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Displaying entries 31 through 45 of 64 found.

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

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)

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)

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)

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)

Xiao, Yi, et al. Bridging Text and Molecule: A Survey on Multimodal Frameworks for Molecules. arXiv:2403.13830. Chinese Academy of Sciences AI researchers provide an example of how readily language-based content can be assimilated by computational methods as they are then employed to parse protein linguistics. Altogether a common natural narrative from nucleotides to nouns is being read and written anew,

With recent trend in machine learning and natural language processing is aimed at building multimodal frameworks to jointly model molecules with textual domain knowledge. In this paper, we present the first systematic survey of this integrative endeavor. We focus on advances in text-molecule alignment methods, categorizing current models into two groups based on their architectures and listing relevant pre-training tasks. We next delve into the utilization of large language models and prompting techniques for molecular tasks and present significant applications in drug discovery. (Excerpt)

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)

Wall, Brydon, et al. Machine and deep learning methods for predicting 3D genome organization. arXiv:2403.03231. We cite this entry by Virginia Commonwealth University computational physicians as an example of how current neural net Ai methods, which have already taken over protein research, can similarly apply to and enhance complex genetic studies. Altogether life’s whole organismic realm continues to gain a deeply common textual essence.

Three-Dimensional (3D) chromatin interactions, such as enhancer-promoter interactions (EPIs), loops, Topologically Associating Domains (TADs), and A/B compartments play vital roles in cellular processes by regulating gene expression. However, current catalogs of 3D structures remain incomplete due to low data resolution. Machine learning methods can be an alternative to obtain more interactions and improve resolution. In this review, we discuss computational tools for predicting three types of 3D interactions (EPIs, chromatin interactions, TAD boundaries) and suggest future research directions.

Zhang, Yang, et al. Computational methods for analysing multiscale 3D genome organization.. Nature Reviews Genetics. 25/3, 2024. We note this report by Carnegie Mellon, NIH, and UCLA geneticists including Tom Misteli at the frontier of this amenable intersection of AI neural net methods with complex genomic forms and functions. Altogether it seems that a common nonlinear narrative, an original literacy from cerebral to ecosmic connectomes, is deftly being deciphered and translated.

Recent progress in whole-genome mapping and imaging technologies has illuminated the spatial organization and folding in of the nucleus. In parallel, advanced computations have revealed multiscale (3D) transcription features. Here, we discuss how machine-learning methods and integrative frameworks, have led to a systematic delineation of genomic and epigenomic features, nuclear components and connective function. However, approaches to scan a wide variety of genomic and imaging datasets are still needed to define cellular phenotypes in health and disease. (Excerpt)

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

Conway Morris, Simon. From Extraterrestrials to Animal Minds: Six Myths of Evolution.. Conshohocken, PA: Templeton Press, 2022. The Cambridge University emeritus paleontologist continues his strong views by which to set aside vested tenets of the neoDarwinian corpus. To wit, there are indeed constrained limits to anatomy and physiology, evolution does proceed along a defined course rather than exhibit blind randomness. Here is the deep difference. SCM long had running debate with Stephen Jay Gould whence each would read the same phenomena as either orderly or random by way of personal persuasion. With regard to the sixth issue of microbial to advanced life in the galactic cosmos, it is concluded the stellar environs are so harsh that most probably we Earthlings, albeit fantastic beings, are most likely uniquely alone.

In this learned romp of science writing, Simon Conway Morris challenges old assumptions that pass as truths amongst the evolutionary orthodox. Life’s onward course is not aimless but highly circumscribed and “seeded with inevitabilities.” Turning from fossils to minds, Conway Morris questions whether the intelligence of humans and animals is similar by a difference of degree. Finally, the existence of other habitable worlds is faced whence the size and scale of the universe suggest that alien beings must exist somewhere. But the author Conway Morris cites the Fermi Paradox (“Where are they?”) to conclude that we alone and unique in the cosmos.

Couce, Alejandro, et al. Changing fitness effects of mutations through long-term bacterial evolution.. Science. January, 2024. Michigan State University, Harvard Medica School and University of Paris biologists including Richard Lenski post a summary report for some years of laboratory test runs of computational organisms and their genetics code. As the Abstract says, and team interviews affirm, a general reliability does become evident.

The distribution of fitness effects of new mutations shapes evolution, but it is a challenge to observe how it changes as organisms adapt. Using Escherichia coli lineages spanning 50,000 evolutionary generations, we quantify the fitness effects of insertion mutations in every gene. Microscopically, changes in individual gene essentiality and deleterious effects often occurred in parallel. The identity and effect sizes of beneficial mutations changed rapidly over time, but many targets of selection remained predictable because of loss-of-function mutations. Taken together, these results reveal the dynamic—but statistically predictable—nature of mutational fitness effects.

Daryakenari, Nazanin, et al.. AI-Aristotle: A physics-informed framework for systems biology gray-box identification.. PLoS Computational Biology. February, 2024. We cite this work by Brown University mathematicians for its leading edge use of computational methods with a physical basis for improved quantifications of active natural phenomena. Into the 2020s, a spiral synthesis like this can define a global integrative phase of scientific endeavors.

Discovering mathematical equations that govern physical and biological systems is a fundamental challenge in scientific research. We present a new physics-informed framework for parameter estimation in the field of Systems Biology. The proposed framework—named AI-Aristotle—combines the eXtreme Theory of Functional Connections (X-TFC) domain-decomposition and Physics-Informed Neural Networks (PINNs) with symbolic regression (SR).. To test the performance of AI-Aristotle, we use sparse synthetic data perturbed by uniformly distributed noise. More broadly, our work provides insights into the accuracy, cost, scalability, and robustness of integrating neural networks with symbolic regressors, offering a comprehensive guide for researchers tackling gray-box identification challenges in complex dynamical systems in biomedicine and beyond. (Excerpt)

Fairchild, Jaspar, et al. Prebiotically plausible chemoselective pantetheine synthesis in water. Science. 383/911, 2024. In a paper that made science news, University College London biochemists including Matthew Powner report that they were able to explain how this unique intermediary compound came into existence on cue so as to complement a vital biochemical regimen so that protocellular metabolisms could proceed on their lively way.

Coenzyme A (CoA) is essential to life and its functional subunit, pantetheine, is vital to origin-of-life scenarios, but how pantetheine (a cysteamine amide analog of pantothenic acid = vitamin B5) emerged on the early Earth remains a mystery. In this work, we report high-yielding and selective prebiotic syntheses of pantetheine in water. Chemoselective multicomponent aldol, iminolactone, and aminonitrile reactions delivered spontaneous differentiation of pantoic acid and proteinogenic amino acid syntheses. Our results are consistent with a role for canonical pantetheine at the outset of life on Earth. (Excerpt)

Papastavrou, Nikolaos, et al.. RNA-catalyzed evolution of catalytic RNA. PNAS. 121/11, 2024. Salk Institute of Biological Studies geneticists including its director Gerald Joyce are now able to discern a pathway by which this crucial nucleotide molecule could shape up, have the necessary capacities so as to propel living systems going on their evolutionary way. See also Prebiotic Astrochemistry from Astronomical Observations and Laboratory Spectroscopy by Lucy Ziurys in the Annual Review of Physical Chemistry (Volume 75, 2024.)

An RNA polymerase ribozyme obtained by directed evolution can propagate a functional RNA through repeated rounds of replication and selection. Earlier versions did not have sufficient copying fidelity, but an improved variant can now replicate the hammerhead ribozyme through a reciprocal synthesis. Two evolutionary lineages were carried out using either the prior low-fidelity or the newer high-fidelity polymerase. Deep sequencing followed the course of evolution, revealing variants that diverged from as fitness increased. This study demonstrates the critical importance of replication fidelity for maintaining heritable information in an RNA-based evolving system, such as is thought to have existed during the early history of life on Earth. (Abstract)

Purvis, Graham, et al. Generation of long-chain fatty acids by hydrogen-driven bicarbonate reduction in ancient alkaline hydrothermal vents. Communications Earth & Environment. 5/30, 2024. Newcastle University paleobiochemists quantify how another vital complexity stage came to readily occur. Once again our Earthuman retrospective scenario from prebiotic sources onto replicative protocells indeed takes on a robust guise of a natural endemic fertility.

The origin of life at some point required membrane-bound compartments to foster the separation and concentration of internal biochemistry from the external environment. Long-chain amphiphilic molecules, such as fatty acids, appear good candidates to have formed the first cell membranes. Here we show that the reaction of dissolved hydrogen and bicarbonate with the iron-rich mineral magnetite under conditions of continuous flow, alkaline pH and simple low temperatures (90 °C) generate a range of long-chain aliphatic compounds. Readily generated membrane-forming amphiphilic organic molecules in the first cellular vesicles may have been driven by similar chemistry generated from the mixing of bicarbonate-rich water with alkaline hydrogen-rich fluids. (Abstract)

Bridges, Alice, et al.. Bumblebees socially learn behaviour too complex to innovate alone. Nature. March, 2024. Seven social biologists mainly at Queen Mary University of London including Lars Chittka demonstrate ways to extend life’s prevalent impetus for collaborative, informed societies all the way to invertebrate insects.

Culture refers to behaviours that are commonly learned and persist within a population over time. It has been found that animal culture can also be cumulative. Here we show that even bumblebees can learn from trained demonstrator bees to obtain food rewards, even though they fail to do so on their own. This suggests that social learning might permit the acquisition of behaviours too complex to ‘re-innovate’ through individual learning. (Excerpt)

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