An efficient, localised approach for the simulation of elastic blood vessels using the lattice Boltzmann method.

Many numerical studies of blood flow impose a rigid wall assumption due to the simplicity of its implementation compared to a full coupling with a solid mechanics model. In this paper, we present a localised method for incorporating the effects of elastic walls into blood flow simulations using the lattice Boltzmann method implemented by the open-source code HemeLB. We demonstrate that our approach is able to more accurately capture the flow behaviour expected in elastic walled vessels than ones with rigid walls. Furthermore, we show that this can be achieved with no loss of computational performance and remains strongly scalable on high performance computers. We finally illustrate that our approach captures the same trends in wall shear stress distribution as those observed in studies using a rigorous coupling between fluid dynamics and solid mechanics models to solve flow in personalised vascular geometries. These results demonstrate that our model can be used to efficiently and effectively represent flows in elastic blood vessels.

High fidelity blood flow in a patient-specific arteriovenous fistula.

An arteriovenous fistula, created by artificially connecting segments of a patient's vasculature, is the preferred way to gain access to the bloodstream for kidney dialysis. The increasing power and availability of supercomputing infrastructure means that it is becoming more realistic to use simulations to help identify the best type and location of a fistula for a specific patient. We describe a 3D fistula model that uses the lattice Boltzmann method to simultaneously resolve blood flow in patient-specific arteries and veins. The simulations conducted here, comprising vasculatures of the whole forearm, demonstrate qualified validation against clinical data. Ongoing research to further encompass complex biophysics on realistic time scales will permit the use of human-scale physiological models for basic and clinical medicine.

VECMAtk: a scalable verification, validation and uncertainty quantification toolkit for scientific simulations.

We present the VECMA toolkit (VECMAtk), a flexible software environment for single and multiscale simulations that introduces directly applicable and reusable procedures for verification, validation (V&V), sensitivity analysis (SA) and uncertainty quantication (UQ). It enables users to verify key aspects of their applications, systematically compare and validate the simulation outputs against observational or benchmark data, and run simulations conveniently on any platform from the desktop to current multi-petascale computers. In this sequel to our paper on VECMAtk which we presented last year [1] we focus on a range of functional and performance improvements that we have introduced, cover newly introduced components, and applications examples from seven different domains such as conflict modelling and environmental sciences. We also present several implemented patterns for UQ/SA and V&V, and guide the reader through one example concerning COVID-19 modelling in detail. This article is part of the theme issue 'Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantification in silico'.

Towards blood flow in the virtual human: efficient self-coupling of HemeLB.

Many scientific and medical researchers are working towards the creation of a virtual human-a personalized digital copy of an individual-that will assist in a patient's diagnosis, treatment and recovery. The complex nature of living systems means that the development of this remains a major challenge. We describe progress in enabling the HemeLB lattice Boltzmann code to simulate 3D macroscopic blood flow on a full human scale. Significant developments in memory management and load balancing allow near linear scaling performance of the code on hundreds of thousands of computer cores. Integral to the construction of a virtual human, we also outline the implementation of a self-coupling strategy for HemeLB. This allows simultaneous simulation of arterial and venous vascular trees based on human-specific geometries.

The influence of base pair tautomerism on single point mutations in aqueous DNA.

The relationship between base pair hydrogen bond proton transfer and the rate of spontaneous single point mutations at ambient temperatures and pressures in aqueous DNA is investigated. By using an ensemble-based multiscale computational modelling method, statistically robust rates of proton transfer for the A:T and G:C base pairs within a solvated DNA dodecamer are calculated. Several different proton transfer pathways are observed within the same base pair. It is shown that, in G:C, the double proton transfer tautomer is preferred, while the single proton transfer process is favoured in A:T. The reported range of rate coefficients for double proton transfer is consistent with recent experimental data. Notwithstanding the approximately 1000 times more common presence of single proton transfer products from A:T, observationally there is bias towards G:C to A:T mutations in a wide range of living organisms. We infer that the double proton transfer reactions between G:C base pairs have a negligible contribution towards this bias for the following reasons: (i) the maximum half-life of the G*:C* tautomer is in the range of picoseconds, which is significantly smaller than the milliseconds it takes for DNA to unwind during replication, (ii) statistically, the majority of G*:C* tautomers revert back to their canonical forms through a barrierless process, and (iii) the thermodynamic instability of the tautomers with respect to the canonical base pairs. Through similar reasoning, we also deduce that proton transfer in the A:T base pair does not contribute to single point mutations in DNA.

Large-scale binding affinity calculations on commodity compute clouds.

In recent years, it has become possible to calculate binding affinities of compounds bound to proteins via rapid, accurate, precise and reproducible free energy calculations. This is imperative in drug discovery as well as personalized medicine. This approach is based on molecular dynamics (MD) simulations and draws on sequence and structural information of the protein and compound concerned. Free energies are determined by ensemble averages of many MD replicas, each of which requires hundreds of cores and/or GPU accelerators, which are now available on commodity cloud computing platforms; there are also requirements for initial model building and subsequent data analysis stages. To automate the process, we have developed a workflow known as the binding affinity calculator. In this paper, we focus on the software infrastructure and interfaces that we have developed to automate the overall workflow and execute it on commodity cloud platforms, in order to reliably predict their binding affinities on time scales relevant to the domains of application, and illustrate its application to two free energy methods.

The heterogeneous multiscale method applied to inelastic polymer mechanics.

Mechanisms emerging across multiple scales are ubiquitous in physics and methods designed to investigate them are becoming essential. The heterogeneous multiscale method (HMM) is one of these, concurrently simulating the different scales while keeping them separate. Owing to the significant computational expense, developments of HMM remain mostly theoretical and applications to physical problems are scarce. However, HMM is highly scalable and is well suited for high performance computing. With the wide availability of multi-petaflop infrastructures, HMM applications are becoming practical. Rare applications to mechanics of materials at low loading amplitudes exist, but are generally confined to the elastic regime. Beyond that, where history-dependent, irreversible or nonlinear mechanisms occur, not only computational cost but also data management issues arise. The micro-scale description loses generality, developing a specific microstructure based on the deformation history, which implies inter alia that as many microscopic models as discrete locations in the macroscopic description must be simulated and stored. Here, we present a detailed description of the application of HMM to inelastic mechanics of materials, with emphasis on the efficiency and accuracy of the scale-bridging methodology. The method is well suited to the estimation of macroscopic properties of polymers (and derived nanocomposites) starting from knowledge of their atomistic chemical structure. Through application of the resulting workflow to polymer fracture mechanics, we demonstrate deviation in the predicted fracture toughness relative to a single-scale molecular dynamics approach, thus illustrating the need for such concurrent multiscale methods in the predictive estimation of macroscopic properties. This article is part of the theme issue 'Multiscale modelling, simulation and computing: from the desktop to the exascale'.

Performance of distributed multiscale simulations.

Multiscale simulations model phenomena across natural scales using monolithic or component-based code, running on local or distributed resources. In this work, we investigate the performance of distributed multiscale computing of component-based models, guided by six multiscale applications with different characteristics and from several disciplines. Three modes of distributed multiscale computing are identified: supplementing local dependencies with large-scale resources, load distribution over multiple resources, and load balancing of small- and large-scale resources. We find that the first mode has the apparent benefit of increasing simulation speed, and the second mode can increase simulation speed if local resources are limited. Depending on resource reservation and model coupling topology, the third mode may result in a reduction of resource consumption.

p-medicine: A Medical Informatics Platform for Integrated Large Scale Heterogeneous Patient Data.

Secure access to patient data is becoming of increasing importance, as medical informatics grows in significance, to both assist with population health studies, and patient specific medicine in support of treatment. However, assembling the many different types of data emanating from the clinic is in itself a difficulty, and doing so across national borders compounds the problem. In this paper we present our solution: an easy to use distributed informatics platform embedding a state of the art data warehouse incorporating a secure pseudonymisation system protecting access to personal healthcare data. Using this system, a whole range of patient derived data, from genomics to imaging to clinical records, can be assembled and linked, and then connected with analytics tools that help us to understand the data. Research performed in this environment will have immediate clinical impact for personalised patient healthcare.

Flexible composition and execution of high performance, high fidelity multiscale biomedical simulations.

Multiscale simulations are essential in the biomedical domain to accurately model human physiology. We present a modular approach for designing, constructing and executing multiscale simulations on a wide range of resources, from laptops to petascale supercomputers, including combinations of these. Our work features two multiscale applications, in-stent restenosis and cerebrovascular bloodflow, which combine multiple existing single-scale applications to create a multiscale simulation. These applications can be efficiently coupled, deployed and executed on computers up to the largest (peta) scale, incurring a coupling overhead of 1-10% of the total execution time.

Confined cubic blue phases under shear.

We study the behaviour of confined cubic blue phases under shear flow via lattice Boltzmann simulations. We focus on the two experimentally observed phases, blue phase I and blue phase II. The disclination network of blue phase II continuously breaks and reforms under shear, leading to an oscillatory stress response in time. The oscillations are only regular for very thin samples. For thicker samples, the shear leads to a 'stick-slip' motion of part of the network along the vorticity direction. Blue phase I responds very differently: its defect network undergoes seemingly chaotic rearrangements under shear, irrespective of system size.

Clinically driven design of multi-scale cancer models: the ContraCancrum project paradigm.

The challenge of modelling cancer presents a major opportunity to improve our ability to reduce mortality from malignant neoplasms, improve treatments and meet the demands associated with the individualization of care needs. This is the central motivation behind the ContraCancrum project. By developing integrated multi-scale cancer models, ContraCancrum is expected to contribute to the advancement of in silico oncology through the optimization of cancer treatment in the patient-individualized context by simulating the response to various therapeutic regimens. The aim of the present paper is to describe a novel paradigm for designing clinically driven multi-scale cancer modelling by bringing together basic science and information technology modules. In addition, the integration of the multi-scale tumour modelling components has led to novel concepts of personalized clinical decision support in the context of predictive oncology, as is also discussed in the paper. Since clinical adaptation is an inelastic prerequisite, a long-term clinical adaptation procedure of the models has been initiated for two tumour types, namely non-small cell lung cancer and glioblastoma multiforme; its current status is briefly summarized.

The EuroPhysiome, STEP and a roadmap for the virtual physiological human.

Biomedical science and its allied disciplines are entering a new era in which computational methods and technologies are poised to play a prevalent role in supporting collaborative investigation of the human body. Within Europe, this has its focus in the virtual physiological human (VPH), which is an evolving entity that has emerged from the EuroPhysiome initiative and the strategy for the EuroPhysiome (STEP) consortium. The VPH is intended to be a solution to common infrastructure needs for physiome projects across the globe, providing a unifying architecture that facilitates integration and prediction, ultimately creating a framework capable of describing Homo sapiens in silico. The routine reliance of the biomedical industry, biomedical research and clinical practice on information technology (IT) highlights the importance of a tailor-made and robust IT infrastructure, but numerous challenges need to be addressed if the VPH is to become a mature technological reality. Appropriate investment will reap considerable rewards, since it is anticipated that the VPH will influence all sectors of society, with implications predominantly for improved healthcare, improved competitiveness in industry and greater understanding of (patho)physiological processes. This paper considers issues pertinent to the development of the VPH, highlighted by the work of the STEP consortium.

Energetics of K+ permeability through Gramicidin A by forward-reverse steered molecular dynamics.

The estimation of ion channel permeability poses a considerable challenge for computer simulations because of the significant free energy barriers involved, but also offers valuable molecular information on the ion permeation process not directly available from experiments. In this article we determine the equilibrium free energy barrier for potassium ion permeability in Gramicidin A in an efficient way by atomistic forward-reverse non-equilibrium steered molecular dynamics simulations, opening the way for its use in more complex biochemical systems. Our results indicate that the tent-shaped energetics of translocation of K+ ions in Gramicidin A is dictated by the different polarization responses to the ion of the external bulk water and the less polar environment of the membrane.

Functional ceramic materials database: an online resource for materials research.

We present work on the creation of a ceramic materials database which contains data gleaned from literature data sets as well as new data obtained from combinatorial experiments on the London University Search Instrument. At the time of this writing, the database contains data related to two main groups of materials, mainly in the perovskite family. Permittivity measurements of electroceramic materials are the first area of interest, while ion diffusion measurements of oxygen ion conductors are the second. The nature of the database design does not restrict the type of measurements which can be stored; as the available data increase, the database may become a generic, publicly available ceramic materials resource.

Self-assembly of ternary cubic, hexagonal, and lamellar mesophases using the lattice-Boltzmann kinetic method.

We use a kinetic lattice-Boltzmann method to simulate the self-assembly of the cubic primitive (P), diamond (D), and gyroid (G) mesophases from an initial quench composed of oil, water, and amphiphilic particles. Here, we also report the self-assembly of the noncubic hexagonal phase and two lamellar phases, one with periodic convolutions. The periodic mesophase structures are emergent from the underlying conservation laws and quasi-molecular interactions of the lattice-Boltzmann model. We locate regions of the model's parameter space where the sequence of appearance of mesophases lamellar --> primitive --> hexagonal is in agreement with pressure jump experiments and the sequence cubic --> lamellar is in agreement with compositional variations reported in the literature. The ability of our lattice-Boltzmann model to simulate self-assembly of cubic and noncubic phases in a unified and consistent manner opens the way for further investigations into the transition pathways and kinetics of the phase transitions between these states as well as of the rheology of these phases.

Design of electroceramic materials using artificial neural networks and multiobjective evolutionary algorithms.

We describe the computational design of electroceramic materials with optimal permittivity for application as electronic components. Given the difficulty of large-scale manufacture and characterization of these materials, including the theoretical prediction of their materials properties by conventional means, our approach is based on a recently established database containing composition and property information for a wide range of ceramic compounds. The electroceramic materials composition-function relationship is encapsulated by an artificial neural network which is used as one of the objectives in a multiobjective evolutionary algorithm. Evolutionary algorithms are stochastic optimization techniques which we employ to search for optimal materials based on chemical composition. The other objectives optimized include the reliability of the neural network prediction and the overall electrostatic charge of the material. The evolutionary algorithm searches for materials which simultaneously have high relative permittivity, minimum overall charge, and good prediction reliability. We find that we are able to predict a range of new electroceramic materials with varying degrees of reliability. In some cases the materials are similar to those contained in the database; in others, completely new materials are predicted.

Structural transitions and arrest of domain growth in sheared binary immiscible fluids and microemulsions.

We investigate spinodal decomposition and structuring effects in binary immiscible and ternary amphiphilic fluid mixtures under shear by means of three-dimensional lattice Boltzmann simulations. We show that the growth of individual fluid domains can be arrested by adding surfactant to the system, thus forming a bicontinuous microemulsion. We demonstrate that the maximum domain size and the time of arrest depend linearly on the concentration of amphiphile molecules. In addition, we find that for a well-defined threshold value of amphiphile concentration, the maximum domain size and time of complete arrest do not change. For systems under constant and oscillatory shear we analyze domain growth rates in directions parallel and perpendicular to the applied shear. We find a structural transition from a sponge to a lamellar phase by applying a constant shear and the occurrence of tubular structures under oscillatory shear. The size of the resulting lamellae and tubes depends strongly on the amphiphile concentration, shear rate, and shear frequency.

Fluctuating hydrodynamic modeling of fluids at the nanoscale.

A good representation of mesoscopic fluids is required to combine with molecular simulations at larger length and time scales [De Fabritiis, Phys. Rev. Lett. 97, 134501 (2006)]. However, accurate computational models of the hydrodynamics of nanoscale molecular assemblies are lacking, at least in part because of the stochastic character of the underlying fluctuating hydrodynamic equations. Here we derive a finite volume discretization of the compressible isothermal fluctuating hydrodynamic equations over a regular grid in the Eulerian reference system. We apply it to fluids such as argon at arbitrary densities and water under ambient conditions. To that end, molecular dynamics simulations are used to derive the required fluid properties. The equilibrium state of the model is shown to be thermodynamically consistent and correctly reproduces linear hydrodynamics including relaxation of sound and shear modes. We also consider nonequilibrium states involving diffusion and convection in cavities with no-slip boundary conditions.