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New Research Challenges Assembly Theory’s Claims of Molecular Complexity
Recent studies have critically examined assembly theory, which was proposed in 2017 as a possible “theory of everything” related to molecular complexity. This theory claims the ability to characterize life, provide explanations for natural selection and evolution, and reshape our fundamental understanding of time, matter, and the universe.
Leading this critical analysis, Dr. Hector Zenil from the School of Biomedical Engineering & Imaging Sciences (BMEIS) has collaborated with researchers from King Abdullah University for Science and Technology (KAUST) and the Karolinska Institute in Sweden. In their newly published research in npj Systems Biology, they assert that traditional statistical and compression algorithms can yield the same results as those claimed by assembly theory, thereby questioning the originality of its conclusions.
In a second study published in PLoS Complex Systems, researchers mathematically demonstrated that assembly theory is essentially equivalent to Shannon Entropy. This equivalence implies that the proposed framework does not present a novel viewpoint in the field but rather performs analogous functions to established compression algorithms like those used in ZIP and PNG formats.
The third paper, titled “Assembly Theory Reduced to Shannon Entropy and Rendered Redundant by Naive Statistical Algorithms,” is accessible on the arXiv preprint server for further examination.
According to Dr. Zenil, “Our findings reveal that the Assembly Index, a key element of assembly theory used to assess an object’s ‘aliveness’ based on the number of its exact copies, lacks originality and its conclusions are fundamentally flawed.” He explains that when traditional compression algorithms were utilized to analyze molecular and chemical data, the results were consistent with those generated under assembly theory. “This indicates that assembly theory does not offer a new analytical framework but rather mirrors existing complexity measures,” he notes.
Prof. Jesper Tegner adds to this critique, stating, “It is challenging to assert that plants with significantly longer genomes, such as onions and ferns, possess a higher degree of complexity or ‘aliveness’ than humans based solely on such a simplistic index.” He emphasizes that defining life involves more than just measuring genetic length or component count; it encompasses the complex interactions organisms maintain with their environments and their ability to adapt and survive.
Dr. Narsis A. Kiani also highlights the shortcomings of assembly theory, pointing out that its numerical indices fail to capture the essence of ‘aliveness’ and the dynamic nature of life. “It is surprising that the importance of dynamic interactions has been overlooked in this discourse on life complexity,” he remarks, while asserting that the formulation of a fixed threshold for life detection appears arbitrary and unfounded.
The ongoing quest to define life is complex and multifaceted. Historical contributions to this discourse range from Gregor Mendel’s work on genetic inheritance to Erwin Schrödinger’s exploration of thermodynamics and Claude Shannon’s advances in statistical entropy, culminating in Gregory Chaitin’s algorithmic theories. Current research in complexity sciences and systems biology suggests that open-endedness, or the capacity for life to adapt beyond regular patterns of behavior, is indeed central to understanding life.
Dr. Zenil’s work on Algorithmic Information Dynamics (AID) aims to provide insights into causal models that explain natural phenomena and offer mechanistic understanding of living systems. AID merges information theory with causal inference, setting a foundation for further exploration in the complex dynamics of life.
More information:
Abicumaran Uthamacumaran et al., “On the salient limitations of the methods of assembly theory and their classification of molecular biosignatures,” npj Systems Biology and Applications (2024). DOI: 10.1038/s41540-024-00403-y
Felipe S. Abrahão et al., “Assembly Theory is an approximation to algorithmic complexity based on LZ compression that does not explain selection or evolution,” PLOS Complex Systems (2024). DOI: 10.1371/journal.pcsy.0000014
Luan Ozelim et al., “Assembly Theory Reduced to Shannon Entropy and Rendered Redundant by Naive Statistical Algorithms,” arXiv (2024). DOI: 10.48550/arxiv.2408.15108
Provided by: King’s College London
Source
phys.org