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

2. The Innate Affinity of Genomes, Proteomes and Language

Lackova, Ludmilla. Folding of a Peptide Continuum: Semiotic Approach to Protein Folding. Semiotica. 233/77, 2020. The Palacky University, Olomouc, CR linguist continues her studies of innate affinities across genetic, metabolic and onto communicative codes, which each seem to have a common textual nature. What then might be their phenomenal message as we first grade readers try to interpret, translate and understand?

In this paper I attempt to study the notion of “folding of a semiotic continuum” in a direction of a possible application to the biological processes (protein folding). The process of obtaining protein structures is compared to the folding of a semiotic continuum. Consequently, peptide chain is presented as a continuous line potential to be formed (folded) in order to create functional units. The functional units are protein structures having a certain usage in the cell or organism (semiotic agents). Moreover, protein folding is analyzed in terms of tension between syntax and semantics. (Abstract)

Lee, Ji-Hoon, et al. A DNA Assembly Model of Sentence Generation. BioSystems. Online, June, 2011. Seoul National University, Kyungpook National University, and University of Arkansas, bioinformatic scientists add to the evidence that these widely separated generative sources of life and culture share deep affinities with regard to their grammatical structures. Since the inklings of Roman Jakobson and Jean Piaget in the 1970s and earlier that genome and “languagome” (just coined) are deeply similar, this emergent evolutionary correspondence has been steadily proven, which this whole section seeks to document.

Recent results of corpus-based linguistics demonstrate that context-appropriate sentences can be generated by a stochastic constraint satisfaction process. Exploiting the similarity of constraint satisfaction and DNA self-assembly, we explore a DNA assembly model of sentence generation. The words and phrases in a language corpus are encoded as DNA molecules to build a language model of the corpus. Given a seed word, the new sentences are constructed by a parallel DNA assembly process based on the probability distribution of the word and phrase molecules. Here, we present our DNA code word design and report on successful demonstration of their feasibility in wet DNA experiments of a small scale. (Abstract)

Li, Zhi, et al. Extracting DNA Words Based on the Sequence Features. Theoretical Biology and Medical Modelling. 13/2, 2016. Shanxi Medical University, Taiyuan, China researchers carry out a formal interpretation of genetic systems by way of linguistic and textual terms. Nucleotide strings appear as a language with words, sentences, vocabularies, so that genomes are akin to a written book. This deep correspondence is braced by a novel algorithm that traces salient aspects of non-uniform distributions and integrity. Its validity is checked by applying to a select English volume, The Holy Bible (see quotes). How fortuitous, for here is evidence of a direct relation between religious scripture and a naturome code, God’s word and works.

Shanxi Medical University, Taiyuan, China researchers carry out a formal interpretation of genetic systems by way of linguistic and textual terms. Nucleotide strings appear as a language with words, sentences, vocabularies, so that genomes are akin to a written book. This deep correspondence is braced by a novel algorithm that traces salient aspects of non-uniform distributions and integrity. Its validity is checked by applying to a select English volume, The Holy Bible (see quotes). How fortuitous, for here is evidence of a direct relation between religious scripture and a naturome code, God’s word and works.

Lin, Yigun, et al.. Exploiting Hierarchical Interactions for Protein Surface Learning. arXiv:2401.10144. Hong Kong University of Science and Technology, and Nanyang Technological University, Singapore computer scientists post another frontier instance of creative ways to learn to read and write life’s amino acid metabolism.

Predicting interactions between proteins is a main project in structural bioinformatics which is often based on geometric and chemical features. Here, we propose key properties of a more effective learning process: 1) relationship atoms linked by covalent bonds to form biomolecules 2): a residue effect that validates hierarchical feature interactions among atoms and surface points). In this paper, we present a principled framework based on deep learning techniques, namely Hierarchical Chemical and Geometric Feature Interaction Network (HCGNet), for protein surface analysis by bridging chemical and geometric features with hierarchical interactions. (Excerpt)

In this work, we highlight the importance of the multiscale relationship between atoms and the hierarchical interaction between chemical and geometric features. To this end, we propose HCGNet, a novel learning architecture for protein surface analysis. HCGNet takes atoms and surface points of a given protein as the input. Then two hierarchical branches are used to learn chemical features from atoms and geometric features from surface points in parallel. In addition, features are hierarchically propagated from the chemical branch to the geometric branch for multi-modality feature fusion. (9)

List, Johann-Mattis, et al. Networks of Lexical Borrowing and Lateral Gene Transfer in Language and Genome Evolution. BioEssays. Online December, 2013. From our late vantage, Philipps-University Marburg, Heinrich-Heine University Düsseldorf, linguists and biologists achieve a keen observation about the historical study and affinity of these disparate programs. The course of linguistics has mostly been reconstructed in terms of vertical “trees,” which is also how eukaryote cellular life proceeds. But language history is actually seen to take horizontal, net-like pathways through sharings, akin to how microbial prokaryotes trade genetic materials. So a further, novel correspondence can be elucidated between genome and languagome. See also in BioEssays 36/1, 2014 Horizontal Gene Acquisitions by Eukaryotes as Drivers of Adaptive Evolution by Gerald Schonknecht, et al, whence such parallel traffic occurs for these nucleated cells.

Like biological species, languages change over time. As noted by Darwin, there are many parallels between language evolution and biological evolution. Insights into these parallels have also undergone change in the past 150 years. Just like genes, words change over time, and language evolution can be likened to genome evolution accordingly, but what kind of evolution? There are fundamental differences between eukaryotic and prokaryotic evolution. In the former, natural variation entails the gradual accumulation of minor mutations in alleles. In the latter, lateral gene transfer is an integral mechanism of natural variation. The study of language evolution using biological methods has attracted much interest of late, most approaches focusing on language tree construction. These approaches may underestimate the important role that borrowing plays in language evolution. Network approaches that were originally designed to study lateral gene transfer may provide more realistic insights into the complexities of language evolution. (List Abstract)

In contrast to vertical gene transfer from parent to offspring, horizontal (or lateral) gene transfer moves genetic information between different species. Bacteria and archaea often adapt through horizontal gene transfer. Recent analyses indicate that eukaryotic genomes, too, have acquired numerous genes via horizontal transfer from prokaryotes and other lineages. Based on this we raise the hypothesis that horizontally acquired genes may have contributed more to adaptive evolution of eukaryotes than previously assumed. Current candidate sets of horizontally acquired eukaryotic genes may just be the tip of an iceberg. We have recently shown that adaptation of the thermoacidophilic red alga Galdieria sulphuraria to its hot, acid, toxic-metal laden, volcanic environment was facilitated by the acquisition of numerous genes from extremophile bacteria and archaea. Other recently published examples of horizontal acquisitions involved in adaptation include ice-binding proteins in marine algae, enzymes for carotenoid biosynthesis in aphids, and genes involved in fungal metabolism. (Schonknecht Abstract)

List, Johann-Mattis, et al. Unity and Disunity in Evolutionary Sciences: Process-Based Analogies Open Common Research Avenues for Biology and Linguistics. Biology Direct. Online August, 2016. University of Pierre and Marie Curie, Paris theorists including Eric Bapteste survey the long together and apart interplay between genetics and languages. While parallels seem innately evident, their actual discernment has proven elusive until these late days of algorithmic network complexities. As this section reports, a cross-fertilization of analytic techniques such as homolog identification, sequence alignment, and protein literacy is much underway. And may we again report that from cosmic and galactic webs to neural net connectomics, from uniVerse to human epitome, the one, same iconic scriptome recurs and informs in kind.

We compared important evolutionary processes in biology and linguistics and identified processes specific to only one of the two disciplines as well as processes which seem to be analogous, potentially reflecting core evolutionary processes. These new process-based analogies support novel methodological transfer, expanding the application range of biological methods to the field of historical linguistics. We illustrate this by showing (i) how methods dealing with incomplete lineage sorting offer an introgression-free framework to analyze highly mosaic word distributions across languages; (ii) how sequence similarity networks can be used to identify composite and borrowed words across different languages; (iii) how research on partial homology can inspire new methods and models in both fields; and (iv) how constructive neutral evolution provides an original framework for analyzing convergent evolution in languages resulting from common descent (Sapir’s drift). (Results)

Liu, Jiajia, et al. Large language models in bioinformatics: applications and perspective.. arXiv:2401.04155. We cite this entry by Center for Computational Systems Medicine, University of Texas Health Science Center, Zhengzhou University, Southwest Jiaotong University and Center of Gerontology and Geriatrics, West China Hospital, Sichuan University computational biologists ast an example of the on-going interplay of bioinformatic studies and these novel linguistic programs, as the quote notes.

Large language models (LLMs) as based on AI deep learning perform well on various tasks such as natural language processing (NLP). LLMs are composed of artificial neural networks with many parameters trained on unlabeled input using self- or semi- supervised learning. However, their potential for bioinformatics studies may even exceed this proficiency. In this review, we review the prominent LLMs such as BERT and GPT, and explore their applications at different omics levels in bioinformatics like transcriptomics, proteomics, drug discovery and single cell analysis. (excerpt)

Livnat, Adi. Simplification, Innateness, and the Absorption of Meaning from Context. arXiv:1605.03440. Reviewed more in Systems Evolution, the University of Haifa theorist continues his project (search) to achieve a better explanation of life’s evolution by way of algorithmic computations, innate network propensities, genome – language affinities, neural net deep learning, and more.

Maggi, Luca. The main role of fractal-like nature of conformational space in subdiffusion in protein. arXiv:2306.07825. A Barcelona Institute of Science and Technology bioinformatics disease mechanism researcher provides a latest report of how vital self-similarities appear to suffuse their metabolic activities. See also The Evolution of Fractal Protein Modules in Multicellular Development by Harry Booth and Peter Bentley in Artificial Life Conference Proceedings (MIT Press 2022).

Protein dynamics studies their biological functions but a theoretical picture of their relevant features is still missing. For example, a prime property exhibited by this dynamic is its subdiffusivity. Here, by comparing all-atom molecular simulations and theory we show that this behavior arises from the fractal network of the network of metastable conformational states over which protein diffusion processes take place. (Excerpt)

Majewski, Maciej, et al. Machine Learning Coarse-Grained Potentials of Protein Thermodynamics. arXiv:2212.07492. We note this work by eleven bioinformatic researchers from Universitat Pompeu Fabra, Barcelona, Rice University, Houston, FU Berlin, Princeton University and Microsoft Research, Cambridge UK as an example of the latest integrations of biological studies (e.g. genes, cells, metabolism), neural net methods, and deep rootings in a conducive physical origin.

We note this work by eleven bioinformatic researchers from Universitat Pompeu Fabra, Barcelona, Rice University, Houston, FU Berlin, Princeton University and Microsoft Research, Cambridge UK as an example of the latest integrations of biological studies (e.g. genes, cells, metabolism), neural net methods, and deep rootings in a conducive physical origin.

Marin, Frederikke, et al.. BEND: Benchmarking DNA Language Models on biologically meaningful tasks. arXiv:2311.12570. At a time when prior sequence techniques have run their course, this paper by Novozymes A/S, Denmark, University of Copenhagen, and Computational Health Center, Munich researchers proposes a turn to natural language processing and large languages models by which enter a new advanced phase of rapid readings of whole genomes in their many functions. In regard, as novel protein linguistics, deep neural network, and AI capabilities come altogether in 2023, they also begin to imply the actual presence of an intrinsic textual, source code-script domain. As a result, biomolecular, genetic, linguistic, and cerebral phases, broadly conceived, gain a text-like similarity. Such a common vernacular has been a metaphor since the 1960s and maybe just now its verity and import can be realized.

The genome sequence contains the blueprint for governing cellular processes. However, experimental annotation of functional, non-coding and regulatory elements encoded in the DNA sequence remains both costly and difficult. This has sparked interest in language modeling of genomic DNA, which has seen much success for protein sequence data. In this study, we introduce BEND, a Benchmark for DNA language models, featuring a collection of realistic downstream tasks defined on the human genome. We find that embeddings from current DNA language models can approach performance of expert methods on some tasks, but only capture limited information about long-range features. (Abstract)

Markos, Anton and Dan Faltynek. Language Metaphors of Life. Biosemiotics. Online August 14, 2010. Charles University (Prague) scientist, and Palacky University (Olomouc, Czech Republic, where I once gave a keynote, see home page) philosopher argue that not only is communication the essence of livingness, it involves constant “readings” by all manner of creatures. Verily a greater, textual nature is revealed that evolved, emergent beings, now we phenomenal humans, are invited to read.

We believe that linguistic processes are present at all levels of life’s organization in the biosphere. Ecosystems, for example, do not build their homes – oikos – for ever; they maintain them by incessant communications games, reading included. We tend to read like our contemporaries, and from this common ground there often emerges something new unique; understanding the text is a unique performance of the reader. The same holds, we believe, for the members of any living species – in a species-specific way.

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