Volkening, Alexandria. Volkening, Alexandria. Methods for quantifying self-organization in biology: a forward-looking survey. arXiv:2407.10832. A Purdue University mathematician contributes a latest tutorial chapter for an interdisciplinary audience which presents various approaches for qualitative data studies across a range of applications. See a prior paper by AV, Linking discrete and continuous models of cell birth and migration, at arXiv:2308.16093.

rom flocking birds to schooling fish, organisms interact to form collective dynamics across the natural world. Self-organization is present at smaller scales as well: cells interact and move during development to produce patterns in fish skin. For all these examples, scientists are interested in the individual behaviors informing spatial group dynamics and the patterns that will emerge due to agent interactions. A current issue is that models of self-organization are qualitative and need pattern data to include quantitative information. In this tutorial chapter, I survey some methods for quantifying self-organization, including order parameters, pair correlation functions, and techniques from topological data analysis. (Abstract)

Pattern formation driven by the interactions of agents is found across the natural and social world, spanning the population scale to the intracellular scale. Large-scale examples include pedestrian movements, honeybee aggregation, schooling fish, and marching locusts. In the domain of cells and tissues, neural-crest cell migration, color in fish skin, and wound healing are examples of self-organization. At smaller scales, proteins and filaments regulate transport and shape within cells. Studying such complex systems often leads to qualitative pattern data in the form of images. Being able to quantify these spatial data opens the door to a broader perspective on self-organization and makes complex systems more amenable to interdisciplinary investigation. (1)

Andrea V. was a programme participant in the Mathematics of Movement: an interdisciplinary approach to mutual challenges in animal ecology and cell biology (Google) symposium at the Isaac Newton Institute for Mathematical Sciences, Cambridge, autumn 2023. She gratefully acknowledges a travel grant from the Association for Women in Mathematics.

Madhanagopal, Bharath, et al. The unusual structural properties and potential biological relevance of switchback DNA. Nature Communications. 5/6636, 2024. A team of eight biogeneticists at SUNY Albany avail the latest instrumental methods and computational visualizations to come upon and illume a polar opposite version of the DNA helical coil. As a result, they can proceed with a retinue of novel properties.

Synthetic DNA motifs form the basis of nucleic acid nanotechnology. Here, we present a detailed characterization of switchback DNA, a globally left-handed structure composed of two parallel DNA strands. Compared to a conventional duplex, this form shows lower thermodynamic stability but exhibits enhanced biostability. Strand competition and strand displacement experiments show that component sequences have a preference for duplex complements. We hypothesize a potential role for switchback DNA as an alternate structure in sequences containing short tandem repeats which can open new avenues in biology and nanotechnology. (Excerpt)

In this work, we present a detailed characterization of a DNA motif called switchback DNA. Although the motif and its self-assembly into a lattice were recently reported, the biochemical and biophysical prop erties of this molecule are unknown. The impact of the unusual left-handed topology and parallel strand orientation on the physico-chemical properties of the motif is of potential interest in nucleic acid structure in general. We hypothesize that short tandem repeats may have the propensity to form switchback DNA as an alternate DNA structure and consider its potential role in biology and prospects in DNA nanotechnology. (2)

The RNA Institute at SUNY Albany is positioned to make significant contributions towards understanding the role of RNA in fundamental biological processes, developing RNA as a tool for science, and harnessing this knowledge to improve human health. The Institute brings together teams of researchers from multiple Departments and Universities with expertise in Biology, Bioinformatics, Chemistry, Engineering, Genetics, and Structural Biology.

, . Sala, Alba, et al. An integrated machine-learning model to predict nucleosome architecture. Nucleic Acids Research. 52/17, September 2024.. Nucleic Acids Research. 52/17, September, 2024. We cite this prime journal entry by seven bioresearchers at the Barcelona Institute of Science and Technology and Universitat de Barcelona including Modesto Orozco as another leading edge of an integral merger of frontier genetic studies and the latest AI neural net computational methods. See also Explainable AI Methods for Multi-Omics Analysis by Ahmad Hussein, et al at arXiv:2410.11910 for more current advances.

We demonstrate that nucleosomes placed in the gene body can be located from signal decay theory. These wave signals can be in phase or in antiphase We found that the first (+1) and the last (-last) nucleosomes are contiguous to regions signaled by transcription factor binding sites. Based on these analyses, we developed a method that combines Machine Learning and signal transmission theory which is able to predict the basal locations of the nucleosomes with an accuracy similar to that of experimental MNase-seq based methods. (Excerpt)

When we applied our model to the human genome, we obtained more accurate results than what would be expected from a random model. Considering the additional layers to deconvolute when studying more complex organisms and that the current model and architecture was optimized for yeast, our current methodology and experimental data, shows the potential to be used to study any nucleosome positioning array. Results presented here show the existence of a clear connection between expression level and the organization of nucleosome arrays, (11)

Karollus, Alexander, et al. Species-aware DNA language models capture regulatory elements and their evolution. Genome Biology.. Vol. 25/Art 83, 2024. In this BMC journal, Technical University of Munich geneticists introduce an effective synthesis of these premier nucleotide and narrative code-script domains. By so doing, a cross-assimilation is achieved of these biomolecular and linguistic text phases to an extent they can be seen as the same descriptive process in different sequential venues. See also How do Large Language Models understand Genes and Cells Chen Fang, et al in bioRxiv preprints for March 27, 2024 and Gene and RNA Editing at arXiv:2409.09057.

Large-scale multi-species genome sequencing promises to shed new light on gene regulatory instructions. To this end, algorithms are needed that can leverage conservation while accounting for their evolution. Here, we introduce species-aware DNA language models trained on 800 species spanning 500 million years of evolution. We show that DNA language models distinguish transcription factor and RNA-binding protein motifs from background non-coding sequence. These results show that species-aware DNA language models are a powerful, flexible, and scalable tool to integrate information from large compendia of highly diverged genomes. (Abstract)

A typical eukaryotic genome contains large regions of non-coding DNA. Tese are not translated into proteins but contain regulatory elements which control gene expression in response to environmental cues. Finding these regulatory elements and elucidating how their combinations and arrangements determine gene expression is a major goal of genomics research and is of great utility for synthetic biology and personalized medicine. (1)

ConclusionIn this study, we trained language models on the genomes of hundreds of fungal species, spanning more than 500 million years of evolution. We specifically directed our attention to non-coding regions, examining the ability of the models to acquire meaningful species-specific and shared regulatory attributes when trained on the genomes of many species. To our knowledge, we are the first to show that LMs are able to transfer these attributes to unseen species.

Zhao, Xiangyi, et al. Irreversibility in Bacterial Regulatory Networks. arXiv:2409.04513. In a paper to appear in Science Advances, Northwestern University and University of Texas Southwestern Medical Center researchers including Adilson Motter take advantage of a widest compass to achieve an exemplary affinity for microbial and genetic phases with deep physical phenomena. In regard, here is another 2024 instance which roots living personal systems a whole scale encoded universality.

Irreversibility, in which a transient perturbation leaves a system in a new state, is an emergent property in systems of interacting entities. This feature has well-established implications in statistical physics but remains underexplored in biological phases. Focusing on the regulatory network of Escherichia coli, we examine responses to transient single-gene perturbations and find that irreversibility increases with the proximity of the perturbed gene to positive circuits. (Excerpt)

A common goal in both statistical physics and systems biology is to connect the attributes of microscopic entities with observable macroscopic properties. Of particular interest are macroscopic properties that are emergent—including pattern formation and synchronization because they arise from interactions between system entities and can therefore enable new system-level functionality. In statistical physics, an important property is the irreversibility of macroscopic processes, where entropy—a state function—can increase despite the time-reversibility of the microscopic dynamics. (1)

Life's Corporeal Evolution Develops, Encodes and Organizes Itself: An Earthtwinian Genesis Synthesis

Duran-Nebreda, Salva, et al. Duran-Nebreda, Salva, et al. On the multiscale dynamics of punctuated evolution.. Trends in Ecology and Evolution. 39/8, 2024. Institut de Biologia Evolutiva, Barcelona, University of Tennessee, Vilnius University, Lithuania, American Museum of Natural History and Texas A&M University including Blai Vidiella, Sergi Valverde and Nils Eldredge himselfwho in 1972 along with Stephen Jay Gould formulated the original theory that life’s long developmental course seems to have proceeded by way of extended quiet periods interrupted by bursts of novel forms and properties. After a half century of wide and deep quantitative studies, this review indeed finds a broad semblance of such episodic patterns.

For five decades, paleontologists, paleobiologists, and ecologists have investigated patterns of punctuated equilibria in biology. Here, we step outside those fields and summarize recent advances in the theory of and evidence for this phenomena gathered from current observations in geology, molecular biology, genetics, anthropology, and sociotechnology. Altogether, our findings lead to a more general theory that we refer to as punctuated evolution. The quality of recent datasets support this expanded view in a way that can be modeled across a vast range from mass extinctions in ages past to the possible Anthropocene futures. (Abstract)

Prokopenko, Mikhail, et al. Biological arrow of time: Emergence of tangled information hierarchies and self-modelling dynamics. arXiv:2409.12029. By later 2024, ten coauthors from astrophysicists to computational biologists at the University of Sydney, ASU, University of Sussex, UCL, and Oxford including Paul Davies, Joseph Lizier, Geraint Lewis and Fernando Rosas can now post a comprehensive application of 21st century complex network systems theory to an equally expansive synthesis of life’s evolutionary emergence to be at last able to discern a central, orthogenesis-like course. In regard, the major sequential transitions scale provides a definitive, vectorial ascent mostly distinguished by relative genetic prescriptions and regnant individuality at each nested stage. See also On the roles of function and selection in evolving systems by Michael Wong, et al at PNAS (120/43, 2023) for another current intimation.

We study open-ended evolution by focusing on computational and information-processing dynamics underlying major evolutionary transitions. In doing so, we consider biological organisms as hierarchical dynamical systems that generate regularities in their phase-spaces through interactions with an environment. These emergent information patterns can then be encoded within an organism. Our main conjecture is that when macro-scale patterns continue on to micro-scale components, it creates tensions between what is encodable at an evolutionary stage and what may be realisable in the environment. This computation-theoretic argument can then be seen to trace a biological arrow of time. (Abstract excerpt)

In consideration of what the nested transitions have in common, they each involve major changes in individuality and how it perpetuates itself by novel inheritance modes of storing and transmitting information. Examples include replicating molecules which form cells as independent replicators, the DNA genetic code and proteins as enzymes; prokaryotes evolving into eukaryotes with a nucleus, and so on towards multicellularity and eusociality, as well as language and sociocultural evolution. (3)
Finally, we suggested that the biological arrow of time generates an oriented course of “information self-creation” by way of three canonical elements of computation: information preservation (memory/storage), information modification (processing), and information usage (communication). We propose that, in terms of dynamical systems, it is precisely this defined quality that forms a dimension for major evolutionary transitions along the biological arrow of time (23)

Slijepcevic, Predrag. Slijepcevic, Predrag.Principles of cognitive biology and the concept of biocivilisations... Biosystems. January, 2024. The Brunel University London biophilosopher provides an article synopsis of his 2023 book Biocivilisations: A New Look at the Science of Life. (London: Chelsea Green) which this abstract well conveys.

A range of recent studies promote the cognitive aspects of life: all organisms, from bacteria to mammals, are capable of sensing/perception, decision-making, problem-solving, learning, and other functions. In this paper I present a scientific and philosophical synthesis which is expressed through the four principles: (1) sentience and consciousness, (2) autopoiesis, (3) free energy and relational biology, and (4) behavioral repertoire. The principles reinforce themselves so that hierarchical and heterarchical shifts are widespread in the biosphere. As a result, I developed the concept of biocivilisations to identify and introduce a non-human social intelligence with equivalents of communication, engineering, science, medicine, art, and agriculture, in all kingdoms of life.

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