Recent Additions: New and Updated Entries in the Past 60 Days
Displaying entries 61 through 75 of 126 found.

Ecosmomics: Independent Complex Network Systems, Computational Programs, Genetic Ecode Scripts

Fariselli, Piero and Amos Maritan. Thermodynamic perspectives into DNA stability and information encoding in the human genome. Communications Physics.. 8/102, 2025. University of Torino and University of Padova system theorists (search AM) offer a deeper energetic explanation for the steady presence of nucleotide descriptive contents.

The perpetuation of species depends on two vital factors at the DNA level: the encoding of information essential for survival and adaptation, and the stability of DNA to preserve this content. Our study focusses on the latter aspect and confirms that local interactions within DNA are sufficient to provide a thermodynamic foundation for effective genome reliability. By evaluating the effective energy of DNA sequences, this framework offers insights into physical principles, information encoding, and mutation dynamics. (Excerpt)

Grundler, Michael, et al. A geographic history of human genetic ancestry. Science. March 27, 2025. This news worthy accomplishment by University of Michigan evolutionary geneticists including Gideon Bradburd achieves a novel, dramatic visualization of past personal and communal genome networks. While our lives go forth guided by such nucleotide endowments, their deep code-script Presence remains invisible to us. For the first time, these graphic patterns of pan-ancestry can now become vividly evident. See a UM report at news.umich.edu/a-genetic-tree-as-a-movie-moving-beyond-the-still-portrait-of-ancestry for a popular review

Describing the distribution of genetic variation across individuals is a fundamental goal of population genetics. We present a method based on the rich genealogical information encoded in genomic tree sequences to infer the geographic locations of the shared ancestors of a sample of sequenced individuals. We used this method to infer the geographic history of genetic ancestry of a set of human genomes sampled from Europe, Asia, and Africa by recovering population movements on those continents. Our findings note the importance of the spatiotemporal context of genetic ancestry when describing human genetic variation. (Abstract)

Lazaridis, Iosif, et al. The genetic origin of the Indo-Europeans. Nature. 639.132, 2025. Some ninety scholars posted at Human Evolutionary Biology, Harvard University, Kalmyk Scientific Centre, Russian Academy of Sciences, National Agency for Archaeology, Moldova, Oxford University, Peter the Great Museum of Anthropology, St. Petersburg, Centre for Applied Bioanthropology, Zagreb, Croatia and so on proceed with further reconstructions of Eurasian cultures by virtue of their genomic migrations. The large project was mainly coordinated by coauthor David Reich and his extended Harvard group. See also A genomic history of the North Pontic Region from the Neolithic to the Bronze Age by Alexey Nikitin in the same issue . Once again into these global 2020s, a whole scale Earthuman recreation is being achieved.

The Yamnaya archaeological complex appeared around 3300 bc across the steppes north of the Black and Caspian Seas, and by 3000 bc it reached its maximal extent, ranging from Hungary in the west to Kazakhstan in the east. To localize Yamnaya origins among the preceding Eneolithic people, we assembled ancient DNA from 435 individuals into three genetic clines. A Caucasus–lower Volga (CLV) cline suffused with Caucasus hunter-gatherer1 ancestry extended between a Caucasus Neolithic southern end and a northern end at Berezhnovka. The Dnipro cline was formed when CLV people moved west, mixing with people with Ukraine Neolithic hunter-gatherer ancestry We therefore propose that the final unity of the speakers of ‘proto-Indo-Anatolian’ occurred in CLV people some time between 4400 bc and 4000 bc. (Excerpt)

Kilgore, Henry, et al. Protein codes promote selective subcellular compartmentalization. Science. February 6, 2025. In our novel phase of AI assisted computational biology, twelve researchers at the Whitehead Institute for Biomedical Research and Computer Science and Artificial Intelligence Laboratory, MIT describe a language based code-script model in addition to functional aspects which can now predict which bounded places they locate in.

Cells have evolved mechanisms to distribute billions of protein molecules to subcellular phases where they are involved in shared functions. Here, we show that these proteins convey amino acid sequence codes that guide them to compartment destinations. A protein language model, ProtGPS, was developed that predicts their localization from the training set. Our results indicate that protein sequences contain not only a folding code, but also a previously unrecognized code governing their distribution to diverse subcellular compartments. (Excerpt)

McBride, John and Tsvi Tlusty. McBride, John and Tsvi Tlusty. The physical logic of protein machines.. Journal of Statistical Mechanics. Vol. 2024/Num. 2, 2025. This paper by Center for Soft and Living Matter, Institute for Basic Science, Ulsan, South Korea theorists was presented at the STATPHYS 28 conference in 2024 as another way to combine neural net learning, proteome programs and AI language methods. We also note usage of the machine word whence it is meant to infer, so to clarify, a computer rather than a lathe. This is traced herein to a Simple mechanics of protein machines by Holger Flechsig and Alexander Mikhailov in the Journal of the Royal Interface for June 2019.

Proteins are intricate biomolecules whose complexity arises from the heterogeneity of the amino acids and their dynamic network of many-body interactions. Their functionality was shaped by an evolutionary history through intertwined paths of selection and adaptation. However, their basic logic remains open. Here, we explore a physical approach that treats proteins as mechano-chemical machines, which are adapted via a concerted evolution of structure, motion, and chemical interactions. (Excerpt)

Albors, Carlos, et al. A Phylogenetic Approach to Genomic Language Modeling.. arXiv:2503.03773. We cite this entry by UC, Berkeley compututational biologists and statisticians including Yun Song and Gonzalo Benegas as an example of active endeavors to work out a viable, reciprocal integration of Genome association studies and Large language methods. As AI capabilities continue to expand, this novel linguistic aspect is seen bring enhanced insights and benefits. See also A DNA language model based on multispecies alignment predicts the effects of genome-wide variants by Gonzalo Benegas, et al in Nature Biotechnology. (January 2025) for more from this group.

Genomic language models (gLMs) have shown some success in identifying evolutionarily constrained elements in mammalian genomes. To advance this task, we introduce a novel framework for training gLMs that explicitly deals with nucleotide evolution on phylogenetic trees. We applied this framework to train PhyloGPN, a model that excels at predicting functionally disruptive variants from a single sequence alone and demonstrates strong transfer learning capabilities. (Albors)

Recently, there has been an emerging interest in training large language models on genome sequences [3].One of the primary reasons for developing these models is to enable transfer learning. If these models make it possible to interpret genetic variants of otherwise-unknown function, they could advance in our understanding of genetics and, in turn, foster human health and welfare. (1)

Protein language models have predicted many hew versions but DNA language models have not yet been applied to complex genomes. Here, we introduce GPN-MSA (genomic pretrained network with multiple-sequence alignment), that leverages whole-genome alignments across multiple species. (Benegas)

Subirana-Granés, Marc, et al. Genetic Studies Through the Lens of Gene Networks.. Annual Review of Biomedical Data Science. February, 2025. Into the mid 2020s entries like this by University of Colorado, Anschutz Medical Campus researchers report how they are taking appropriate advantage of AI capabilities with regard to GWAS studies so to gain new levels of insight and functional benefit.

Genome-wide association studies have identified many variant–trait associations, but most are located in noncoding regions, making the link to biological function elusive. Here, we review approaches to leverage machine learning methods that identify gene modules by coexpression and functional relationships. This integration provides a context-specific understanding of disease processes and enhances the interpretability of genetic studies in personalized medicine. (Excerpt)

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

Csillag, Marten, et al. From Bayes to Darwin: Evolutionary search as an exaptation from sampling-based Bayesian inference.. Journal of Theoretical Biology. Vol. 599, February, 2025. Senior bioscholars at the Centre for Ecological Research, Budapest and UC San Diego including Eors Szathmary, whereby this paper becomes his latest contribution. The technical essay is written much as a work in progress by this extended group as innovative theory and real evidence continues to arise and inform. In regard, life’s selective process by way of many diverse candidates and their selective retention is now viewed as a whole scale iterative optimization process. This latest view is then seen to similarly be in effect for both prebiotic occasions and cerebral phenomena.

Building on the algorithmic equivalence between population replicator dynamics and particle filter approximations of Bayesian inference, we design a computational model for Darwinian evolution. Selection for Bayesian inference at the collective level then induces an adaptive emergence by an iterative (filial) transition in individuality. We suggest that this selective occasion can explain how Darwinian dynamics can apply neural nets within animal and human brain to endow them with planning capabilities. Further physical implementations might include prebiotic molecules and reinforcement learning agents. (Excerpts)

Doolittle, W. Ford. Darwinizing Gaia: Natural Selection and Multispecies Community Evolution. Cambridge: MIT Press,, 2024. The author is an esteemed biologist who for many years was a professor at Dalhousie University in Nova Scotia. But he was notably resistant since the 1980s to this view that living systems can regulate and maintain themselves on a planetary scale. Now four decades later when evolutionary understandings have moved beyond Darwinian strictures (W. Veit, et al), a more considerate acceptance is indeed possible. However, in so doing most of the book is a latest, thorough survey to date of these theoretical frontiers. Ten chapters such as Unresolved Conflicts in Darwinism, Holobiosis, Extended Phenotypes, Replicators and Interactors and especially Evolutionary Transitions in Individuality present a topical discussion of our mid-paradigm shift mix (per T. Kuhn) of both vested and replacement versions.

In the 1970s, James Lovelock's Gaia Hypothesis proposed that living organisms developed in tandem with their inorganic surroundings so to form a complex, self-regulating system. But evolutionary biologists still consider the theory problematic. In Darwinizing Gaia, W. Ford Doolittle, one of evolutionary and molecular biology's most prestigious thinkers, reformulates what evolution by natural selection is while legitimizing the controversial Gaia Hypothesis. As the first book attempting to reconcile Gaia with Darwinian thinking, and the first on persistence-based evolution, Doolittle's clear, innovative position broadens evolutionary theory by offering potential remedies for Gaia's theoretical challenges

Lala, Kevin, et al. Evolution Evolving: The Developmental Origins of Adaptation and Biodiversity. Princeton: Princeton University Press, 2024. Five veteran authors, KL (nee Laland), Tobias Uller, Nathalie Feiner, Marcus Feldman and Scott Gilbert orient and advance a once and future proposal that life’s personal occasion and maturation, aka eco evo devo, across the extent of Metazoan animals should take the place of life’s central explanatory basis.

A new scientific view of evolution is emerging that questions and expands our understanding of how evolution works. Recent research shows that organisms differ in how effective they are at evolving because the process itself has changed over time. In this book, a group of leading biologists draw on the latest findings in evo-devo studies, as well as epigenetics, symbiosis and inheritance to examine the central role that developmental processes play.

Sokolowski, Thomas, et al. Deriving a genetic regulatory network from an optimization principle. PNAS. 122/1, 2025. Institute of Science and Technology Austria and Princeton University computational theorists including William Bialek and Gašper Tkačik post a latest, full scale representation of life’s evolution as some manner of a persistent, episodic, iterative improvement. Although at an early stage, it is far from Darwin and may presage, at last, an insightful verification of life’s actual ecosmic orthogenesis

Many biological systems operate near the physical limits to their performance, suggesting that aspects of their behavior and underlying mechanisms could be derived from optimization principles. Here, we explore a detailed model of the gap gene network in the Drosophila embryo, made to maximize the information that gene expression levels provide. Our framework quantifies the tradeoffs involved in functional behavior and allows for the exploration of alternative network configurations. Our results suggest that multiple solutions to the optimization problem might exist across related organisms. (Abstract)

Thacik, Gaspar and Pieter Rein ten Wolde. Information Processing in Biochemical Networks. Annual Review of Biophysics. February, 2025. Institute of Science and Technology, Austria and AMOLF, Amsterdam biotheorists suggest better ways to understand and include the vital metabolic conveyance of encoded, instructional content. In the course of elucidating this advance, they also contribute new appreciations of how natural evolution can be seen to proceed and develop by way a constant optimization procedure See also Deriving a genetic regulatory network from an optimization principle by Thomas Sokolowski, et al in PNAS (122/1, 2025).

Living systems are characterized by controlled flows of matter, energy, and information. While the biophysics community has engaged with the first two, addressing information issues has been harder to achieve. In regard, studies at the interface of biophysics, quantitative biology, and engineering have led to a mathematical language at the molecular scale. Here we review how flows of information through biochemical networks use information-theoretic data computed within modeling frameworks. Optimization is presented as a design principle that navigates the relevant time, energy, crosstalk, and metabolic constraints to predict reliable cellular signaling and gene regulation.
(Excerpt)

AMOLF conducts leading research on the fundamental physics and design principles of natural and man-made animate complex matter. The institute applies this knowledge to create novel functional materials, and to solve societal challenges in renewable energy, green ICT, sustainable materials, and healthcare. AMOLF is home to some 150 scientists in 19 research groups and located at Amsterdam Science Park. See also the AMOLF Magazine on this site.

Veit, Walter, et al. Evolution, Complexity, and Life History Theory. Biological Theory.. January, 2025.
Along with new admissions of a teleologic course (see section), if common convergence, homology, transitions and more are factored in, a growing sense accrues that an oriented emergence seems to be going on by itself which does not square with the neoDarwinian view. This paper by University of Reading and Oxford University biologists is instance as a younger generation entering the theoretic fray. An earlier version was presented at the Paradox of the Organism meeting at Georgetown University in November 2022 (Google for much more) where similar concerns and proposals were broached.

As the evidence builds and the paradigm shifts see also Metabolic complementation between cells drives the evolution of tissues and organs by Mihaela Pavlicev, et al in Biology Letters (November 2024), Evolution Evolving: The Developmental Origins of Adaptation and Biodiversity by Kevin Lala et al (Princeton 2024) and especially Darwinizing Gaia: Natural Selection and Multispecies Community Evolution by Ford Doolittle (MIT Press, 2024).

In this article, we revisit the longstanding debate of whether there is a pattern in the evolution of organisms towards greater complexity, and how this hypothesis could be tested using an interdisciplinary lens. We argue that this debate remains alive today due to the lack of a quantitative measure of complexity that is related to the teleonomic nature of living systems. We propose that an ideal method to quantify this complexity lies within life history strategies for they are under selection to optimize the organism’s fitness. In this context, we consider how this complexity can be measured mathematically, and how to engage in a comparative analysis of this complexity across species to investigate the evolutionary forces driving this orientation.

Pavlicev, Mihaela, et al. Metabolic complementation between cells drives the evolution of tissues and organs.. Biology Letters. November, 2024. As expansive evolutionary understandings continue to presage a 2020s genesis synthesis, University of Vienna, James DiFrisco, Francis Crick Institute, London, Alan Love, University of Minnesota and Günter Wagner, Yale University describe a further array of recurrent, nested transitional domains that appear to occur and distinguish within creaturely anatomies and physiologies.

Although evolutionary transitions of individuality have been extensively theorized, little attention has been paid to the origin of levels of organization within organisms. We propose a hypothesis for this version based on metabolic constraints on functional performance and the capacity for complementation between cells. We illustrate this process of ‘supra-functionalization’ using the nervous system and pancreas. (Excerpt)Although evolutionary transitions of individuality have been extensively theorized, little attention has been paid to the origin of levels of organization within organisms. We propose a hypothesis for this version based on metabolic constraints on functional performance and the capacity for complementation between cells. We illustrate this process of ‘supra-functionalization’ using the nervous system and pancreas. (Excerpt)

Russo, Christopher, et al. Soft Modes as a Predictive Framework for Low-Dimensional Biological Systems Across Scales.. Annual Review of Biophysics. February, 2025. As the quote cites, University of Chicago biophysicists are now technically able to discern and finesse a new metabolic feature which further distinguishes life’s entire ascendant flourishall the way to our late learned notice and record.

All biological systems are subject to thermal fluctuations, environments, or mutations. Yet, while they consist of interacting components, recent experiments have shown that their response is low dimensional with a few stereotyped changes. In this review, we explore a unifying dynamical framework, dubbed soft modes, to explain and analyze low dimensionality from biomolecules to ecosystems. We argue that this condition generalizes classic ideas from developmental biology to disparate systems, namely phenocopying, dual buffering, and global epistasis. (Excerpt)

Previous   1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9  Next