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Despite the existence of a powerful theoretical foundation for the development of multiscale models of infectious disease dynamics in the form of the replication-transmission relativity theory, the majority of current modelling studies focus more on single-scale modelling. The explicit aim of this study is to change the current predominantly single-scale modelling landscape in the design of planning frameworks for the control, elimination and even eradication of infectious disease systems through the exploitation of multiscale modelling methods based on the application of the replication-transmission relativity theory. We first present a structured roadmap for the development of multiscale models of infectious disease systems. The roadmap is tested on hookworm infection. The testing of the feasibility of the roadmap established a fundamental result which can be generalized to confirm that the complexity of an infectious disease system is encapsulated with a level of organization spanning a microscale and a macroscale.

Drug development is one of the most significant processes in the pharmaceutical industry. Various computational methods have dramatically reduced the time and cost of drug discovery. In this review, we firstly discussed roles of multiscale biomolecular simulations in identifying drug binding sites on the target macromolecule and elucidating drug action mechanisms. Then, virtual screening methods (e.g., molecular docking, pharmacophore modeling, and QSAR) as well as structure- and ligand-based classical/de novo drug design were introduced and discussed. Last, we explored the development of machine learning methods and their applications in aforementioned computational methods to speed up the drug discovery process. Also, several application examples of combining various methods was discussed. A combination of different methods to jointly solve the tough problem at different scales and dimensions will be an inevitable trend in drug screening and design.

We prove the global existence, along with some basic boundedness properties, of weak solutions to a PDE-ODE system modeling the multiscale invasion of tumor cells through the surrounding tissue matrix. The model has been proposed in [G. Meral, C. Stinner, and C. Surulescu, On a Multiscale Model Involving Cell Contractivity and its Effects on Tumor Invasion, preprint, TU Kaiserslautern, Kaiserslautern, Germany, 2013] and accounts on the macroscopic level for the evolution of cell and tissue densities, along with the concentration of a chemoattractant, while on the subcellular level it involves the binding of integrins to soluble and insoluble components of the peritumoral region. The connection between the two scales is realized with the aid of a contractivity function characterizing the ability of the tumor cells to adapt their motility behavior to their subcellular dynamics. The resulting system, consisting of three partial and three ordinary differential equations including a temporal delay, in particular involves chemotactic and haptotactic cross diffusion. In order to overcome technical obstacles stemming from the corresponding highest-order interaction terms, we base our analysis on a certain functional, inter alia involving the cell and tissue densities in the diffusion and haptotaxis terms, respectively, which is shown to enjoy a quasi-dissipative property. This will be used as a starting point for the derivation of a series of integral estimates finally allowing for the construction of a generalized solution as the limit of solutions to suitably regularized problems. [PUBLICATION ABSTRACT]

The yeast Saccharomyces cerevisiae is widely used as a cell factory and as an important eukaryal model organism for studying cellular physiology related to human health and disease. Yeast was also the first eukaryal organism for which a genome-scale metabolic model (GEM) was developed. In recent years there has been interest in expanding the modeling framework for yeast by incorporating enzymatic parameters and other heterogeneous cellular networks to obtain a more comprehensive description of cellular physiology. We review the latest developments in multiscale models of yeast, and illustrate how a new generation of multiscale models could significantly enhance the predictive performance and expand the applications of classical GEMs in cell factory design and basic studies of yeast physiology. High-quality genome-scale metabolic models (GEMs) provide a solid basis for developing the next generation of computational metabolic models for yeast.Enhanced metabolic models have been reconstructed for yeast by combining enzymatic constraints and their derived parameters to improve prediction performance.Multiscale models of yeast connect heterogeneous molecular networks with GEMs to integrate complex regulation into the models.Multiscale models of yeast can serve as a basis for computational design of future yeast cell factories.

Models of electrical activation and recovery in cardiac cells and tissue have become valuable research tools, and are beginning to be used in safety-critical applications including guidance for clinical procedures and for drug safety assessment. As a consequence, there is an urgent need for a more detailed and quantitative understanding of the ways that uncertainty and variability influence model predictions. In this paper, we review the sources of uncertainty in these models at different spatial scales, discuss how uncertainties are communicated across scales, and begin to assess their relative importance. We conclude by highlighting important challenges that continue to face the cardiac modelling community, identifying open questions, and making recommendations for future studies. This article is part of the theme issue ‘Uncertainty quantification in cardiac and cardiovascular modelling and simulation’.

1. To manage anthropogenic environmental change for the benefit of biodiversity, we must improve our understanding of the complex relationships between organisms and their environment. We have developed multiscale habitat suitability models (HSMs) for bats, a mobile group of mammals, for a geographically varied region of the UK. We ask whether the models have sufficient accuracy to contribute to informed decision-making in habitat management and in minimizing the impact of climate change and human infrastructural development. 2. We used acoustic surveys supplemented by catching to gather presence data for eight species from 30 sites across the south of the Lake District National Park in NW England. Species were identified by manual and automated extraction and analysis of echolocation calls. Fine-resolution (50 and 100 m) habitat maps were generated at twelve spatial scales by calculating the variables across squares of increasing size, from 100 to 6000 m, around each focal 50 or 100 m square. Presence-only HSM software, MaxEnt, was used to determine the predictive power of each habitat variable at each scale. Multiscale models included data for each variable at the scale at which it had the strongest relationship with the presence of each species. 3. The best multiscale models were selected using fivefold cross-validation, with backwards, stepwise variable removal, whilst minimizing residual spatial autocorrelation and sampling bias. Further tests with independent field data indicated good model transferability across the entire National Park. 4. Foraging bats were generally most strongly associated with variables measured at small spatial scales and distance measures. However, each species responded differently across the range of scales, and strong associations were also found at the largest scale of analysis (6000 m). 5. Synthesis and applications. The best models for determining habitat suitability had few variables, making them easy to interpret and use in practical conservation planning. The approach is applicable to any taxa for which reliable presence records are available, providing insight into the potential impacts of land-use and environmental change. Maps identify areas of conservation concern, such as hot spots for diversity, rare or vulnerable species and potential or threatened network corridors, making them useful for ecological impact assessment of proposed developments, and to conservation managers planning habitat creation or improvement.

Internal root aeration enables waterlogging‐tolerant species to grow in anoxic soil. Secondary aerenchyma, in the form of aerenchymatous phellem, is of importance to root aeration in some dicotyledonous species. Little is known about this type of aerenchyma in comparison with primary aerenchyma. Micro‐computed tomography was employed to visualize, in three dimensions, the microstructure of the aerenchymatous phellem in roots of Melilotus siculus . Tissue porosity and respiration were also measured for phellem and stelar tissues. A multiscale, three‐dimensional, diffusion–respiration model compared the predicted O 2 profiles in roots with those measured using O 2 microelectrodes. Micro‐computed tomography confirmed the measured high porosity of aerenchymatous phellem (44–54%) and the low porosity of stele (2–5%) A network of connected gas spaces existed in the phellem, but not within the stele. O 2 partial pressures were high in the phellem, but fell below the detection limit in the thicker upper part of the stele, consistent with the poorly connected low porosity and high respiratory demand. The presented model integrates and validates micro‐computed tomography with measured radial O 2 profiles for roots with aerenchymatous phellem, confirming the existence of near‐anoxic conditions at the centre of the stele in the basal parts of the root, coupled with only hypoxic conditions towards the apex.

The mesoscale population balance modelling (PBM) technique is widely used in predicting aggregation processes. The accuracy and efficiency of PBM depend on the formulation of its kernels. A model of the volume- and time-dependent one-dimensional aggregation kernel is developed for predicting the temporal evolution of the considered particulate system. To make the developed model physically relevant, the PBM model needs three unknown parameters as input: volume-dependency in collisions, collision frequency per particle and aggregation probability. For this, the microscale discrete element model (DEM) is used. The system’s collision frequency is extracted periodically using a novel collision detection algorithm that detects and ignores duplicate collisions. Finally, a multiscale bi-directional PBM–DEM coupling framework is presented to simulate the aggregation mechanism. PBM and DEM simulations take place periodically to update the particle size distribution (PSD) and extract the collision-frequency, respectively. The coupling framework successfully explains the dependence between the PSD and the collision frequency. Additionally, computational cost of the algorithm is optimized while maintaining the accuracy of the results. Lastly, the accuracy and efficiency of the developed framework are verified using two different test cases. In one of the examples, a simple aggregation is simulated directly inside the DEM for the first time.

Geopolymer concrete is an eco-efficient and environmentally friendly construction material. Various ashes were used as the binder in geopolymer concrete, such as fly ash, ground granulated blast furnace slag, rice husk ash, metakaolin ash, and Palm oil fuel ash. Fly ash was commonly consumed to prepare geopolymer concrete composites. It is essential to have 28 days resting period of the concrete to attain compressive strength in the structural design. In the present investigation, several soft computing models were employed to form the predictive models for forecasting the compressive strength of ground granulated blast furnace slag (GGBFS) concrete. A complete dataset of 268 samples was extracted from published research articles and analyzed to establish models. The modeling process incorporated seven effective parameters such as water content ( W ), temperature ( T ), water-to-binder ratio ( w/b ), ground granulated blast furnace slag-to-binder ratio (GGBFS/b), fine aggregate (FA) content, coarse aggregate (CA) content, and the superplasticizer dosage (SP) that were examined and measured on the compressive strength of GGBFS concrete by utilizing various modeling techniques, viz., Linear Regression (LR), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Support Vector Regression (SVR), Grey Wolf Optimization (GWO), Differential Evolution (DE), and Mantra Rays Foraging Optimization (MRFO). The compressive strength of the training datasets was predicted using the SVR-PSO and SVR-GWO models, with a reliable coefficient of correlation of 0.9765 and 0.9522, respectively.

An important determinant of crop yields is the regulation of photosystem II (PSII) light harvesting by energy-dependent quenching (qE). However, the molecular details of excitation quenching have not been quantitatively connected to the fraction of excitations converted to chemical energy by PSII reaction centers (PSII yield), which determines flux to downstream metabolism. Here, we incorporate excitation dissipation by qE into a pigment-scale model of excitation transfer and trapping for a 200 × 200-nm patch of the grana membrane. We show that excitation transport can be rigorously coarse grained to a 2D random walk with an excitation diffusion length determined by the extent of quenching. We present an alternative method for analyzing pulse amplitude-modulated chlorophyll fluorescence measurements that incorporates the effects of a variable excitation diffusion length during qE activation.