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Recent Additions: New and Updated Entries in the Past 60 Days
Displaying entries 61 through 75 of 115 found.


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

Cosmic Code > nonlinear > networks

Baptista, Anthony, et al. Mining higher-order triadic interactions. arXiv:2404.14997. Into this year, Queen Mary University of London, Alan Turing Institute, Central European University, Vienna, University of Southampton, UK, and Potsdam Institute for Climate Impact Research system theorists including Ginestra Bianconi, and Jurgen Kurths exemplify how vast and richly adorned nature’s network anatomy and physiology really is by still finding further multiplex dimensions.

Complex systems often present higher-order interactions which require us to go beyond their description in terms of pairwise networks. Triadic interactions are a fundamental type of higher-order interaction that occurs when one node regulates the interaction between two other nodes. Triadic interactions are a fundamental type of higher-order networks, found in a large variety of biological systems from neuron-glia to gene-regulation and ecosystems. In this article, a theoretical principle is used to model and mine this triune phase from node metadata, which is applied to Acute Myeloid Leukemia. Our work reveals higher-order properties which to advance our understanding of complex systems ranging from biology to the climate. (Excerpt)

Cosmic Code > nonlinear > networks

Pal, Kumar Palash, et al. Pal, Kumar Palash, et al. Global synchronizatin in generalized multilayer higher-order networks. arXiv:2406.03771. Indian Statistical Institute, Kolkata and University of Maribor, Slovenia system physicists including Matjaz Perc and Dibakar Ghosh continue to trace multiplex features and benefits of nature’s essential organismic anatomy and physiology as it becomes evident and suffuses from physical to societal interactive vitalities.

Networks incorporating higher-order interactions introduce novel dynamics into various processes such as synchronization. Here, we investigate these coordinations in multilayer networks beyond pairwise connections, both within and across layers. We demonstrate the existence of a stable global synchronous state resembling a master stability function. Our findings are supported by simulations using Hindmarsh-Rose neuronal and Rössler oscillators which illustrate how synchronization is facilitated by multiplex forms, over scenarios involving interactions within single layers. (Excerpt)

The study of complex networks has emerged as a prominent area of research. This interest growing arises from their capacity to model interconnected dynamical systems across many fields, such as physics, biology, ecology, social sciences, and engineering [1–3]. These networks are comprised of nodes, representing entities or elements, and links, representing connections or pairwise interactions between them. Many real-world systems can be conceptualized as multilayer networks include transportation networks [6], neuronal networks in the brain [7, 8], and various types of social networks [9]. A multilayer network consists of individual networks, each with its set of nodes and links (referred to as intralayer links), interconnected through interlayer links. The representation of multilayer networks hinges on a fundamental assumption: the complex connections among individuals within and across layers are comprehensively elucidated through pairwise links.

Cosmic Code > nonlinear > Algorithms

Miralavy, Iliya and Wolfgang Banzhaf. Spatial Artificial Chemistry Implementation of a Gene Regulatory Network Aimed at Generating Protein Concentration Dynamics. Artificial Life. 30/1, 2024. Michigan State University computer scientists provide a mid-2020s cross-integration between complex genomes, protein combinatorics and artificial chemical concepts (search WB). The paper is included in a special retrospective issue on the Artificial Life endeavor since the 1990s. See a lead essay What Is Artificial Life Today, and Where Should It Go? by Alan Dorin and Susan Stepney for more.

Gene regulatory networks are networks of interactions in organisms responsible for determining the production levels of proteins and peptides. Mathematical and computational models of gene regulatory networks have been proposed, some of them rather abstract and called artificial regulatory networks. In this contribution, a spatial model for gene regulatory networks is proposed that incorporates an artificial chemistry to realize the interaction between regulatory proteins called the transcription factors and the regulatory sites of simulated genes. The result is a system that is able to produce complex dynamics similar to those observed in nature. Here an analysis of the shows that such models are evolvable and can be directed toward desired protein dynamics. (Excerpt)

Cosmic Code > nonlinear > Rosetta Cosmos

Hartnett, Kevin. A Rosetta Stone for Mathematics. Quanta. May 6, 2024. A science writer recounts the work of the French mathematician Andre Weil (1906-1998) with regard to a letter he wrote in 1940 to his sister Simone Weil, a philosopher in London. As the quotes say, he drew upon the exemplary Rosetta stone as a basis for how various mathematical methods could have a deeper affinity with each other. The article goes on to add further instances such as the 1970 Langlands Program (See What Is the Langlands Program? by Alex Kontorovich in Quanta for June 1, 2022) and other esoteric modes all the way to 2020. As a presentation slide of mine (home page) entitled A Rosetta Cosmos sought to convey, a vital insight might be that every aspect of a human uniVerse can mirror and be translated into each other.

Following the example of the famous engraving by that same name — a trilingual text that made ancient Egyptian writing legible to Western readers through translation into Ancient Greek — Weil’s Rosetta stone linked three fields of mathematics: number theory, geometry, and, in the middle, the study of finite fields. Other mathematicians had proposed ideas in this direction, but Weil was the first to spell out an exact vision. His letter presaged the Langlands program, a major initiative in contemporary mathematical research.

Cosmic Code > nonlinear > 2015 universal

Cai, Chao-Ran, et al. Epidemic criticality in temporal networks. Physics Reviews Research. 6/L022017 April, 2024. Northwest University, China, Shaanxi Key Laboratory for Physics Frontiers, Xi’an, China and Aalto University, Espoo, Finland (Petter Holme) theorists discern the deep presence of self-organized critical transitions even in public phenomena such as disease vectors amongst diverse, many body populations.

Analytical studies of network epidemiology often focus on the extreme situations where the timescales of network dynamics are well separated (longer or shorter) from that of propagation. In realistic scenarios, however, these timescales could be similar, which has profound implications for modeling. Combining Monte Carlo simulations and mean-field theory, we analyze the behavior of susceptible-infected epidemics in the vicinity of the critical threshold of temporal networks. Dynamic correlations from being close to infected nodes increases the likelihood of infection and drive the state in the opposite direction. (Excerpt)

Cosmic Code > nonlinear > Common Code

Lynn, Christopher, et al. Heavy-tailed neuronal connectivity arises from Hebbian self-organization. Nature Physics. January 2024, . Into this year, CCNY, Princeton and University of Chicago neuroscientists are able to report a constant cerebral topology which appears constant across mammalian species. As a result their next insight is to attribute this genomic commonality to the presence of universal, independent self-organizing forces.

The connections in networks of neurons are heavy-tailed, with a small number of connected more strongly. However, it remains unclear whether these patterns emerge from underlying mechanisms. Here we propose a minimal model of synaptic self-organization wherein links are pruned and synaptic strength rearranges by a mixture of preferential and random dynamics. Under these generic rules, networks evolve to produce connectivities that are scale-free with a power-law exponent. We confirm these predictions in the connectomes of several animals, suggesting that this cerebral phenomena may arise from general principles of network self-organization rather than mechanisms specific to individual species or systems.
Together, the results and models developed here demonstrate how two fundamental features of connectomes—heavy–tailed connectivity and clustering—can arise from simple network dynamics, providing a framework for future investigations into the self–organization of neuronal connectivity. (13)

Cosmic Code > nonlinear > Common Code

Sowinski, Damian, et al. Information-theoretic description of a feedback-control Kuramoto model. arXiv:2404.02221. University of Rochester physicists including Adam Frank propose an innovative synthesis of combinatorial methods with states of more or less orderly oscillations as novel way to root living systems in physical principles. See also Semantic Information in a model of Resource Gathering Agents by this group along with Marcelo Gleiser and Artemy Kolchinsky in PRX Life (1/023003, 2024).

Semantic Information Theory (SIT) offers a new approach to evaluating the information architecture of complex systems. In this study we describe its application to dynamical problems by four steps: (1) separate the system into agent-environment sub-systems; (2) choose appropriate coarse graining and quantifying correlations; (3) identify a measure of viability; and (4) implement a protocol to measure the semantic content. We study the neural dynamics of epileptic seizures whereby an agent tries to control a base environment in a desynchronized state through Kuramoto phase synchronization. The oscillation structure for agent and environment allows us to apply a computational perspective, where the agent-environment dynamics can be become a communication channel. (Excerpt)

Cosmic Code > Genetic Info > Paleo/Cosmo

Barrie, William, et al. Elevated genetic risk for multiple sclerosis emerged paleo-genetic in steppe pastoralist populations. Nature. 625/321, 2024. We cite this entry to log in papers in this issue about the latest DNA recovery achievements, mainly in northern Europe. The team work was mainly done by GeoGenetics Centre, University of Copenhagen and GeoGenetics Group, University of Cambridge (Eske Willerslev) and at the University of Bremen. We note The selection landscape and genetic legacy of ancient Eurasians by Evan Irving-Pease, et al (625/312), 100 ancient genomes show repeated population turnovers in Neolithic Denmark by Morten Allentoft, et al (625/329) and Population genomics of post-glacial western Eurasia by Morten Allentoft, et al (625/301). See also A 2-million-year-old ecosystem in Greenland uncovered by environmental DNA by Kurt Kjaer, et al in Nature (612/283, 2022).

This breakthrough project was profiled on a PBS program Hunt for the Oldest DNA (pbs.org/video/hunt-for-the-oldest-dna-zckys0/) shown in February 2024. For decades, scientists have tried to unlock the secrets of ancient DNA. Follow the dramatic quest to recover DNA millions of years old and reveal a lost world from before the last Ice Age. So once again we wonder for what reason does an ecosmic genesis evolves a planetary intelligence which can then proceed to recreate how we peoples all came to be.

Late Pliocene and Early Pleistocene epochs 3.6 to 0.8 million years ago had climates resembling those now forecast under future warming. The biological communities inhabiting the Arctic during this time remain poorly known. Here we report an ancient environmental DNA (eDNA) record of the rich plants and animals of the Kap København Formation in North Greenland. An open boreal forest ecosystem with mixed vegetation of poplar, birch and thuja trees, and shrubs were found, along with mastodons, reindeer, rodents and geese. Across four papers in this week’s issue, Eske Willerslev and colleagues use genetic data from ancient Eurasians to probe the effects of cross-continental migrations on prehistoric peoples. The researchers detail genetic changes due to the mixing of ancient steppe, farming and hunter-gatherer populations. In particular, they identify that these movements brought an elevated genetic risk for multiple sclerosis to Europe. (Editorial: Step by Steppe)

Cosmic Code > Genetic Info > DNA word

Abramson, Josh, et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature. May 8, 2024. Some fifty computational biologists at Google Deepmind, London and Isomorphic Labs, London led by John Jumper introduce a latest, much advanced edition of this Protein Structure Database capability which first came out in mid 2021. As the quotes say, major AI deep machine neural learning advances have now fostered capabilities not possible earlier. Among many news reports see Google Unveils A.I. for Predicting Behavior of Human Molecules by Cade Metz in the NY Times (May 8, 2024).

In this paper, we describe our latest AlphaFold 3 model with an updated diffusion-based architecture which can predict complex structures including proteins, nucleic acids, small molecules, ions, and modified residues. The new version has improved analytic accuracy for protein-ligand interactions, protein-nucleic acids, and higher antibody-antigen predictability. Together these results achieve a revolutionary stage of precise modelling across biomolecular space within a single unified deep learning framework. (Abstract)

The development of bottom-up modelling of cellular components is a key step in unravelling the complexity of molecular regulation within the cell, and the performance of AlphaFold 3 shows that the right deep learning frameworks can reduce the amount of data required to obtain relevant performance. We expect that structural modelling will continue to improve not only due to deep learnings but also by cryo electron microscopy and tomography. The parallel advance of experimental and computational methods promise an era of structurally informed biological understanding and therapeutic development. (10)

Cosmic Code > Genetic Info > DNA word

Fang, Jing-Kai, et al. Divide-and-Conquer Quantum Algorithm for Hybrid de novo Genome Assembly of Short and Long Reads. PRX Life. 2/023006, 2024. We note this contribution by BGI Research, Shenzhen, China computational geneticists as a frontier instance of how genetic studies are being taken to a new dimension by virtue of quantum capabilities. The evidential result implies that life’s implicate genomic proscription can gain an affinity with this fundamental physical ground.

Researchers have begun to apply quantum computing in genome assembly implementation, but the issue of repetitive sequences remains unresolved. Here, we propose a hybrid assembly quantum algorithm using short reads and long reads which utilizes divide-and-conquer strategies to approximate the ground state of a larger Hamiltonian while conserving quantum resources. The convergence speed is improved via the problem-inspired Ansatz based on the known information. In addition, we verify that entanglement within quantum circuits may accelerate the assembly path optimization. (Excerpt)

Cosmic Code > Genetic Info > DNA word

Hwang, Yunha, et al. Genomic language model predicts protein co-regulation and function. Nature Communications.. 15/2880, 2024. We enter this work by Cornell, Harvard, Johns Hopkins, and MIT biologists including Sergey Ovchinnikov as another literate version of the textual affinity of nucleotides and narratives. See also ProteinEngine: Empower LLM with Domain Knowledge for Protein Engineering at arXiv:2405.06658.


Deciphering the relationship between a gene and its genomic context is vital to understand and modify biological systems. Machine learning can study the sequence-structure-function paradigm but higher order genomic information remains elusive. Evolutionary processes dictate genomic contexts in which a gene occurs across phylogenetic distances, and these emergent patterns can be leveraged to uncover functional relationships. Here, we train a genomic language model (gLM) on metagenomic scaffolds to uncover regulatory relationships between genes. Our findings illustrate that gLM’s deep learning of metagenomes is an effective approach to encode the semantics and syntax of genes and uncover complex relationships in a genomic region. (Abstract)

The unprecedented amount and diversity of metagenomic data presents opportunities to learn hidden patterns and structures of biological systems. With larger amounts of data, these models can disentangle the complexity of organismal genomes and their encoded functions. The work presented here validates the concept of genomic language modeling. Our implementation of the masked genomic language modeling illustrates the feasibility of training such a model, and provides evidence that biologically meaningful information is being captured in learned contextualized embeddings. (9)

Cosmic Code > Genetic Info > Genome CS

Fang, Jing-Kai, et al. Divide-and-Conquer Quantum Algorithm for Hybrid de novo Genome Assembly of Short and Long Reads.. PRX Life. 2/023006, 2024. We note this contribution by BGI Research, Shenzhen, China computational geneticists as a frontier example of how genetic studies are being taken to a new dimension by virtue of quantum capabilities. An evidential result implies that life’s genomic proscription can gain an affinity with this fundamental physical phase.

Researchers have begun to apply quantum computing in genome assembly implementation, but the issue of repetitive sequences remains unresolved. Here, we propose a hybrid assembly quantum algorithm using short reads and long reads which utilizes divide-and-conquer strategies to approximate the ground state of a larger Hamiltonian while conserving quantum resources. The convergence speed is improved via the problem-inspired Ansatz based on the known information. In addition, we verify that entanglement within quantum circuits may accelerate the assembly path optimization. (Excerpt)

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

Quickening Evolution

MacIver, Malcolm and Barbara Finlay. The neuroecology of the water-to-land transition and the evolution of the vertebrate brain. Philosophical Transactions of the Royal Society B. December, 2021. Veteran Northwestern University and Cornell University evolutionary neuroscientists make a case that this epochal movement of aquatic creatures onto dry, sunlit surfaces played a much more paramount role in life’s emergence than previously seen.

The water-to-land transition in vertebrate evolution offers a unique opportunity for computational affordances and a new ecology for the brain. As a result, a much enlarged visual sensorium owing to air versus water as a medium, then led to mobile eyes and neck. In water, the midbrain tectum coordinates approach/avoid decisions, due to water flow and the bodily state and learning. On land, the relative motions of sensory surfaces and effectors must be resolved, adding on computational architectures from the dorsal pallium. For the large-brained and long-living denizens, making the right decision allows animals to learn from experience. Integration of memorized panoramas in the basal ganglia/frontal cortex becomes a substantial cognitive habit-to-plan benefit. (Excerpt)

Quickening Evolution > Systems Biology

Linden-Santangeli, Nathaniel, et al. Increasing certainty in systems biology models using Bayesian multimodel inference.. Linden-Santangeli, Nathaniel, et al. Increasing certainty in systems biology models using Bayesian multimodel inference. arXiv:2406.11178.. UC San Diego bioscientists show how to integrate this popular research procedure with an holistic sense of metabolic vitalities.

Mathematical models are a good way to study the structure and behavior of intracellular signaling networks. As a result, the same signal pathway can be represented by multiple models, each with underlying assumptions. Here, we use Bayesian inference to develop a way to achieve increasing certainty. A case study of extracellular regulated kinase (ERK), we show that multimodel inference enhances predictive accuracy. Finally, we use multimodel inference to explain sub-cellular location-specific ERK activity dynamics. (Excerpt)

Quickening Evolution > Nest > Life Origin

Pérez-Mercader., Juan. Making Biochemistry-Free Life in a Test Tube.. Di Mauro, Ernesto, ed.. The First Steps of Life. Wiley Online, 2023. In this chapter, a senior scientist (see below) now at Origins describes evidential results which may well imply an intrinsic organism-like ecosmic fertility. See also Competitive exclusion principle among synthetic non-biochemical protocells by Sai Krishna Katla, et al (J P-M) in Cell Reports Physical Science (4/4, 2024).


As we discover many extrasolar planets it is time to ask: Is biochemistry-based life the only chemical support for life? On Earth, all living systems (i) process information, (ii) metabolize, (iii) self-reproduce and (iv) evolve. But can processes (i)-(iv) take place in a non-biochemical chemical system? We present progress resulting from experiments on a reaction during the non-equilibrium synthesis of functional polymer vesicles from small, non-biochemical molecules. Their dynamical evolution integrates metabolism, growth, reproduction, and descent with modification by implementing a polymerization induced self-assembly (PISA) scenario. Together, these results offer insights into generic chemistry-based artificial life, as well as into the origin of proto-cells enroute to proto-life and pre-LUCA living systems. (Abstract)

Juan Pérez-Mercader earned his Ph.D. from the City College of New York. In 1998 he founded Spain's Centro de Astrobiología as its first Director. In 2010, he joined Harvard in the Earth and Planetary Sciences and Origins of Life Initiative, where he leads a project on the "Top-down Synthesis of an Ex-novo Chemical Artificial Living System". Some of his Research Interests are Physics of Self-organizing Behavior, Information in Non-equilibrium, Physico-chemical Systems, Chemical Computation, Quantum Field Theory, and Dynamical Renormalization Group,

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