Multiscale modelling, simulation and computing: from the desktop to the exascale

Hoekstra, Alfons G.; Zwart, Simon Portegies; Coveney, Peter V.

doi:10.1098/rsta.2018.0355

Cited by 15 publications

(10 citation statements)

References 21 publications

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“…UQ is an established domain in applied mathematics and engineering but has been notably absent inter alia from the analysis of computer simulations performed using electronic structure and molecular simulation methods. At this time, we are witnessing unified developments in quantifying uncertainty in computer simulation across a wide range of domains including weather, climate, material, fusion, molecular and biomedical sciences [7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Rapid, accurate, precise and reproducible ligand–protein binding free energy prediction

et al. 2020

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A central quantity of interest in molecular biology and medicine is the free energy of binding of a molecule to a target biomacromolecule. Until recently, the accurate prediction of binding affinity had been widely regarded as out of reach of theoretical methods owing to the lack of reproducibility of the available methods, not to mention their complexity, computational cost and time-consuming procedures. The lack of reproducibility stems primarily from the chaotic nature of classical molecular dynamics (MD) and the associated extreme sensitivity of trajectories to their initial conditions. Here, we review computational approaches for both relative and absolute binding free energy calculations, and illustrate their application to a diverse set of ligands bound to a range of proteins with immediate relevance in a number of medical domains. We focus on ensemble-based methods which are essential in order to compute statistically robust results, including two we have recently developed, namely thermodynamic integration with enhanced sampling and enhanced sampling of MD with an approximation of continuum solvent. Together, these form a set of rapid, accurate, precise and reproducible free energy methods. They can be used in real-world problems such as hit-to-lead and lead optimization stages in drug discovery, and in personalized medicine. These applications show that individual binding affinities equipped with uncertainty quantification may be computed in a few hours on a massive scale given access to suitable high-end computing resources and workflow automation. A high level of accuracy can be achieved using these approaches.

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Section: Introductionmentioning

confidence: 99%

Rapid, accurate, precise and reproducible ligand–protein binding free energy prediction

et al. 2020

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“…While the quantitative nature of our formalism gives us the freedom to integrate specific numerical models of systems biology within our framework, its hierarchical nature provides a multi-scale modeling logic giving the possibility of integrating such methods consistently across all scales of biology. From the point of view of the literature, our work tackles the longstanding need of 'a generic theory or calculus of multi-scale modeling' for systems biology, as explained in (32). Based on Castiglione et al (33), our framework addresses the 'ultimate goal of multi-scale modeling' by proposing a mathematical language in which it is possible to 'link [every scale] in a consistent manner so that the information from a lower scale can be carried into the simplified model of a higher scale'.…”

Section: Discussionmentioning

confidence: 99%

Cellular intelligence: dynamic specialization through non-equilibrium multi-scale compartmentalization

et al. 2021

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Intelligence is usually associated with the ability to perceive, retain and use information to adapt to changes in one's environment. In this context, systems of living cells can be thought of as intelligent entities. Here, we show that the concepts of non-equilibrium tuning and compartmentalization are sufficient to model manifestations of cellular intelligence such as specialization, division, fusion and communication using the language of operads. We implement our framework as an unsupervised learning algorithm, IntCyt, which we show is able to memorize, organize and abstract reference machine-learning datasets through generative and self-supervised tasks. Overall, our learning framework captures emergent properties programmed in living systems, and provides a powerful new approach for data mining. to memorize, organize and abstract reference machine-learning datasets through generative and self-supervised tasks. Overall, our learning framework captures emergent properties programmed in living systems, and provides a powerful new approach for data mining.

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“…These phenomena naturally, and computationally, are multi-scale both in time and space [4,13,15,16,18,28,32]. Multi-scale computing [3,5,10,11,14,[17][18][19] depends on scale separation and invokes set of single-scale models, each representing a process in time and space, coupled together to describe a phenomenon ranging over temporal and spatial scales. From a computational point of view, the single-scale models, which by themselves could be massively parallel simulations, could run independently of each other, which provide new opportunities in terms of scalability and performance tuning for the emerging exascale [5,17].…”

Section: Introductionmentioning

confidence: 99%

Towards Heterogeneous Multi-scale Computing on Large Scale Parallel Supercomputers

2019

JSFI

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New applications that can exploit emerging exascale computing resources efficiently, while providing meaningful scientific results, are eagerly anticipated. Multi-scale models, especially multi-scale applications, will assuredly run at the exascale. We have established that a class of multi-scale applications implementing the heterogeneous multi-scale model follows, a heterogeneous multi-scale computing (HMC) pattern, which typically features a macroscopic model synchronising numerous independent microscopic model simulations. Consequently, communication between microscopic simulations is limited. Furthermore, a surrogate model can often be introduced between macro-scale and micro-scale models to interpolate required data from previously computed micro-scale simulations, thereby substantially reducing the number of micro-scale simulations. Nonetheless, HMC applications, though versatile, remain constrained by load balancing issues. We discuss two main issues: the a priori unknown and variable execution time of microscopic simulations, and the dynamic number of micro-scale simulations required. We tackle execution time variability using a pilot job mechanism to handle internal queuing and multiple sub-model execution on large-scale supercomputers, together with a data-informed execution time prediction model. To dynamically select the number of micro-scale simulations, the HMC pattern automatically detects and identifies three surrogate model phases that help control the available and used core amount. After relevant phase detection and micro-scale simulation scheduling, any idle cores can be used for surrogate model update or for processor release back to the system. We demonstrate HMC performance by testing it on two representative multi-scale applications. We conclude that, considering the subtle interplay between the macroscale model, surrogate models and micro-scale simulations, HMC provides a promising path towards exascale for many multiscale applications.

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Multiscale modelling, simulation and computing: from the desktop to the exascale

Cited by 15 publications

References 21 publications

Rapid, accurate, precise and reproducible ligand–protein binding free energy prediction

Rapid, accurate, precise and reproducible ligand–protein binding free energy prediction

Cellular intelligence: dynamic specialization through non-equilibrium multi-scale compartmentalization

Towards Heterogeneous Multi-scale Computing on Large Scale Parallel Supercomputers

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