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Displaying entries 61 through 75 of 102 found.

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

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)

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.

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)

Matange, Kavita, et al. Biological Polymers: Evolution, Function, and Significance. Accounts of Chemical Research. February 5, 2025. NASA Center for Integration of the Origins of Life, Atlanta and Georgis Tech bioscientists including Loren Dean Williams describe a more complete precursor trajectory whereof these prebiotic compounds are seen to make a significant contribution.

A holistic description of biopolymers and their evolution will contribute to our understanding of biochemistry and the origins of life. While biopolymer sequences evolve through Darwinian processes, how the backbones of polypeptides, polynucleotides, and polyglycans came to be is less certain.? To address this, we distinguished chemical species produced by evolutionary mechanisms from those formed by physical, chemical, or geological processes. Biopolymers display homo- and hetero- complementarity, enabling atomic-level control of structure and function. We argue that evolved biopolymer backbones then facilitated a seamless transition from chemical to Darwinian evolution. (Excerpt)

In sum, we present a model in which life on Earth was preceded by sustained chemical evolution. We propose that the chemical evolutionary process that led to biology is a special case of a general phenomenon. This model opens the possibility of applications of directed chemical evolution to a broad range from pharmaceuticals to material sciences. If an evolutionary process produced incredible molecules such as RNA and protein, then humankind can gain advantage by understanding and redirecting that process. (10)

Matange, Kavita, et al.. Evolution of complex chemical mixtures reveals combinatorial compression and population synchronicity. Nature Chemistry. February, 2025. In a news worthy paper, NASA Center for Integration of the Origins of Life, Atlanta and Georgis Tech bioscientists including Loren Williams and Jessica Bowman add a new dimension to origin studies by an emphasis on generic processes such as Wet-Dry Cycling, Complex Libraries of Condensable Components, Population Synchronicity, Energy Harvesting and Selective Fitness. As a result, a pre-existent self-organizing fertility can become evident before complex biochemicals began to form. As a planatural note, here is one more sophisticated advance as many prebiotic aspects just now reveal a phenomenal integrity from the uniVerse to an evolutionary gestation to our curious selves.

Many open questions about the origins of life involve the generation of complex chemical species. Here we propose to investigate general processes by which chemical systems continuously change with water as a medium. Our system (1) transitions to new chemical spaces; (2) demonstrates combinatorial compression with stringent selection; and (3) displays synchronicity of molecular populations. Our results suggest that chemical complexity can be observed in organic mixtures and might produce a broad array of molecules with novel structures and functions. (Excerpt)

Future Prospects. Our focus here is early-stage chemical evolution, rather than the production of highly evolved biopolymers such as RNA or protein. As noted by François Jacob, “the really creative part in biochemistry must have occurred very early.”
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The Center for Integration of the Origins of Life (iCOOL) seeks to integrate chemical sciences with evolutionary theory. We are developing conceptual and experimental models of chemical evolution focusing on selection, exaptations, mutualisms, and creativity. We believe that humankind will learn to understand, recapitulate, and avail chemical progressions analogous to those that led to the formation of biopolymers on ancient Earth.

Gilpin, William. Gilpin, William. The cell as a token: high-dimensional geometry in language models and cell embeddings.. arXiv:2503.20278.. A UT Austin systems biologist (see lab website) describes a latest beneficial blend of micro cellular studies and a select usage of novel AI attributes.

Single-cell sequencing technology maps to a complex space encoding their internal activity. This process mirrors machine learning methods, where large language models ingest unstructured text by converting words into discrete tokens. Here we explore how understanding the formation of language embeddings can inform efforts to analyze single cell datasets. We highlight advances in language modeling that can enhance future projects to build and consolidate cell atlases. (Excerpt)

Muro, Enrique, et al. The emergence of eukaryotes as an evolutionary algorithmic phase transition. PNAS. 122/13, 2025. By virtue of new instrumental Universidad Politécnica de Madrid led by Jordi Bascompte can now illume and describe a just how life’s long microbrial age was finally able to merge onward to nucleated cells. As the quotes say, this advance was found to involve a degree of algorithmic computations, along with the symbiotic unions long advocated by Lynn Margulis.

For half the history of life on Earth, the complexity of organisms was limited to prokaryotic cells such as contemporary bacteria. The process by which genes are activated was entirely regulated by proteins. This set up a limit on cellular complexity, as even larger proteins became computationally unfeasible. The eukaryotic cell with its membrane nucleus finally emerged due to a conserved process of gene growth and a change in genetic regulation. This increase in cellular complexity which occurred a relatively abrupt manner opened up the path toward multicellular organisms. (Significance)

The origin of eukaryotes represents one of the main events in evolution since it led to multicellular organisms. Yet, it remains unclear how existing regulatory mechanisms of gene activity were transformed to allow this increase in complexity. Here, we address analyze the length distribution of proteins for 6,000 species across the tree of life. We then found a scale-invariant relationship naturally originates through a multiplicative process of gene growth across the entire evolutionary history. Our results indicate that this original advance was due to an algorithmic phase transition similar to search algorithms involved in engendering larger proteins. (Abstract)

Reyes, Jorge and Jörn Dunke. Functional classification of metabolic networks.. arXiv:2503.14437. A MIT systems biologist and a mathematician identify and finesse the presence of a common, recurrent motif and motive which seems to grace our physiology and anatomy.

Chemical reaction networks underpin biological and physical phenomena from microbial interactions to planetary atmospheres. In biological systems, comparative genomics can trace evolutionary paths and sort organisms via DNA sequences. Metabolic changes driven due to nutrient availability requires appropriate information. Here we introduce a computational scheme that compares reaction networks to distances between the stoichiometric matrices. As a generalization has been widely applied which reveals its potential for many more disparate comparisons. (Excerpt)

In bacterial communities, spatiotemporal pyruvate cross-feeding by swarming Bacillus subtilis has been observed; bacteria in the swarm front consume their preferred carbon source and deposit pyruvate which is consumed by bacteria in the bulk. In mice and humans, models of metabolic processes have resolved cycles and energy use. On the astrophysical scale, Earth’s atmosphere is distinct from of other celestial bodies in the Solar System, which could also be a network-based biosignature. (1)

Recent experimental and computational developments offer an opportunity to develop functional classifications of chemical reaction networks grounded in physical principles. Here, we classify these networks using differences between nullspaces of their stoichiometric matrices. This new found generality from chemical reaction networks in bacteria to human tissues to atmospheres can lead to a universal atlas in which systems across length scales must reside. (9)

Callier, Vivianne. How Metabolism Can Shape Cells’ Destinies.. Quanta. March 21, 2025. A science writer reviews an array of recent research projects by Jan Żylicz, Lydia Finley, Berna Sozen, Navdeep Chandel, Kathryn Wellen and others which altogether reach quantified findings that much more is going on in physiological development and corporeal well-being beyond genetic-like influences. See, for example, Selective utilization of glucose metabolism guides mammalian gastrulation by Dominica Cao, et al (B. Sozen) in Nature (October 24, 2024.)

A growing body of work suggests that cell metabolism — the chemical reactions that provide energy and building materials — plays a vital, overlooked role in the first steps of life. In some ways, this interplay between metabolism and genes is obvious: We know that life is influenced by both its genes and its environment. This new, exciting field of research shows at a molecular level how the materials available to our cells influence their fates, and ours. (V. Callier)

The formation of a body plan from a simple multicellular structure occurs during gastrulation, the essential process of embryogenesis. Localized morphogen signals guide cell-fate decisions and behaviours to shape the embryo, but the precise mechanisms by which these critical signals integrate at the correct time and place to mediate gastrulation morphogenesis are not fully understood. The remarkable fidelity of this process indicates that multiple regulatory layers ensure robust embryonic patterning. (D. Cao)

Puri, Devina and Kyle Allison. Multicellular self-organization in Escherichia coli.. arXiv:2503.03001. Washington University, Saint Louis and Emory University, Atlanta biologists find persistent evidence of life’s constant trendings toward multiple cell to cell assemblies for metabolic and survival benefits. See also Escherichia coli self-organizes developmental rosettes by DP and KA in PNAS (121/23, 2024).

Escherichia coli has long been a trusty companion, maintaining health in our guts and advancing biological knowledge in the laboratory. In light of recent findings, we discuss multicellular self-organization in E. coli and develop general ideas for multicellularity, including dynamics and interpretation. In this context, We next discuss the self-organized behaviors such as rosette formation and internal communication. (Excerpt)

Lin, Guozheng, et al.. Experimental evidence of stress-induced critical state in schooling fish. bioRxiv.. February 25, 2025. Into this new year Centre de Recherches sur la Cognition Animale and Laboratoire de Physique Theorique, Universite de Toulouse, Beijing Normal University, Kunming University of Science and Technology, China and Indian Institute of Science, Bengalore biobehavior scientists including Guy Theraulaz can reach a new comprehensive phase of evidential veracity and theoretical maturity. In regard, their many references span this first quarter century such as Scott Camazine (2001), John Beggs (2008), William Bialek and Ian Couzin (2014) as they lead up to and converge on a mid 2020s confirmation. As the quotes allude, a constant recurrence of self-organized, critically poised member/neighbor/groups as flocks, pods, herds, tribes and so on have been found across every Metazon instance. See also Scaling in branch thickness and the fractal aesthetic of trees by Jingyi Gao and Mitchell Newberry in PNAS Nexus (4/2, 2025) for a similar view.

How do animal groups adjust their collective behavior in response to environmental changes remains to be fully explained. Here, we investigate how rummy-nose tetras are able to tune their groups as a way of testing whether these systems operate near a critical state to maximize sensitivity, responsiveness, and adaptability. Under stress, we find that the fish do in fact adjust their social interactions so to reach a critical state for these benefits. By revealing how stress and group size drive self-organization toward criticality, our study provides fundamental insights into collective biological systems and emergent properties in animal groups. (Excerpt)

Collective behaviors in biological systems have long intrigued scientists. These phenomena are observed at all scales from macromolecules and cell constituents to groups of organisms and result from an ability to self-organize and coordinate through their interactions. Recently, the concept of criticality derived from statistical mechanics has emerged as a compelling lens through which one can understand these adaptive behaviors. The criticality hypothesis suggests that biological systems should operate close to critical points akin to phase transitions in physics. A critical state offers unique properties that maximize its sensitivity to perturbations, enhance information processing and responses to environmental cues. (1)

Recent empirical evidence has begun to shed light on the relevance of criticality in various biological systems. From neural dynamics to gene regulation, aggregation in social amoeba, and collective motion in animal groups, studies have reported instances of scale invariance and critical behavior. In particular, research on spontaneous behavioral cascades and escape waves in schooling fish has provided valuable insights into how these systems do actually operate near criticality. (1)

In conclusion, this work made it possible to reveal and model the mechanisms underlying collective state transition phenomena observed in fish and the conditions under which these animal groups can reach a critical state. (10)

Taborsky, Michael, et al, eds. Division of labour as key driver of social evolution. Philosophical Transactions of the Royal Society B. March, 2025. This an editorial introduction by MT, Jennifer Fewell, Robert Gilles and Barbara Taborsky for a special title issue as the quote notes. Their credits are Behavioural Ecology, University of Bern, Arizona State University and MPI Animal Behavior. Among the eighteen entries are Cultural evolution, social ratcheting and the evolution of human division of labour by Lucio Vinicius, et al, Specialism and generalism in social animals by Koichi Ito and Andrew Higginson, Changes of division of labour along the eusociality spectrum in termites by J. Korb and Division of labour during honeybee colony defence by Daniela Ramirez-Moreno, et al.

Division of labour is a key driver of the economic success of human societies and of social evolution in general. Importantly, division of labour is not confined to human societies. It is present in social organisms ranging from bacteria to vertebrates, and accounts for the impressive ecological success of social insects such as ants and termites. There are intriguing parallels to interspecific mutualisms, which are characterised by the exchange of different services and commodities among unequal partners. This special issue provides a comprehensive view on how task specialisation and division of labour come about, how they are organized and what the biological roots are of this human ‘turbo enhancer’. Finally, its relevance for our modern world is critically evaluated. (Summary)

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