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Supercomputer der Universität unterstützt Kampf gegen COVID-19

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Veröffentlicht am Freitag, den 26. Juni 2020

Das High Performance Computing (HPC, der Höchstleistungsrechner) der Universität hat seit Mitte März 2020 wesentlich zum Kampf gegen die COVID-19-Pandemie beigetragen.

Der „Supercomputer“ und das Team unter der Leitung von Prof. Pascal Bouvry und Dr. Sébastien Varrette haben mit ihren Rechenressourcen Universitätsforscher und externe Partner in mehr als sieben Projekten unterstützt.

HPC liefert eine hohe Leistung, um große Probleme schneller lösen zu können. Aufgaben, deren Berechnung auf einem typischen Desktop-Computer normalerweise mehrere Jahre dauern würde, können mit einem HPC-System in nur wenige Stunden, Tage oder Wochen durchgeführt werden. Und für die Forschung zur Bekämpfung der COVID-19-Pandemie ist die Beschleunigung der Zeit bis zur Lösung ein entscheidendes Kriterium, um die Ausbreitung der Pandemie effizient einzudämmen.

Die enorme Rechenleistung und Speicherkapazität des Supercomputers der Universität wurde genutzt, um die COVID-19-Forschung in den Bereichen Biomedizin und Biowissenschaften, IKT und Materialwissenschaften zu ermöglichen und zu beschleunigen. Er ermöglicht unter anderem maschinellem Lernen basierende Schätzungen der Bioverfügbarkeit der Lunge; die Modellierung von Geschäftsökosystemen und Simulationstechniken zur Unterstützung wirtschaftspolitischer Entscheidungsträger in Luxemburg und im Ausland und ermöglicht gleichzeitig die Berechnung von Prognosen für die Sichtbarkeit des Coronavirus auf Oberflächen.

Die HPC-Dienste der Universität unterstützen vier universitätsgeführte Projekte, die im Rahmen des FNR COVID-19 Fast Track Calls finanziert werden, ein Projekt der Research Luxembourg COVID-19 Arbeitsgruppe und eine gemeinschaftliches Projekt zwischen dem Luxembourg Centre for Systems Biomedicine der Universität, der TU München und dem Flatiron-Institut.

Die HPC-Einrichtung ist ein Element der umfangreichen digitalen Forschungsinfrastruktur und -kompetenz, die die Universität in den letzten Jahren entwickelt hat. Sie unterstützt auch die ehrgeizige digitale Strategie der Universität und insbesondere die Schaffung einer Einrichtung für Daten und HPC-Wissenschaften. Diese Einrichtung zielt darauf ab, eine exzellente nutzerorientierte digitale Infrastruktur und Dienste bereitzustellen, um die Entwicklung von Kooperationen im Zusammenhang mit der Pionierforschung und -lehre in den Bereichen Computer- und Datenwissenschaften zu fördern (einschließlich Hochleistungsrechnen, Datenanalyse, Großdatenanwendungen, künstliche Intelligenz und maschinelles Lernen).

Mehr als 1030 Aufträge wurden auf die vom HPC-Team festgelegten Reservierungen gesetzt (der längste Auftrag dauerte 58 Tage). Eine Übersicht über die damit verbundene Lastnutzung in der kritischsten Periode der Pandemie ist in Abbildung 1 dargestellt. Das HPC der Universität ist nach wie vor entschlossen, Ressourcen und Beratung für laufende und zukünftige COVID-19-bezogene Projekte bereitzustellen

 

Abbildung 1: Überblick über die von den COVID-19-Projekten von Mitte März bis Ende April verwendeten Rechenressourcen.

Die hohe Nutzungsrate der Ressourcen während dieser kritischen Periode zeigt das starke Engagement und die Zusammenarbeit aller UL-Partner bei der Bekämpfung der Pandemie. Die folgende Liste stellt die wichtigsten COVID-19-bezogenen Projekte vor, die sich auf die HPC-Computerressourcen der Uni stützten:

Combined In Silico Molecular Docking And In Vitro Experimental Assessment Of Drug Repurposing Candidates For Covid-19 (CovScreen)

Currently, no vaccine or sufficiently validated pharmacological treatment is available for COVID-19. Drug-based strategies to reduce the viral load in patients with severe forms of COVID-19 include the repurposing of existing small molecule compounds that inhibit the activity of key viral proteins or human proteins involved in mediating viral entry or release from the host cell. The project proposes a combined computational and experimental approach to rank an alternative candidate known as drugs, antivirals, and natural compounds, which are commercially available, inexpensive, and safe in humans. The project screens and filters in silico x M~10k compounds using molecular docking and machine learning based lung bioavailability estimations and conducts molecular dynamics simulations for refined binding affinity estimation of the 100 top-ranked compounds. The project aims to provide a fast experimental validation of drug repurposing candidates for COVID-19 from a computational pre-selection of antivirals, drugs, and natural compounds that are inexpensive, have known safety properties, and high predicted bioavailability in the lung. The project is led by Enrico Glaab.

Figure 2: A) Modeled SARS-CoV-2 Spike Glycoprotein overlaid with the SARS-CoV (PDB: 2GHV) unique amino acids are shown. Variable amino acid residue side chains are shown: Green: SARS-CoV Red: SARS-CoV-2. B) Minimised final structure of modeled SARS-CoV-2 spike glycoprotein. Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7152904/figure/fig1/

Machine Learning To The Rescue: From Health Recovery To Economic Revival (REBORN)

REBORN is a data science project that focuses on ensuring sustainable economic recovery in the face of COVID-19. The project team applies advanced Machine Learning, business ecosystem modelling (i.e., expert knowledge) and simulation techniques to yield recommendations of economic actions given different scenarios in which the lockdown is relaxed, partially or totally lifted. By interacting with other teams of the Luxembourg Task Force, this project targets high impact for the various sectors of the Luxembourg economy, by providing appropriate data-driven recommendations for political decision-makers. REBORN aims to answer important questions such as: What are the industrial sectors to help in priority? What are the sectors that should be restarted first? What are the possible changes in consumer habits and the impact of neighboring country decisions on commuters? etc. Ultimately, REBORN contributes towards reflections on initiatives to limit the spread of the future economic crisis due to COVID-19 as well as avoid worsening future waves of coronaviruses. The project is led by Jacques Klein.

Figure 3: Tools to strengthen resilience for COVID-19.
Source: https://council.science/current/blog/setting-up-a-data-ecosystem-to-defeat-covid-19/

Privacy Preserving Monitoring of Social Distancing In Public Environments Machine Learning, Computer Vision, Social Distancing, GDPR By Design (PEOPLE)

The project aims to provide a platform to run a comprehensive analysis on the Social Distancing measures decided by the government in the context of the COVID-19 pandemic. It aims to analyse anonymised video data in the city of Luxembourg. The first step is to anonymise the video feed using well-known Artificial Intelligence (AI) models (face blurring). The next step uses other AI models to identify pedestrians and groups of individuals, calculate their relative distances and overall density. Those metrics can then be evaluated over time for different locations and provide valuable insights on the greater or lesser risks of infection spreading based on behaviour. The rules can be used to inform where the police need to focus their efforts on enforcing laws or informing and influencing the public’s actions (or both). The project is led by Raphael Frank.

Virus-surface Interactions In Dynamic Environments (V-SIDE)

The scientific community is trying to establish how long COVID-19 can survive on a given surface (e.g., paper, plastics, glass, and metals). Their properties (smooth v/s rough) play a crucial role during the infection spreading phase. Using the Fourier-transform infrared spectroscopy (FTIR) helps to predict the surface-specific visibility of the coronavirus in the steady-state and dynamic state (e.g., changes in temperature and humidity). Later the obtained model will be mapped into the biophysical model system. Machine learning techniques are used for an optimistic and future prediction of the visibility of the virus on the surfaces. This is the part where the UL HPC software and hardware facilities will be used. The project is led by Anupam Sengupta.

Figure 4: How long does the COVID-19 virus survive on surfaces?
Source: New England Journal of Medicine

Sars-CoV-2 protein structure prediction

Bioinformaticians from Rostlab at TU Munich, the LCSB and the Flatiron institute joined forces to participate in a worldwide effort to predict 3D structures of Sars-CoV-2 proteins. The project was organised by the team behind CASP (Critical Assessment of protein Structure Prediction), who selected ten viral proteins for which no experimental structure is available, nor can homology-based modeling be used to infer structure. The goal is to obtain consensus structures, which would give insight into the molecular mechanisms of the virus, as well as aid vaccine development and the evaluation of possible drug targets.

To this end, the team from TUM and LCSB, helped by collaborators at the Flatiron Institute, trained multiple Deep Learning systems, leveraging evolutionary information from multiple sequence alignments, to predict pairwise distances of amino acids in proteins. The hardest targets of previous CASP competitions were used to assess the reliability of predictions. Computer generated distance maps were then used as constraints to simulate protein folding and obtain 3D structures. Three nodes of the Iris HPC cluster with four Tesla GPUs each (in addition to three similar nodes at the Flatiron Institute and one node from the UCC at TUM) were used to prepare datasets, as well as to train DL models, enabling high quality submissions within the initiative's short-term.

Figure 5: (left) Atomic-level structure of the spike protein of the virus that causes COVID-19 (Source: McLellan Lab, University of Texas at Austin); (right) Visualisations of predicted protein structures from this project.

COVID-19 Task Force

In response to the global COVID-19 pandemic, the COVID-19 Task Force was set up by Research Luxembourg at the early stage of the confinement. As part of Work Package 6 (statistical pandemic projections), researchers from LCSB have developed a computational agent-based model that is essential in understanding the progression of the Covid-19 epidemic in Luxembourg. The statistical projections are required to avoid saturation of the healthcare system and can be used to aid in political decision-making by simulating the epidemic development and its associated outcomes under various future scenarios and strategies.

In order to make informed decisions, hundreds of scenarios with several randomised replicates need to be considered. Parallelisation of the underlying computer code has been key to speed up the simulations and reduce the computing time to an absolute minimum. Thanks to the HPC platform of the University of Luxembourg and its team, it is now possible to launch hundreds of scenarios simultaneously, leading to results being made available to decision-makers within minutes.

Medical image analysis of X-ray and CT of COVID19

During the past weeks, the HPC infrastructure and in particular the GPUs were employed for the AICovIX project. This project focuses on the development of automated solutions for the analysis of medical image analysis of X-ray and CT of COVID19 pneumonia patients using computer vision and deep learning techniques. This work is conducted by researchers of the INS group.

Figure 6: Chest X-rays from a patient with COVID-19 pneumonia, original x-ray (left) and AI-for-pneumonia result (right) (Photo courtesy of UC San Diego Health).