the main therapeutic mechanisms of each chemotherapeutic agent. Multiple drug administration scenarios on each cancer cell line are simulated by varying the drug concentration, while a Bayesian-based method for model parameter optimisation is employed. Our proposed procedure of combining in vitro cancer drug screening with an in silico agent-based model successfully reproduces the impact of chemotherapeutic drugs in cancer growth behaviour, while the mechanisms of action of each drug are characterised through model-derived probabilities of cell apoptosis and division. We suggest that our approach could form the basis for the prospective generation of experimentally-derived and model-optimised pharmacological variables towards personalised cancer therapy.

Investigating the reference domain influence in personalised models of cardiac mechanics : Effect of unloaded geometry on cardiac biomechanics.

A major concern in personalised models of heart mechanics is the unknown zero-pressure domain, a prerequisite for accurately predicting cardiac biomechanics. As the reference configuration cannot be captured by clinical data, studies often employ in-vivo frames which are unlikely to correspond to unloaded geometries. Alternatively, zero-pressure domain is approximated through inverse methodologies, which, however, entail assumptions pertaining to boundary conditions and material parameters. Both approaches are likely to introduce biases in estimated biomechanical properties; nevertheless, quantification of these effects is unattainable without ground-truth data. In this work, we assess the unloaded state influence on model-derived biomechanics, by employing an in-silico modelling framework relying on experimental data on porcine hearts. In-vivo images are used for model personalisation, while in-situ experiments provide a reliable approximation of the reference domain, creating a unique opportunity for a validation study. Personalised whole-cycle cardiac models are developed which employ different reference domains (image-derived, inversely estimated) and are compared against ground-truth model outcomes. Simulations are conducted with varying boundary conditions, to investigate the effect of data-derived constraints on model accuracy. Attention is given to modelling the influence of the ribcage on the epicardium, due to its close proximity to the heart in the porcine anatomy. Our results find merit in both approaches for dealing with the unknown reference domain, but also demonstrate differences in estimated biomechanical quantities such as material parameters, strains and stresses. Notably, they highlight the importance of a boundary condition accounting for the constraining influence of the ribcage, in forward and inverse biomechanical models.

Breast cancer surgery with augmented reality.

INTRODUCTION: Innovations in 3D spatial technology and augmented reality imaging driven by digital high-tech industrial science have accelerated experimental advances in breast cancer imaging and the development of medical procedures aimed to reduce invasiveness. PRESENTATION OF CASE: A 57-year-old post-menopausal woman presented with screen-detected left-sided breast cancer. After undergoing all staging and pre-operative studies the patient was proposed for conservative breast surgery with tumor localization. During surgery, an experimental digital and non-invasive intra-operative localization method with augmented reality was compared with the standard pre-operative localization with carbon tattooing (institutional protocol). The breast surgeon wearing an augmented reality headset (Hololens) was able to visualize the tumor location projection inside the patient's left breast in the usual supine position. DISCUSSION: This work describes, to our knowledge, the first experimental test with a digital non-invasive method for intra-operative breast cancer localization using augmented reality to guide breast conservative surgery. In this case, a successful overlap of the previous standard pre-operative marks with carbon tattooing and tumor visualization inside the patient's breast with augmented reality was obtained. CONCLUSION: Breast cancer conservative guided surgery with augmented reality can pave the way for a digital non-invasive method for intra-operative tumor localization.

Decompressive craniectomy of post-traumatic brain injury: an in silico modelling approach for intracranial hypertension management.

Traumatic brain injury (TBI) causes brain edema that induces increased intracranial pressure and decreased cerebral perfusion. Decompressive craniectomy has been recommended as a surgical procedure for the management of swollen brain and intracranial hypertension. Proper location and size of a decompressive craniectomy, however, remain controversial and no clinical guidelines are available. Mathematical and computational (in silico) models can predict the optimum geometric conditions and provide insights for the brain mechanical response following a decompressive craniectomy. In this work, we present a finite element model of post-traumatic brain injury and decompressive craniectomy that incorporates a biphasic, nonlinear biomechanical model of the brain. A homogenous pressure is applied in the brain to represent the intracranial pressure loading caused by the tissue swelling and the models calculate the deformations and stresses in the brain as well as the herniated volume of the brain tissue that exits the skull following craniectomy. Simulations for different craniectomy geometries (unilateral, bifrontal and bifrontal with midline bar) and sizes are employed to identify optimal clinical conditions of decompressive craniectomy. The reported results for the herniated volume of the brain tissue as a function of the intracranial pressure loading under a specific geometry and size of craniectomy are exceptionally relevant for decompressive craniectomy planning.

Biomechanical modelling of spinal tumour anisotropic growth.

Biomechanical abnormalities of solid tumours involve stiffening of the tissue and accumulation of mechanical stresses. Both abnormalities affect cancer cell proliferation and invasiveness and thus, play a crucial role in tumour morphology and metastasis. Even though, it has been known for more than two decades that high mechanical stresses reduce cancer cell proliferation rates driving growth towards low-stress regions, most biomechanical models of tumour growth account for isotropic growth. This cannot be valid, however, in tumours that grow within multiple host tissues of different mechanical properties, such as the spine. In these cases, structural heterogeneity would result in anisotropic growth of tumours. To this end, we present a biomechanical, biphasic model for anisotropic growth of spinal tumours. The model that accounts for both the fluid and the solid phase of the tumour was used to predict the evolution of solid stress and interstitial fluid pressure in intramedullary spinal tumours and highlight the differences between isotropic and anisotropic growth. Varying the degree of anisotropy, we found considerable differences in the shape of the tumours, leading to tumours of more realistic ellipsoidal shapes.

Engineered magnetoactive collagen hydrogels with tunable and predictable mechanical response.

In the present study, the synthesis of superparamagnetic collagen-based nanocomposite hydrogels with tunable swelling, mechanical and magnetic properties is reported. The fabrication strategy involved the preparation of pristine collagen type-I hydrogels followed by their immersion in highly stable aqueous solutions containing pre-formed double-layer oleic acid-coated hydrophilic magnetite nanoparticles (OA.OA.Fe3O4) at different concentrations, to interrogate nanoparticles' deposition within the 3D fibrous collagen matrix. Besides the investigation of the morphology, composition and magnetic properties of the produced materials, their mechanical properties were experimentally evaluated under confined compressive loading conditions while an exponential constitutive equation was employed to describe their mechanical response. Moreover, the deposition of the nanoparticles in the collagenous matrix was modeled mathematically with respect to the swelling of the gel and the effective stiffness of the matrix. The model recapitulated nanoparticle diffusion and deposition as well as hydrogel swelling, in terms of nanoparticles' size and concentration of OA.OA.Fe3O4 aqueous solution.

A quantitative in silico platform for simulating cytotoxic and nanoparticle drug delivery to solid tumours.

The role of tumour-host mechano-biology and the mechanisms involved in the delivery of anti-cancer drugs have been extensively studied using in vitro and in vivo models. A complementary approach is offered by in silico models, which can also potentially identify the main factors affecting the transport of tumour-targeting molecules. Here, we present a generalized three-dimensional in silico modelling framework of dynamic solid tumour growth, angiogenesis and drug delivery. Crucially, the model allows for drug properties-such as size and binding affinity-to be explicitly defined, hence facilitating investigation into the interaction between the changing tumour-host microenvironment and cytotoxic and nanoparticle drugs. We use the model to qualitatively recapitulate experimental evidence of delivery efficacy of cytotoxic and nanoparticle drugs on matrix density (and hence porosity). Furthermore, we predict a highly heterogeneous distribution of nanoparticles after delivery; that nanoparticles require a high porosity extracellular matrix to cause tumour regression; and that post-injection transvascular fluid velocity depends on matrix porosity, and implicitly on the size of the drug used to treat the tumour. These results highlight the utility of predictive in silico modelling in better understanding the factors governing efficient cytotoxic and nanoparticle drug delivery.