Biomedical Science

The HANAMI project’s biomedical research will focus on molecular simulations related to respiratory diseases and cellular simulations focusing on cancer research.

Molecular dynamics simulations are used as a complement to experimental methods, offering high space and time resolution for better understanding molecular processes. Researchers will enhance data-driven simulations by combining information from various experimental techniques to guide state sampling, using the European GROMACS code and the Japanese GENESIS code. Cellular simulations leverage HPC to model and analyse biological processes within cells while capturing their interactions with the extracellular environment. By integrating multimodal molecular data—such as DNA mutations and gene expression—these simulations enhance our understanding of complex cellular behaviours, enabling the design of targeted therapies. Moreover, they provide a robust framework for reproducing emergent phenomena observed in real cell populations, such as tumour growth or the progression of viral infections. In this context, the widely used PhysiCell framework, along with its extension PhysiBoSS, offers a powerful approach for researchers to explore how intracellular behaviour influences tissue integrity at larger scales and vice versa.

To tackle these challenges, a combined approach using CFD, HPC, and machine learning (ML) will be needed, utilizing both European (m-AIA) and Japanese (CUBE) codes. Internally, the focus is on supporting surgeons in treating respiratory diseases. An automated surgery planning tool, currently limited, will be improved with European and Japanese resources and techniques.

This can help increase the success rate of surgeries, such as those for nasal septum deviation, which currently stands at just 55%. Externally, the focus will be on improving airflow in waiting rooms, including managing the spread of droplets from coughing. The goal is to optimize the environment, such as ventilation design. From the internal perspective, optimization will be enhanced by integrating jointly developed ML algorithms.

In the scope of Biomedical Science area, HANAMI will work on three different projects:

1

Exascale Electrostatics & Machine Learning to Enable Molecular Dynamics of Cell-size Systems

2

Development of Genome Analysis Pipelines for Personalized Medicine

3

Personalized Medicine on Macroscopic Level: Respiratory Flows and Infection Risk Analyses

Exascale Electrostatics & Machine Learning to Enable Molecular Dynamics of Cell-size Systems

The molecular life science pillar strategy aims to expand existing collaborations on software and hardware in molecular dynamics simulations, focusing on long-term development of the GROMACS (EU) and GENESIS (RIKEN) codes. It also involves coordinating with the simulation hardware from the RIKEN MD-GRAPE project. These two codebases complement each other: GENESIS specializes in scaling on Fugaku’s high-end ARM CPUs, while GROMACS is optimized for various GPU (NVIDIA, AMD, Intel) and CPU architectures. The collaboration will use both codes as benchmarks and optimization targets in post-Fugaku projects, offering a unique co-design opportunity for both European and RIKEN teams.

Development of Genome Analysis Pipelines for Personalized Medicine

This project builds on past and ongoing collaborations between Japan and Europe, including the International Cancer Genome Consortium (ICGC) and the ICGC-Argo project. Its primary objective is to develop cellular simulations based on PhysiBoSS software capable of replicating disease progression in individual patients by integrating molecular data derived from their genomic profiles, treatment responses, and tissue characteristics.

To ensure accuracy, these simulations will be trained on synthetic data designed to realistically mimic real tumours and capture key cancer features. Then, their effectiveness will be assessed by deploying and testing the developed pipelines in both European and Japanese HPC environments. By establishing a robust infrastructure for personalized medicine, this project aims to lay the foundation for future digital twins, enabling physicians to tailor and optimize cancer treatment strategies.

Personalized Medicine on Macroscopic Level: Respiratory Flows and Infection Risk Analyses

This project builds on a long-standing collaboration between the Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich, and the RIKEN Center for Computational Science (R-CCS), under the Joint Laboratory for Extreme Scale Computing (JLESC). In the first project, "Comparison of Meshing and CFD Methods for Accurate Flow Simulations on HPC systems," the m-AIA and CUBE codes from RWTH Aachen University and RIKEN R-CCS were ported and benchmarked on Jülich and RIKEN machines. The recent project, "Deep Neural Networks for CFD Simulations," focuses on developing AI methods for engineering applications, particularly in biomedical computations, such as respiratory flow analysis for personalized medicine, part of CoE RAISE.

The first goal of this project is to develop AI-assisted automated methods for running large-scale computational fluid dynamics (CFD) simulations, including analyzing results for individual patients. The second goal is to use AI methods and novel hardware technologies at JSC and RIKEN R-CCS to speed up computations. The third goal is to provide an AI-assisted surgery planning tool for surgeons as a demonstration. The methodologies developed will also help achieve the final goal: assessing the risk of exhaled infectious viruses. All implementations will enhance the capabilities of the m-AIA and CUBE codes.

What they say about HANAMI

More testimonials

Meet the Team

Keep exploring
our project!

Results