November 22, 2023A team of scientists has developed a new algorithm to solve theoretical equations for active materials, deepening our understanding of living materials. This research is of vital importance in biology and computational science, paving the way for new discoveries in cell morphology and the creation of artificial biological machines. Advanced open-source supercomputer algorithms predict the patterns and dynamics of living matter and enable exploration of its behavior across space and time. Biological materials are made up of individual components, such as tiny motors that convert fuel into motion. This process creates a pattern of movement, guiding the shape of the material itself through a consistent flow driven by constant energy consumption. Such permanently driven substances are called “active substances.”

How cells and tissues work can be explained by active matter theory, a scientific framework for understanding the shape, flow, and form of living matter. Active matter theory consists of many difficult mathematical equations. Scientists from Dresden’s Max Planck Institute for Molecular Cell Biology and Genetics (MPI-CBG), the Dresden Center for Systems Biology (CSBD), and the Dresden University of Technology have developed an algorithm implemented in open-source supercomputer code. For the first time, you can solve active matter theory equations in realistic scenarios. These solutions bring him a big step closer to solving his century-old mystery of how cells and tissues acquire their shape, and to designing artificial biological machines. 3D simulation of active substances in a dividing cell-like geometry. Credit: Singh et al. Physics of Fluids (2023) / MPI-CBG

Biological processes and behaviors are often highly complex. Physical theory provides a precise and quantitative framework for understanding physical theories. Active matter theory provides a framework for understanding and explaining the behavior of active substances, which are materials made up of individual components that can convert chemical fuels (“food”) into mechanical forces. The development of this theory was led by several Dresden scientists, including Frank Uricher, director of the Max Planck Institute for Complex Systems Physics, and Stefan Grill, director of MPI-CBG. These physical principles allow us to mathematically describe and predict the dynamics of active organisms. However, these equations are very complex and difficult to solve. Therefore, scientists need the power of supercomputers to understand and analyze living matter. There are various ways to predict the behavior of active materials, including by focusing on small individual particles, by studying active materials at the molecular level, and by studying active fluids on a larger scale. These studies help scientists understand how active substances behave at different scales in space and time. Scientist in the research group of Dresden University of Technology Ivo Sbalzarini Professor at the Dresden Center for Systems Biology (CSBD), research group leader at the Max Planck Institute molecular cell The Dean of the Department of Biology and Genetics (MPI-CBG) and Computer Science at the Technical University of Dresden has now developed a computer algorithm to solve the active substance equation. Their research was published in the journal fluid physics and it appeared on the cover. They present an algorithm that is capable of solving complex equations for active materials in three-dimensional and complex-shaped spaces.

“Our approach can handle a variety of shapes in three dimensions over time,” says research mathematician Abhinav Singh, one of the study’s first authors. He continued, “Even when the data points are not regularly distributed, our algorithm employs a novel numerical approach that works seamlessly for complex biologically realistic scenarios, and the theoretical equations Using our approach, we can finally understand the long-term behavior of active materials in both mobile and non-mobile scenarios in order to predict dynamic scenarios. Additionally, theory and simulation can be used to program biological materials and create engines at the nanoscale to extract useful work.” The other first author, Philipp Suhrcke, holds a master’s degree in computational modeling and simulation from the Technical University of Dresden. “Thanks to our research, scientists can predict, for example, the shape of tissues and when biological materials will become unstable or dysregulated, leading to growth and disease. This has far-reaching implications for our understanding of mechanisms.”

The scientists implemented the software using the open source library OpenFPM. This means that others can use it freely. OpenFPM was developed by his Sbalzarini group to democratize large-scale scientific and technical computing. The authors first developed a custom computer language that allows computational scientists to write code for a supercomputer by specifying mathematical formulas that let the computer do the work of writing the correct program code. As a result, you no longer have to start from scratch every time you write code, effectively reducing code development time in scientific research from months or years to days or weeks, greatly increasing productivity.

Because the study of three-dimensional active materials has significant computational demands, using OpenFPM the new code is scalable on shared and distributed memory multiprocessor parallel supercomputers. This application is designed to run on powerful supercomputers, but can also be run on regular office computers to study 2D materials. Ivo Sbalzarini, the study’s lead researcher, summarizes: All this has been integrated into a tool for understanding her three-dimensional behavior of living matter. Our code, which is open source, scalable, and able to handle complex scenarios, opens new avenues in active materials modeling. This could ultimately lead to an understanding of how cells and tissues acquire their shape, addressing fundamental questions in morphogenesis that have puzzled scientists for centuries. There is a gender. But it may also be useful for designing artificial biological machines with minimal components.

References: “Numerical solver for three-dimensional active fluid dynamics and its application to active turbulence” by Abhinav Singh, Philipp H. Suhrcke, Pietro Incardina, and Ivo F. Sbalzarini, October 30, 2023. fluid physics. DOI: 10.1063/5.0169546 This research was funded by the Federal Ministry of Education and Research (Bundesministerium f€ur Bildung und Forschung, BMBF), the Federal Center for Scalable Data Analysis and Artificial Intelligence, ScaDS.AI, and Dresden/Leipzig. The computer code supporting the results of this study is publicly available in the 3Dactive-hydynamics github repository at: https://github.com/mosaic-group/3Dactive-hydrodynamic sThe open source framework OpenFPM is available at: https://github.com/mosaic-group/openfpm_pdataRelated publications for embedded computer languages https://doi.org/10.1016/j.cpc.2019.03.007https://doi.org/10.1140/epje/s10189-021-00121-x (function (d, s, id) {var js, fjs = d.getElementsByTagName (s) [0]; if (d.getElementById (id)) return; js = d.createElement (s); js.id = id; js.src = “https://connect.facebook.net/en_US/sdk.js#xfbml=1&version=v2.6”; fjs.parentNode.insertBefore (js, fjs); } (document, ‘script’, ‘facebook-jssdk’));

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