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Displaying entries 31 through 45 of 62 found.

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

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.

Noble, Denis and Raymond Noble.. Understanding Living Systems.. Cambridge: Cambridge University Press, 2023. The esteemed octogenarian British brothers continue on message that a fixation on genes and mutation only is quite misguided, out of date and should be replaced such as Philip Ball does in his 2023 work How Life Works: A User’s Guide to the New Biology (herein).

Life is definitively purposive and creative. This book presents a paradigm shift in understanding living systems where the genome is not a code, blueprint or set of instructions. The authors show that gene-centrism misrepresents what genes are and how they are used by living systems. In fact, organisms make choices, influence their behaviour, development and evolution, and act as agents of natural selection. Reading this book will make you see life in a new light as a marvellous phenomenon.

Denis Noble is a British physiologist and biologist who held the Burdon Sanderson Chair of Cardiovascular Physiology at the University of Oxford from 1984 to 2004. Noble established The Third Way of Evolution project with James Shapiro which predicts that the entire modern synthesis will be replaced. Raymond Noble is Honorary Associate Professor at University College London.

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)

Strogatz, Stephen and Iain Cousin. How Is Flocking Like Computing? Quanta. March 29, 2024. A transcript from an interview in The Joy of Why series between the Cornell University complexity theorist and a Max Planck Institute of Animal Behavior senior researcher (search each). The subject was the state of ongoing studies about evolutionary and environmental groups which seem to assemble and persist in similar kind from active particles all the way through every creaturely form onto our own neural and social selves. Once again into the 2020s, a nested, recurrent consistency of one same pattern and process is being defined, filled in and confirmed. As the quotes cite, the constant phenomenon is lately realized to imply and arise from an independent mathematical, program-like code-script source.

Well, that’s one of the most amazing things about studying collective behavior. It’s central to a widest range of organisms from the simplest placozoa animal, a swarm of cells, moving like a bird flock or a fish school — up through the invertebrates, like ants, that form swarms, to vertebrates, such as schooling fish, flocking birds, herding ungulates, and primates, including ourselves — humans.

And so, this is one of the things I find most remarkable about collective behavior, is that even though the system properties, whether you’re a cell or whether you’re a bird, are very different, when you look at the whole phenomena, the mathematics that underlie this actually turn out to be very similar. And so we can find these, sort of, what are called universal properties that connect these different, apparently disparate systems.

But we’re beginning to understand is that the common feature they share is computation. It’s that these entities gather together to compute about their environment in ways that they can’t compute on their own. And so, there’s these deep questions that we’re beginning to address about computation and the emergence of complex life which relates to what we’ve learned from physical systems close to a phase transition. (Iain Couzin)

Enquist, Brian, et al. Scaling approaches and macroecology provide a foundation for assessing ecological resilience in the Anthropocene. Philosophical Transactions B. April, 2024. Senior environmental theorists BE, University of Arizona, Doug Erwin, National Museum of Natural History and Van Savage and Pablo Marquet, Santa Fe Institute make a case for wider perspectives as a better way to study, analyze and manage flora and fauna biotas because of their multiple complexities.

In the Anthropocene, intensifying ecological disturbances challenge our predictive capabilities for ecosystem responses. A macroecology of emergent statistical patterns in ecological systems can find consistent regularities in biodiversity and ecosystems by way of abundance, body size, geographical range, species interaction networks, or the flux of matter and energy. We suggest a conceptual and theoretical basis for ecological resilience that integrates macroecology with a stochastic diffusion approximation constrained by principles of biological symmetry. We show how our framework can quantify major disturbances and their extensive ecological ramifications. (Excerpt)

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