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

, . Stepney, Susan. Physical reservoir computing: a tutorial. Natural Computing. November 2024.. Natural Computing.. November, 2024. The University of York computer scientist (search) provides a latest succinct explanation of this increasingly popular procedure especially as quantum versions become available. See, for example, A Reservoir-based Model for Human-like Perception of Complex Rhythm Pattern by Zhongju Yuan, et al at arXiv:2503.12509.

This tutorial covers physical reservoir computing which first defines what it means for a physical system to compute, rather than evolve under the laws of physics. It describes the underlying computational Echo State Network (ESN) model, and explains why the it is suitable for direct physical implementation. The entry then describes how to characterise a physical reservoir in terms of benchmark tasks, and task-independent measures, along with optimising configuration parameters, and exploring the space of potential configurations. (Excerpt)

Reservoir computing is derived from recurrent neural network theory that maps input signals into dimensional spaces through a non-linear system called a reservoir The first key benefit is that training is performed only at the readout stage. The second is that the computational power of natural systems, both classical and quantum, can reduce the relative cost.

Flamm, Christoph, et al. Computation in chemical graph rewriting networks. Journal of Physics: Complexity. 6/1, 2025. CF and Peter Stadler, University of Vienna and Daniel Merkle, Algorithmic Cheminformatics Group, Bielefeld University discuss perceptive ways to investigate and identify the computational capabilities of ‘constructive’ chemistry.

transformations underlying the turn-over of their molecular components. In chemical reaction networks, computation may refer to two main aspects: concentrations of molecules, and molecular structures. The latter can be modeled by a chemical rewriting system acting on structural formulae, i.e. labeled graphs. We investigate graph rewriting and show that it can emulate Turing machines. and the computational capabilities of ‘constructive’ chemistry. (Excerpt)

Sidl, Leonhard, et al. Computational complexity, algorithmic scope, and evolution. Journal of Physics: Complexity. 6/1, 2025. University of Vienna and University of Leipzig bioinformatic researchers including Peter Stadler consider better ways by which life’s metabolic processes can be perceived and understood in terms of program operating systems. See also Computation in chemical graph rewriting networks by Christoph Flamm, et al in the same issue.

Biological systems are widely regarded as performing computations. Here we explore the idea that evolution confines biological computation to subsets of instances that can be solved with algorithms that are 'hardcoded' in the system. We use RNA secondary structure prediction as a developmental program to show that the salient features of the genotype–phenotype map remain intact even if 'simpler' algorithms are employed that correctly compute the structures for small subsets of instances. (Abstract)

Biological systems are often perceived to perform computations that solve complex problems at low computational cost as an evolutionary adaptation such as the central ‘information metabolism’ of a cell, i.e. DNA replication, transcription, and translation of RNA to proteins. Beyond these transformations of encoded information processing in neural systems, it is often unclear what exactly a biological system is computing. (1)

Returning to the question whether the concepts developed in theoretical computer science to describe computational complexity can also apply to computation in biological systems, we arrive at an affirmative answer. (11)

Monakhov, Sergei and Holger Diessel. Complex Words as Shortest Paths in the Network of Lexical Knowledge.. Cognitive Science. 48/11, 2024. Friedrich-Schiller University, Jena system linguists carry out a latest, comprehensive analysis of the English language to show how it is wholly characterized by complex network topologies and emergent behaviors. See also Composition as Nonlinear Combination in Semantic Space: A Computational Characterization of Compound Processing by Tianqi Wang and Xu Xu in this Journal (49/2, 2025) for similar findings in Chinese script. In regard, an extensive 2025 verification of this deeper, common ecode, textual dimension is again achieved, which then by turns implies a natural literary narrative.

Lexical models diverge on how to represent complex words. Under the morpheme-based approach, each morpheme is treated as a separate unit, while in the word-based methods, morphological structure is derived from complex words. In this paper, we propose a computational model for word-based networks to view how complex words are learned, stored, and processed. Our study shows that complex words can be segmented into morphemes through the shortest pathway and novel terms are often formed along optimal paths. Our empirical results are tested by a usage-based grammar which reveals that network science provides a deep language structure. (Excerpt)

ity of the network. In conclusion, network science provides a powerful framework for analyzing language. In this paper, we have focused on central aspects of morphological productivity. However, if we think of language as an encompassing network, the network approach can also be applied to many other phenomena in phonology, morphology, and syntax. This approach is consistent with the way psychologists and neuroscientists analyze the human mind and brain and resonates with the emergentist view of grammar. (28)

Arroyo, Jose. et al. Arroyo, Jose, et al. Toward a General Theory for the Scaling and Universality of Thermal Responses in Biology.. arXiv:2503.05128. Santa Fe Institute system theorists including Pablo Marquet, Christopher Kempes, and Geoffrey West post a chapter for the forthcoming volume Scaling in Biology: A New Synthesis from SFI Press. The technical paper goes on to discern still another instance of innate, recurrent self-similarities with regard to energetic gradients in metabolisms.

We developed a theory showing that under appropriate normalizations and rescalings, temperature response curves show a remarkably regular behavior and follow a general, universal law. The impressive universality of temperature response curves remained hidden due to curve-fitting models not well-grounded in first principles. In addition, this framework can help explain the origin of thermal scaling relationships in from biology to ecosystems. Here, we summarize the background, predictions, implications, and extensions of this theory. (Abstract)

Importantly, our framework can be used for predicting scenarios of global warming, disease spread, and industrial applications. It provides a general equation that can be integrated into theoretical ecology and evolution, such as Major Transitions in Evolution. It also allows us to better understand the impacts of climate change at global scales, whereby mutation rates and mortality of viruses will likely increase, given their convex temperature response curves. (16)

Deco, Gustavo, et al. Deco, Gustavo, et al. Complex harmonics reveal low-dimensional manifolds of critical brain dynamics. Physical Review E. 111/014410, January, 2025. Universitat Pompeu Fabra, Barcelona and Oxford University open another window to view a neural-like nature which evolves and proceeds to attain a twintelligence (herein a reciprocal poise) and effective cognizance by way of this inherent self-organized complementarity and familiarity. We also note that the paper appears in a traditional physics journal as the two realms grow together and reunite as one.

The brain needs to perform time-critical computations to ensure survival, for which nonlocal, distributed computation at the whole-brain level make possible by self-organized criticality. These responses accord with Schrödinger's wave equation, so as to form a complex harmonics decomposition (CHARM) framework to express the complex network dynamics that are the key computational engines of critical brain dynamics. (Excerpt)

Korbel, Jan, et al.. Microscopic origin of abrupt mixed-order phase transitions. Nature Communications. 16/2628, 2025. Veteran system theorists JK and Stefan Thurner, Complexity Science Hub, Vienna and Shlomo Havlin, Bar-Ilan University, Israel extend their 21st century studies to wider and deeper perceptions o an inherent natural criticality at each and every turn. Into April, many entries like this are now reaching a critical number which strongly implies a phenomenal independent existence of a universal mathematic source code that exemplifies itself in a genetic sense everywhere.

We suggest a possible origin for abrupt mixed-order transitions in physical systems by way of three Ising interaction models. We identify a microscopic origin driven by long-term cascades of changes. We calculate the critical exponents for the cascading, magnetization, convergence, and fluctuations of single-trajectory critical temperature. Our findings can shed light on the microscopic mechanisms behind many abrupt transitions in nature and technology. (Excerpt)
n this paper, we studied the origin of mixed-order phase transitions in the case of the Ising model with three types that change the interaction network. We studied three models: (i) the Ising model with molecule formation, (ii) the Potts model with hidden states, and (iii) the Truncated Ising model. In each of them, the their interactions changed the order of the phase transition from a second-order transition in the standard Ising model to an abrupt first-order, critical transition. (10)

Gabriel, Nicholas, et al. Connecting the geometry and dynamics of many-body complex systems with message passing neural operators. arXiv:2502.15913. George Washington University and Brown University system mathematicians including Neil Johnson describe a real connection all the way from deep physical phenomena to cerebral and public realms by way of novel renormalization theories. Once again, a deep grounding in substantial, generative dynamics is achieved as they continuously instantiate and exemplify themselves everywhere.

The relationship between scale transformations and dynamics established by renormalization group techniques is a cornerstone is in effect from fluid mechanics to elementary particle physics. Integrating these methods into neural operators for many-body complex systems could enhance their utility and uncover a multiscale organization. In this regard, we introduce a scalable AI framework, ROMA (Renormalized Operators with Multiscale Attention), for learning evolution operators and apply it to large systems with 1M nodes, long-range interactions, and Kuramoto oscillators. (Excerpt)

Hecker, Nikolai, et al. Enhancer-driven cell type comparison re. Enhancer-driven cell type comparison reveals similarities between the mammalian and bird pallium. Science. February 14, 2025. Twenty VIB Center for AI & Computational Biology. Leuven, Belgium biologists apply the latest neuroimage techniques to discern a constant recurrence of genomic sources and cerebral architectures throughout the Metazoan creatures. Once again our Earthumen acumen reveals how nature’s long developmental course reuses in kind the same patterns and processes.

Despite vast diversity in behavior and cognition, a consistent similarity in brain structures and even gene expression is being found to exist across the amniote group of reptiles, birds, and mammals. Three papers in this issue explore the development and evolution of the brain telencephalon. Rueda-Alana et al used single-cell resolution and mathematical modeling to investigate sensory circuits in chicken, gecko, and mouse. Zaremba et al generated a spatial cell atlas in chicken pallium. Hecker et al developed deep learning models to identify telencephalon cell types in chicken, human, and mouse. (Editorial)

Combinations of transcription factors govern the identity of cell types, which is reflected by genomic enhancer codes. We used deep learning to compare cell types in the telencephalon across amniotes. To this end, we resolved transcriptomics data of the chicken telencephalon. Enhancer codes of avian mesopallial neurons are most similar to those of mammalian deep-layer neurons. (Hecker Excerpt)

Poggialini, Anna, et al. Networks with many structural scales: a Renormalization Group perspetive. arXiv:2406.19104. We cite this work by Università “Sapienza” Rome and Universidad de Granada (Miguel Munoz) system physicists as an example of the increasing avail of this foundational theory in many far removed cerebral, bioregion and public phases. As the second quotes advises, by the mid 2020s such a consistently apt utiliety can then be seen to imply a true universal invariance. See also, e.g., Laplacian renormalization group: heterogeneous coarse-graining by Guido Caldarelli, et al in the Journal of Statistical Mechanics: (August 2, 2024). Gabriel, Nicholas, et al. Connecting the geometry and dynamics of many-body complex systems by Nicholas Gabriel, et al. at (arXiv:2502.15913).

Scale invariance profoundly influences the dynamics and structure of complex systems from critical phenomena to network architecture. Here, we propose a precise definition of scale-invariant networks by leveraging the concept of a constant entropy-loss rate in a renormalization-group coarse-graining setting. This approach differentiates between scale-free and scale-invariant networks, revealing characteristics within each class. We then survey genuine networks to show that the human connectome exhibits true scale invariance. (Excerpt)

The network paradigm captures essential attributes of real-world complex systems, offering a natural framework for studying entangled interconnected systems across disciplines like neuroscience [1], ecology [2], and epidemiology [3], among others [4]. Understanding the evolution-ary dynamics of complex networks, as they adapt their connectivity patterns to achieve diverse goals, is crucial to understanding their long-term stability or other features influencing functional roles and performance [5, 6]. Notably, amidst the multitude of potential network structures, one organization ubiquitously arises in natural systems: the scale-free architecture. (1)

Brixi, Garyk, et al. Genome modeling and design across all domains of life with Evo 2. biorxiv. February 21, 2025. Some fifty computational geneticists at the Arc Institute (Google); Stanford University, NVIDIA, Liquid AI, UC Berkeley, Columbia University and the University of California, San Francisco post a latest version of this their human person – AI planet initiative to expand and enhance a wider scope of (epi)genetic encodings. See also Semantic mining of functional de novo genes from a genomic language model by Aditi Merchant at bioRxiv (December 18, 2024). In early regard, the concept that some Turing-type, genomic-like, LLModel code script is indeed running as it programs and informs an ecosmic genesis becomes increasingly evident.

While the sequencing, synthesis, and editing of genomic codes have transformed biological research, intelligently composing new biological systems requires deeper understandings of their immense complexity. We introduce Evo 2, a foundation model trained on 9.3 trillion DNA base pairs from a genomic atlas spanning all domains of life. Evo 2 learns from DNA sequences to accurately predict the influences of genetic variation. Guiding Evo 2 via inference-time search also enables controllable generation of epigenomic structure, which we demonstrate for the first time. (Excerpt)

Our work shows that a generative model of genomic language enables a machine learning model to achieve generalist prediction and design capabilities across Metazoan life. By learning the statistical properties of DNA via a trillion tokens of genomic sequences, Evo 2 can predict mutational effects on protein function, ncRNA function, and organismal fitness. (17) Biological foundation models capable of composing novel systems to advance biomedical innovation, but also raise safety, security and ethical considerations. Aligned with Responsible AI Biodesign (Google), we assessed and mitigated concerns prior to open source publication. (18)

New AI breakthrough can model and design genetic code across all domains of life. A team of scientists from UC Berkeley, Arc Institute, UCSF, Stanford University and NVIDIA have developed a machine learning model trained on the DNA of over 100,000 species across the entire tree of life. The model, called Evo 2, can identify patterns in gene sequences across disparate organisms that would typically need years to uncover. In addition to identifying disease-causing mutations in human genes, Evo 2 can design new genomes that are as long as that of simple bacteria. (UC Berkeley Center for Computational Biology)

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)

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)

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