Deciphering Life’s Mysteries: Breakthroughs in Biology Through Active Matter Theory

by Henrik Andersen
Active Matter Theory

A collective of researchers has crafted an innovative algorithm to unravel the equations of active matter theory, significantly advancing our comprehension of biological substances. This groundbreaking achievement in the realms of biology and computational science is set to revolutionize our grasp of cellular structures and the development of synthetic biological machinery.

The cutting-edge algorithm, available as open-source on advanced supercomputers, offers insights into the patterns and dynamics of biological substances, enabling the study of their behavior over various scales and periods.

These biological materials are made up of units, including minuscule motors that convert energy into motion, forming movement patterns. This self-organizing process results in the material shaping itself through energy-driven coherent flows. Such constantly energized materials are termed “active matter.”

Active matter theory provides a scientific framework to decode the shape and flow dynamics of living materials, involving complex mathematical equations.

Researchers from the Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG), the Center for Systems Biology Dresden (CSBD), and the TU Dresden have innovated an algorithm, integrated into an open-source supercomputer program, capable of solving these equations realistically. This breakthrough moves us closer to deciphering how cells and tissues form their shapes and to the creation of artificial biological entities.

A 3D simulation of active matter mirrors the division process of a cell. This is noted in the work of Singh et al., published in Physics of Fluids (2023) / MPI-CBG.

Understanding biological behaviors is complex, and physical theories offer precise frameworks for this. The active matter theory elucidates the behavior of materials composed of individual elements that convert chemical energy into mechanical forces.

Key contributors to this theory include Frank Jülicher, director at the Max Planck Institute for the Physics of Complex Systems, and Stephan Grill, director at the MPI-CBG. By applying physics principles, the dynamics of living active matter can be mathematically modeled and forecasted.

The complexity of these equations necessitates supercomputers for analysis and understanding. Different approaches are used to predict active matter behavior, focusing on individual particles, molecular-level interactions, or large-scale active fluids. These methods allow the observation of active matter behaviors across different scales and timeframes.

In an effort to resolve these intricate equations, the team led by Ivo Sbalzarini, Professor at TU Dresden and affiliated with CSBD and MPI-CBG, developed a computer algorithm. Published in Physics of Fluids and highlighted on its cover, the algorithm successfully solves active matter equations in three-dimensional and complex spaces.

Abhinav Singh, a mathematician and one of the study’s lead authors, explains that their method can handle various shapes in three dimensions over time, even with irregular data point distribution. This approach enables accurate solutions in biologically realistic scenarios. It enhances our understanding of active materials’ long-term behavior in both static and dynamic states, aiding in the prediction of their dynamics and potentially in programming biological materials or creating nano-scale engines.

Philipp Suhrcke, another lead author and graduate of TU Dresden’s Computational Modeling and Simulation M.Sc. program, emphasizes the significance of their work in predicting the shapes and stability of biological materials, with important implications for understanding growth and disease mechanisms.

The team has made their software publicly accessible through the OpenFPM library, democratizing large-scale scientific computing. They developed a custom computer language enabling computational scientists to write supercomputer codes directly in mathematical notation. This innovation reduces code development time in scientific research significantly.

Due to the high computational demands of studying three-dimensional active materials, the new code is scalable on various supercomputers and can also run on standard computers for two-dimensional studies.

Ivo Sbalzarini, the Principal Investigator, remarks that a decade of research has culminated in this simulation framework, enhancing computational science productivity. This tool, open-source, scalable, and capable of handling complex scenarios, is pivotal in understanding the behavior of living materials and could unlock the mystery of cell and tissue morphogenesis, a question that has intrigued scientists for centuries. It may also pave the way for designing minimal-component artificial biological machines.

Reference: Singh et al.’s study, titled “A numerical solver for active hydrodynamics in three dimensions and its application to active turbulence,” was published on 30 October 2023 in Physics of Fluids, with a DOI of 10.1063/5.0169546.

The study received funding from the Federal Ministry of Education and Research (BMBF), the Federal Center for Scalable Data Analytics and Artificial Intelligence, ScaDS.AI, Dresden/Leipzig.

The supporting computer code for this study is available in the 3Dactive-hydrodynamics GitHub repository at

OpenFPM, the open-source framework, can be found at

Related publications on the embedded computer language and the OpenFPM software library can be accessed via the provided DOI links.

Frequently Asked Questions (FAQs) about Active Matter Theory

What is active matter theory?

Active matter theory is a scientific framework used to understand the behavior, shape, and flow dynamics of living materials. It involves complex mathematical equations that describe how these materials, composed of individual components like tiny motors, self-organize and move by converting energy into motion.

How does the new algorithm advance our understanding of living materials?

The newly developed algorithm allows scientists to solve the complex equations of active matter theory in realistic scenarios. This facilitates a deeper understanding of the formation and dynamics of cellular structures and tissues, and aids in the creation of artificial biological machines.

Who developed the algorithm for active matter theory?

The algorithm was developed by a team of scientists from the Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG), the Center for Systems Biology Dresden (CSBD), and the TU Dresden.

What are the applications of this new algorithm in biological sciences?

This algorithm enables scientists to predict the behavior and shape of biological materials, understand the mechanics of cells and tissues, and potentially design artificial biological machines. It has far-reaching implications in understanding growth processes and disease mechanisms in biological systems.

Is the algorithm for solving active matter theory equations accessible to other researchers?

Yes, the algorithm is implemented in an open-source supercomputer code and is available for public use. It is part of the OpenFPM library, allowing broad accessibility for scientific research and collaboration.

What makes this algorithm unique in studying active matter?

The algorithm is capable of solving active matter equations in three dimensions and complex-shaped spaces, which is essential for accurately modeling and predicting the behavior of biological materials in realistic scenarios.

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Sarah K. November 23, 2023 - 10:01 am

amazing work by the team at Max Planck and TU Dresden, this could lead to some real breakthroughs in how we understand cell behavior and diseases…

Mike Johnson November 23, 2023 - 3:14 pm

Wow, this is huge for science! Can’t believe they finally cracked the code on active matter theory. This is gonna change everything we know about bio materials.

Greg H November 23, 2023 - 9:28 pm

I’m not a scientist, but this sounds complicated. How do they even begin to solve these equations? Must need some serious brain power!

Emma Thompson November 23, 2023 - 9:41 pm

Really excited to see what comes next, especially with the open-source aspect. means lots of brains can work together on this, right? the future is bright for biology!

Daniel L. November 23, 2023 - 9:54 pm

Is it just me or does this sound like the start of creating real-life artificial organs? If they can predict how tissues form, that’s a big step forward.


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