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Home » Catalytic molecules self-organize into metabolically active clusters

Catalytic molecules self-organize into metabolically active clusters

Researchers from the Max Planck Institute for Dynamics and Self-Organization (MPI-DS) have discovered an intriguing in their recent study. They found that catalytic can form metabolically active clusters by creating and following concentration gradients, which has significant implications for understanding the origin of life.

Their model predicts that molecules involved in metabolic pathways can self-organize into dynamic functional structures, shedding light on the formation of complex biological networks. This understanding could serve as a platform for experiments on the origins of life.

One potential scenario for the emergence of life involves the spontaneous organization of interacting molecules into cell-like droplets. These molecules would form the first self-replicating metabolic cycles, which are essential in biology and exist throughout all organisms. However, the traditional notion of slow cluster formation seems at odds with the rapid appearance of life.

To address this, scientists from the department of Living Matter Physics at MPI-DS proposed an alternative model. They considered different molecules in a simple metabolic cycle, where each species produces a chemical used by the next one. The key elements in the model are the catalytic activity of the molecules, their ability to follow concentration gradients of the chemicals they produce and consume, and the information on the order of molecules in the cycle.

Remarkably, the model demonstrated the exponential and rapid growth of catalytic clusters comprising various molecular species. This suggests that molecules can assemble quickly and in large numbers into dynamic structures, explaining the fast onset of chemical reactions necessary for life to form.

Additionally, the number of molecule species participating in the metabolic cycle plays a crucial role in determining the structure of the formed clusters. The researchers found that the model leads to various complex scenarios for self-organization, making specific predictions about functional advantages that arise based on the odd or even number of participating species.

In another study, the authors discovered that self-attraction is not a requirement for clustering in a small metabolic network. Network effects can cause even self-repelling catalysts to aggregate, revealing new conditions where complex interactions lead to self-organized structures.

Source: Max Planck Society

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