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MULTISCALE SIMULATIONS

Linking the length scales

Nature Machine Intelligence volume 3, pages 374–375 (2021)Cite this article

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A challenge for multiscale simulations is how to link the macroscopic and microscopic length scales effectively. A new machine-learning-based sampling approach enables full exploration of macro configurations while retaining the precision of a microscale model.

All biological systems are inherently multiscale. From the dynamics of molecules to protein–protein interactions, from cellular assembly to whole organism development, the optimization of biological processes under environmental stress, which is the fundamental law of evolution, requires the tight integration between chemical and physical processes happening at very different time and space scales. Along the centuries, scientists have developed tools to examine biological systems at almost any scale imaginable, from genes to whole populations. As different scales are not isolated, but rather intertwined so that information can be shared or passed across levels, the need for modelling such complex systems at multiple scales has emerged. A new multiscale computational approach is reported in Nature Machine Intelligence, as Harsh Bhatia and co-authors propose dynamic-importance (DynIm) sampling, a machine-learning-based sampling approach that tackles the challenge of linking different scales1.

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Fig. 1: Illustration of multiscale simulations of RAS–lipid interactions.

References

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Authors and Affiliations

  1. IBM Almaden Research Center, San Jose, CA, USA

    Shangying Wang & Simone Bianco

  2. Center for Cellular Construction, San Francisco, CA, USA

    Shangying Wang & Simone Bianco

Authors
  1. Shangying Wang
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  2. Simone Bianco
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Correspondence to Shangying Wang.

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The authors declare no competing interests.

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Wang, S., Bianco, S. Linking the length scales. Nat Mach Intell 3, 374–375 (2021). https://doi.org/10.1038/s42256-021-00351-w

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  • DOI: https://doi.org/10.1038/s42256-021-00351-w

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