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Recent Additions: New and Updated Entries in the Past 60 Days
Displaying entries 31 through 45 of 82 found.


Ecosmomics: Independent, UniVersal, Complex Network Systems and a Genetic Code-Script Source

Cosmic Code

Cepelewicz, Jordana. The Quest to Decode the Mandelbrot Set, Math’s Famed Fractal. Quanta. January 27, 2024. A science reporter writew a luminous review about the whole history of such fractional theories from Benoit and his predecessors to their present state. Its focal occasion was a 2023 conference in Denmark to guide this now global project going forward. The article covers two main themes with vignettes of mathematicians who have spent lifetimes plumbing the endless depths of these equations. At the center are Misha Lyubich and Dima Dudko from SUNY Stony Brook whose years go back to Moscow, Ukraine and Belarus. We also meet Jeremy Kahn, Mitsuhiro Shishikura. Wolf Jung, Arnaud Chéritat of the University of Toulouse, Carsten Petersen of Roskilde University who chaired the meeting, Christophe Yoccoz, and more. The other half is a succinct tutorial all about fractal geometries with vivid graphs and images.

See, for example, MLC at Feigenbaum points by Dudko and Lyubich at arXiv:2309.02107 on universal renormalizations. I heard Benoit speak in the 1990s at Boston University. Afterwards I asked him if a hierarchy aspect might apply to fractals. He replied, rather archly, I have no use for that word. Of course he is right because these infinite iterations are not a linear ladder but nature’s organic, self-similarity as it springs from an essential genome-like code-script. (In my 2004 talk on the home page I mused that Thomas Aquinas’ “analog of proper proportion” phrase would be an apt description.) But as 2024 is taken over by incendiary conflicts, however might such mathematical geometries, as Galileo once advised, ever become actually realized and availed?

Use a computer to zoom in on the Mandelbrot set’s jagged boundary, and you’ll encounter seahorses, parades of elephants, spiral galaxies and neuron-like filaments. No matter how deep you explore, you’ll always see near-copies. That endless complexity was a core element of James Gleick’s 1987 book Chaos. The Mandelbrot set had become a symbol and represented the need for a new mathematical language, a better way to describe the fractal nature of the world. It illustrated how profound intricacy can emerge from the simplest of rules — much like life itself with relative order and disorder. (JC)

Cosmic Code

Jensen, Henrik. Complexity Science: The Study of Emergence. Cambridge. UK: Cambridge University Press, 2023.. Cambridge. UK: Cambridge University Press, 2023. The Imperial College London mathematician (search) writes a latest comprehensive textbook for this nascent 21st century study of our actual lively, anatomic, physiological, procreativity. Its contents course from first theoretic principles to statistical mechanics, networks, information, much more and onto critical transitions and tipping points.

Cosmic Code

Rosas, Fernando, et al.. Software in the natural world: A computational approach to emergence in complex multi-level systems. arXiv:2402.09090. University of Sussex, Imperial College London (Pedro Mediano), Graz University of Technology, Austria, McGill University (Anil Seth), University of Hertfordshire, and EPFL, Lausanne propose to cross-combine nonlinear complexity phenomena with mathematical program procedures as a beneficial way to achieve a complete, effective integration.


Understanding the functional architecture of complex systems is crucial to reveal their inner workings and enable prediction and control. Here we develop a computational approach to study emergent macroscopic processes by way of a mathematical formalism that can express self-contained informational, interventional properties. Our method forms a hierarchy of nested self-contained processes from the statistical physics and computational neuroscience literature wherein holistic processes are akin to software-like. Overall, this framework enables a deeper understanding of multi-level complex systems so they can be better simulated, predicted, and controlled. (Abstract edit)

Cosmic Code > nonlinear > networks

Lalli, Margherita and Diego Gariaschelli. Geometry-free renormalization of directed networks: scale-invariance and reciprocity. arXiv:2403.00235. IMT School for Advanced Studies, Lucca, Italy physicists are able to demonstrate an effective integrity of this physical attribute with multiplex phenomena across diverse, practical instances. See also Renormalization of Complex Networks with Partition Functions by Jung, Sungwon, et al at arXiv:2403.07402

Recent research has tried to extend the concept of renormalization to more general networks with arbitrary topology. Here we show that the Scale-Invariant Model can be extended to directed networks without an embedding geometry or Laplacian structure. Moreover, it can account for the tendency of links to occur in mutual pairs more or less often than predicted by chance. By way of renormalization rules, we propose a multiscale international trade network with nontrivial reciprocity and an annealed model where positive reciprocity emerges spontaneously. (Excerpt)

Cosmic Code > nonlinear > networks

Vidal-Saez, Maria, et al. Biological computation through recurrence.. arXiv:2402.05243. As scientific “megatrends” proceed apace, Universitat Pompeu Fabra and Universitat Autònoma de Barcelona biologists propose a further finesse of multiplex network capabilities as a better way to study and explain how organisms are able to get along and survive.

One of the defining features of living systems is their adaptability to changing environmental conditions. This requires organisms to extract temporal and spatial informative features to compute the appropriate response. A growing body of work from machine learning and neuroscience has shown that such complex information processing can be performed by recurrent networks from interactions between incoming stimuli and internal dynamics. Here we review understandings of how recurrent networks are used by biological systems from cells to brains for this purpose. We focus on simpler networks and learning algorithms that have been found by evolution. We go on to discuss some relevant aspects concerning the emergence of this natural computation paradigm. (Abstract)

Living organisms are faced with the ongoing challenge of processing signals coming from their environment. The dynamical nature of recurrent networks allows the integration of the present conditions faced by the organism with its (recent) past, making the concept of recurrence-based computation an initial step towards a conceptual model for temporal information processing in living beings. The synergistic merger between applied mathematics, neuroscience and machine learning in this review can provide us with a general principle of biological information processing that has so far been elusive. (13-14)

Cosmic Code > nonlinear > Algorithms

Le Verge-Serandour, Mathieu and Karen Alim.. Le Verge-Serandour, Mathieu and Karen Alim. Physarum polycephalum: Smart Network Adaptation. Annual Review of Condensed Matter Physics. Volume 15, 2024. Center for Protein Assemblies; Technical University of Munich biophysicists provide a network neuroscience review of this cellular invertebrate whom is able to exhibit an advanced behavioral responses. Once again, an early, deep insistence of intelligent agency is evident as if a universal repertoire to access.

Life evolved organisms to adapt to their environment and autonomously exhibit behaviours. While complex behaviours are associated with the capability of neurons to process information, the unicellular organism Physarum polycephalum is able to solve complex tasks despite being a single cell shaped into a tubular network. In Physarum, smart behaviours arise as network tubes grow or shrink due to coupling, fluid flows and transport. From our physicist's perspective, we introduce the biology and active chemo-mechanics of this living matter entity (Abstract)

Physarum polycephalum, a giant single-cell slime mould, fascinates researchers with its sophisticated behaviour despite its simple build. The network-shaped body of Physarum plasmodia tops typical cell size reaching up to meters in length while enclosing thousands even millions of nuclei, which allows for complex behaviour similar to multi-cellular organisms. Here is a life form that beat the odds of 600 million years of evolution to thrive on earth today and combine traits of what later on became animals, plants and fungi in itself.

Cosmic Code > nonlinear > Rosetta Cosmos

Melko, Roger and Juan Carrasquilla.. Language models for quantum simulation. Nature Computational Science. 4/1, 2024. University of Waterloo, Ontario theorists (search each) consider the latest cross-integrations of these widely separated natural and social realms which then increasingly appear to have an innate, common affinity. As Earth continues to learn into this year, the real presence of an actual recursive narrative from uniVerse to US gains a deep veracity.

A key challenge in the effort to simulate today’s quantum computing devices is the ability to learn and encode the complex correlations that occur between qubits. Emerging technologies based on language models adopted from machine learning have shown unique abilities to learn quantum states. We highlight the contributions that language models are making in the effort to build quantum computers and discuss their future role in the race to quantum advantage.

Cosmic Code > Genetic Info > Paleo/Cosmo

Chisholm, Lauren, et al. Ancestral Reconstruction and the Evolution of Protein Energy Landscapes. .. Annual Review of Biophysics.. Volume 53, 2024. University of Oregon paleogeneticists cast a unique retrospective, only possible just now, back to primordial protein biomolecules as a prior reference for current novel formulations.

A protein's sequence determines its conformational energy landscape, and in turn, the protein's function. Understanding the evolution of new proteins involves how mutations alter their energy landscape. Ancestral sequence reconstruction (ASR) has proven a valuable tool whereby one phylogenetically infers the sequences of ancient proteins to characterize their properties. When coupled to biophysical, biochemical, and functional aspects, ASR can reveal how historical mutations allowed the evolution of enzyme activity, altered conformations, binding specificity, oligomerization, and other features. (Abstract).

Cosmic Code > Genetic Info > DNA word

Heckmeier, Philipp, et al.. A billion years of evolution manifest in nanosecond protein dynamics. PNAS. 121/10, 2024. We cite this paper by University of Zurich and Columbia University biochemists as an example of how far the scope and range of these current techniques can reach. And again who are we peoples with an Earthomo sapience to be able to look down and back and reconstruct and re-present how it all came to occur?

Protein dynamics forms a broad bridge between structure and function, yet the impact of evolution on ultrafast protein processes remains enigmatic. This study delves into the nanosecond-scale phenomena of a conserved protein across species separated by almost a billion years as a way to investigate ten complex homologs. In so doing, we found a cascade of rearrangements which manifest in discrete time points over hundreds of millions of years. Our work poses a novel scientific inquiry within molecular paleontology compared by the rapid pace of protein processes which can connect the shortest time scale in living matter (10^-9 s) with the largest ones (10^16 s). (Abstract)

Cosmic Code > Genetic Info > DNA word

Maggi, Luca. The main role of fractal-like nature of conformational space in subdiffusion in protein. arXiv:2306.07825. A Barcelona Institute of Science and Technology bioinformatics disease mechanism researcher provides a latest report of how vital self-similarities appear to suffuse their metabolic activities. See also The Evolution of Fractal Protein Modules in Multicellular Development by Harry Booth and Peter Bentley in Artificial Life Conference Proceedings (MIT Press 2022).

Protein dynamics studies their biological functions but a theoretical picture of their relevant features is still missing. For example, a prime property exhibited by this dynamic is its subdiffusivity. Here, by comparing all-atom molecular simulations and theory we show that this behavior arises from the fractal network of the network of metastable conformational states over which protein diffusion processes take place. (Excerpt)

Cosmic Code > Genetic Info > DNA word

Outeiral, Carlos and Charlotte Deane. Codon language embeddings provide strong signals for use in protein engineering.. Nature Machine Intelligence. 6/2, 2024. We enter this note by Oxford University biostatisticians because it treats this metabolic regime as if it can be typically parsed by various grammatical methods.

Protein representations from deep language models have achieved good performance in computational protein studies surpassing the datasets they were trained on. But here we propose an alternative direction. We show that LLMs trained on codons, instead of amino acid sequences, provide high-quality results that outperform across a variety of tasks. For species recognition, prediction of protein and transcript abundance or melting point estimation, we show that a codon language surpasses every other published version. This topical shift indicates that the information content of biological data provides an orthogonal direction to expand the utility of machine learning in biology. (Excerpt)

Cosmic Code > Genetic Info > DNA word

Sondka, Zbyslaw, et al.. COSMIC: a curated database of somatic variants and clinical data for cancer.. Nucleic Acids Research. 52/D1, 2024. Wellcome Sanger Institute geneticists describe the latest four year version of their extensive, actively used informational resource for treating this malady.


The Catalogue Of Somatic Mutations In Cancer (COSMIC), https://cancer.sanger.ac.uk/cosmic, is an expert-curated knowledgebase providing data on somatic variants in cancer, supported by a comprehensive suite of tools for interpreting genomic data, discerning the impact of somatic alterations on disease, and facilitating translational research. Within the last 4 years, COSMIC has substantially expanded its utility by adding new resources: the Mutational Signatures catalogue, the Cancer Mutation Census, and Actionability.

Data curation is the organization and integration of data collected from various sources. It involves annotation, publication and presentation of the data so that the value of the data is maintained over time, and the data remains available for reuse and preservation. In science, data curation may indicate the process of extraction of important information from scientific texts, such as research articles to be converted into an electronic format.

Cosmic Code > Genetic Info > DNA word

Wu, Fang, et al. Integration of pre-trained protein language models into geometric deep learning networks. Communications Biology. 6/876, 2023. Westlake University, Hangzhou, China, Yale University, and Tsinghua University, Beijing computational biologists provide another example of this frontier cross-adoption of protein linguistics with AI neural net contents. Our comment for these contributions is that as genetic and metabolic processes are able to be grammatically parsed, so to say, they gain a common textual basis. As a result, a wide and deep natural narrative is being realized in our midst written in an ecosmome to geonome code script. See also ProtLLM: An Interleaved Protein-Language LLM with Protein-as-Word Pre-Training by Le Zhuo, et al at arXiv:2403.07920 for more work in this regard.

Geometric deep learning has achieved much success in defining 3D structures of large biomolecules. Meanwhile, protein language models trained on 1D sequences apply to a broad range of applications. In this work, we integrate the knowledge learned by protein language models into geometric networks and evaluate a variety of protein representation learning benchmarks. The incorporation of protein language knowledge enhances geometric networks’ capacity and can be generalized to complex tasks. (Excerpt)

Cosmic Code > Genetic Info > DNA word

Zambon, A., et al. Structure of the space of folding protein sequences defined by large language models. Physical Biology. January, 2024. We cite this entry by Center for Complexity and Biosystems, University of Milan researchers as another instance of this mid 2020s cross-integrity of metabolic methods with AI computational network capabilities.

Proteins populate a sequence space whose geometrical structure guides their natural evolution. By way of transformer models, we examine the protein landscape as an effective energy of sequence foldability, an approach similar to optimization methods in machine learning. We then employ statistical mechanics algorithm to explore regions with high local entropy in relatively flat landscapes. Our work thus combines machine learning and statistical physics so to provide new insights into the exploration of sequence landscapes where wide, flat minima coexist alongside narrower minima. (Excerpt)

Cosmic Code > Genetic Info > Genome CS

Ghorbani, Mahboobeh, et al. Gene Expression Is Not Random: Scaling, Long-Range Cross-Dependence, and Fractal Characteristics of Gene Regulatory Network.. Frontiers in Physiology. October, 2018. University of Southern California system theorists including Paul Bogdan describe how of a self-similar topology is evident in complex genomes.

Understanding the dynamics of gene expression is crucial to unraveling the physical complexities of this process. Here, we report the scaling properties of gene expression time series in Escherichia coli. Our results demonstrate that the gene expression time series display fractal and long-range dependence characteristics. The interplay between genes and transcription factors in regulatory networks are also fractal and cross-correlated. (Excerpt)

Lastly, mathematical and analytical investigation of the relation between structure and dynamics of processes are also fundamental in theory. Answering to the question of how long-range dependency transfers between structure and dynamics and how the degree of fractality/multifractality of structure and dynamics are like each other would have a huge impact on predicting the behavior of complex systems.

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