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Novel Algorithm Unlocks Secrets of Active Matter Theory

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Wojciech Zylm
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Novel Algorithm Unlocks Secrets of Active Matter Theory

In a significant stride forward in biological and computational sciences, scientists from the Max Planck Institute of Molecular Cell Biology and Genetics (MPI CBG), the Center for Systems Biology Dresden (CSBD), and the Technical University of Dresden have developed an unprecedented algorithm capable of solving complex equations associated with active matter theory. This breakthrough not only deepens our comprehension of living materials like cells and tissues, but also opens doors to potential discoveries in cellular morphology and the fabrication of artificial biological machines.

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Decoding the Mechanics of Living Materials

Living materials, such as cells and tissues, are composed of individual components that convert chemical energy into motion. This process creates patterns and dynamics, a phenomenon propelled by the continuous consumption of energy, making these materials active matter. Active matter theory, which describes the mechanics of living materials, involves intricate mathematical equations which, until now, have been challenging to decipher.

A New Era in Computational Sciences

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The scientists have introduced an open-source supercomputer algorithm that can predict the patterning and dynamics of active matter in three dimensions and complex geometries, akin to a dividing cell. This development allows for the exploration of these material's behaviors across space and time, bringing us a step closer to solving the age-old conundrum of how cells and tissues attain their shape.

Implications and Future Directions

The research, led by a team including Frank Jülicher, Stephan Grill, and Ivo Sbalzarini, with significant contributions from mathematicians Abhinav Singh and Philipp Suhrcke, was published in the journal Physics of Fluids and featured on its cover. The algorithm was implemented using the OpenFPM library, accessible to the scientific community. This tool holds profound implications for understanding cellular morphology, crafting artificial biological machines, and potentially addressing the fundamental question of morphogenesis.

The development of this algorithm, which is scalable on shared and distributed memory multi-processor parallel supercomputers and can also run on regular office computers for studying two-dimensional materials, is a testament to the strides made in computational sciences. As we continue to unravel the complexities of active matter, the advancement heralds a new era in our understanding of the living world.

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