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

2. The Innate Affinity of Genomes, Proteomes and Language

Searls, David. The Language of Genes. Nature. 420/211, 2002. An affirmation that the molecular genetic code, as now studied by computer-based bioinformatics, is in fact a true language with its own grammar and syntax. And these techniques are also being used to explore the structures of literature.

…nucleic acids may be said to be at about the same level of linguistic complexity as natural human languages.…genes do convey information, and furthermore this information is organized in a hierarchical structure whose features are ordered, constrained and related in a manner analogous to the syntactic structure of sentences in a natural language. (213)

Searls, David. Trees of Life and of Language. Nature. 426/391, 2003. The same pattern occurs for the lineage of ancient languages and the reconstruction of evolutionary ancestors, whereby “philology recapitulates phylogeny.”

Shabi, Uri, et al. Processing DNA Molecules as Text. Systems and Synthetic Biology. 4/3, 2011. Weizmann Institute of Science, Rehovot, Israel mathematicians, biochemists, and a cell biologist spell out an extensive, technically detailed consideration of the nucleotide genome in terms of, as if, a linguistic document. A paper in the next issue (4/4, 2010) “Creating Novel Protein Scripts beyond Natural Alphabets” by Anil Kumar, University of Toronto, and Vibin Ramakrishan, Rajiv Gandhi Centre for Biotechnology, similarly reinforces. Are we altogether at last verifying a true literal nature, as prior traditions well know, whose creative genetic program then manifests in kind at each emergent level? And could it now be passing into our conscious knowledge, indeed to commence a new era of “synthetic biology”?

Polymerase Chain Reaction (PCR) is the DNA-equivalent of Gutenberg’s movable type printing, both allowing large-scale replication of a piece of text. De novo DNA synthesis is the DNA-equivalent of mechanical typesetting, both ease the setting of text for replication. What is the DNA-equivalent of the word processor? (227) Here we present a novel operation on DNA molecules,…and show that it provides a foundation for DNA processing as it can implement all basic text processing operations on DNA molecules including insert, delete, replace, cut and paste and copy and paste. (227) In this work we present a uniform framework for DNA processing that encompasses DNA edition, DNA synthesis, and DNA library construction. (228)

Sheinman, Michael, et al. Evolutionary Dynamics of Selfish DNA Explains the Abundance Distribution of Genomic Sequences. Nature Scientific Reports. 6/30851, 2016. As an instance of genome complexity, with Anna Ramisch, Florian Massip, and Peter Arndt, MPI Molecular Genetics researchers draw upon physics and linguistics to finesse features from these realms. See Massip in the next section for more from this team. Circa 2016, genomes are commonly treated as a whole entity, which are then seen to have deep affinities to universal nonlinear systems before and after.

Since the sequencing of large genomes, many statistical features of their sequences have been found. One intriguing feature is that certain subsequences are much more abundant than others. In fact, abundances of subsequences of a given length are distributed with a scale-free power-law tail, resembling properties of human texts, such as Zipf’s law. Despite recent efforts, the understanding of this phenomenon is still lacking. Here we find that selfish DNA elements, such as those belonging to the Alu family of repeats, dominate the power-law tail. Interestingly, for the Alu elements the power-law exponent increases with the length of the considered subsequences. Motivated by these observations, we develop a model of selfish DNA expansion. The predictions of this model qualitatively and quantitatively agree with the empirical observations. This allows us to estimate parameters for the process of selfish DNA spreading in a genome during its evolution. The obtained results shed light on how evolution of selfish DNA elements shapes non-trivial statistical properties of genomes. (Abstract)

Our genome is a sequence of A, C, G and T nucleotides and can be viewed as a long text of about three billion letters. Only a small part of our genome is functional and under selection; the rest (so-called junk DNA) mostly evolves neutrally and, therefore, is naively expected to be a random sequence. However, the junk DNA contains many homologous sequences, sharing significant similarities to each other. Hence, its statistical properties differ from those of random sequences. One of these properties, which we discuss here, is that for a given length, certain subsequences are much more abundant than others. Namely, the abundances of k-mers—sequences of length k—possess a wide, scale-free distribution, as shown in Fig. 1. This phenomenon resembles statistical properties of human texts, where abundances of words also exhibit a scale-free distribution. (1)

Soares, Eduardo, et al. Beyond Chemical Language: A Multimodal Approach to Enhance Molecular Property Prediction. arXiv:2306.14919. Seven IBM researchers posted in Rio de Janeiro, Brazil and San Jose, USA including Dmitry Zubarev first describe current approaches as this broad field of biomolecule parsings actively shifts to deep machine learning methods. See also Artificial Intelligence-aided Protein Engineering from Topological Data Analysis to Deep Protein Language Models at 2307.14587 for another instance. A number of technique proposals are then advanced going forward. Altogether such novel literacies add more evidence for an affine genetic and protein equivalence.

Protein engineering is an emerging field in biotechnology that has the potential to revolutionize various areas, such as antibody design, drug discovery, food security, ecology, and more. However, the mutational space involved is too vast to be handled through experimental means alone. Leveraging accumulative protein databases, machine learning (ML) models, particularly those based on natural language processing (NLP), have considerably expedited protein engineering. Moreover, advances in topological data analysis (TDA) and artificial intelligence-based protein structure prediction, such as AlphaFold2, have made more powerful structure-based ML-assisted protein engineering strategies possible. This review aims to offer a comprehensive, systematic, and indispensable set of methodological components, including TDA and NLP, for protein engineering and to facilitate their future development. (Excerpt)

Sondka, Zbyslaw, et al.. COSMIC: a curated database of somatic variants and clinical data for cancer.. Nucleic Acids Research. 52/D1, 2024. Wellcome Sanger Institute geneticists describe the latest four year version of their extensive, actively used informational resource for treating this malady.

The Catalogue Of Somatic Mutations In Cancer (COSMIC), https://cancer.sanger.ac.uk/cosmic, is an expert-curated knowledgebase providing data on somatic variants in cancer, supported by a comprehensive suite of tools for interpreting genomic data, discerning the impact of somatic alterations on disease, and facilitating translational research. Within the last 4 years, COSMIC has substantially expanded its utility by adding new resources: the Mutational Signatures catalogue, the Cancer Mutation Census, and Actionability.

Data curation is the organization and integration of data collected from various sources. It involves annotation, publication and presentation of the data so that the value of the data is maintained over time, and the data remains available for reuse and preservation. In science, data curation may indicate the process of extraction of important information from scientific texts, such as research articles to be converted into an electronic format.

Steels, Luc. Analogies between Genome and Language Evolution. Pollack, J. et.al, eds. Proceedings of Artificial Life IX. Cambridge: MIT Press, 2004. The Vrije Universiteit Brussel computer scientist and SONY Paris AI laboratory director contributes to the welling comparison between these molecular and textual programmatic modes.

The paper develops an analogy between genomic evolution and language evolution, as it has been observed in the historical change of languages through time. The analogy suggests a reconceptualisation of evolution as a process that makes implicit meanings or functions explicit.

First I emphasize the benefit of looking at the whole system (form, meaning, and effect in the case of language; genes, biochemical function, and structure/behavior in the case of genomes), as opposed to only focusing on the evolution of syntax. Second I will emphasize that both language evolution and genomic evolution are concerned with making certain meanings/functions explicit which were implicit before, or vice-versa.

Suhr, Stephanie. Is the Notion of Language Transferable to the Genes? Dorries, Matthiaus, ed. Experimenting in Tongues. Stanford: Stanford University Press, 2002. From a volume on how metaphors inform scientific paradigms, a history of linguistic interpretations and analogies of the molecular genetic code. These two “information-trading processes” share much affinity, which springs from a long tradition of imagining nature as a book to be read and translated.

Recursivity is indeed a universal phenomenon, as it shows in fractal pattern formation; it is an economical phenomenon as well creating complex variety – as for example the human brain – out of a few elements; and it is an important creative principle, which applies to areas beyond linguistics and information transmission. (60)

Tavares, Ana, et al. DNA Word Analysis Based on the Distribution of the Distances Between Symmetric Words. Nature Scientific Reports. 7/728, 2017. We note in 2017 this paper by University of Aveiro, Portugal, medical and computational mathematicians as an example of how it has become common usage to consider genetic phenomena by way of similar linguistic features.

We address the problem of discovering pairs of symmetric genomic words (i.e., words and the corresponding reversed complements) occurring at distances that are overrepresented. For this purpose, we developed new procedures to identify symmetric word pairs with uncommon empirical distance distribution and with clusters of overrepresented short distances. We focused on the human genome, and analysed both the complete genome as well as a version with known repetitive sequences masked out. We reported several well-defined features in the distributions of distances, which can be classified into three different profiles, showing enrichment in distinct distance ranges. (Abstract excerpts)

Turenne, Nicolas. On a Possible Similarity between Gene and Semantic Networks. arXiv:1606.00414. The University of Paris, INRA Science and Society bioinformatics researcher contributes to growing realizations, after decades of intimations since Jean Piaget and Roman Jakobson, that as similar self-organizing systems, the disparate realms of literature and genomes are necessarily one and the same natural testaments.

In several domains such as linguistics, molecular biology or social sciences, holistic effects are hardly well-defined by modeling with single units, but more and more studies tend to understand macro structures with the help of meaningful and useful associations in fields such as social networks, systems biology or semantic web. A stochastic multi-agent system offers both accurate theoretical framework and operational computing implementations to model large-scale associations, their dynamics and patterns extraction. We show that clustering around a target object in a set of associations of object prove some similarity in specific data and two case studies about gene-gene and term-term relationships leading to an idea of a common organizing principle of cognition with random and deterministic effects. (Abstract)

Victorri, Bernard. Analogy Between Language and Biology. Cognitive Processing. 8/1, 2009. The Centre National de la Recherche Scientifique (CNRS) linguist finds a deep correspondence between the hierarchical array of protein forms and transcriptions, and how human communication employs a similar scale from phonemes (smallest unit conveying a distinct meaning) to essay or speech. A dual “productive system” accrues in both cases of external events, as if a resultant phenotype, which springs from literal descriptions. A salient discovery might then be revealed in this work and companion approaches, which courses in both directions. Life’s evolution is distinguished by an ascendant “linguistic” essence, while our languages are in some real way akin to the molecular genetic code. Altogether an original, independent cosmic code is quite inferred, human and universe once again mirror each other, this late time as a temporal gestation.

If we now turn to the structural aspect of the analogy, the first observation to be made is that in both cases there is a primary sequential structure forming the basis of a complex hierarchical organization. As regards proteins, the discrete units composing the sequence are the twenty proteinogenic amino acids composing the polypeptide chain. As for language, the discrete units are the phonemes. Their number changes from one language to another, but the order of magnitude remains the same as the number of amino acids. (14)

Wang, Li-Min, et al. Mechanism of Evolution Shared by Genes and Language. arXiv:2012.14309. Nine National Tsing Hua University, Taiwan biologists and linguists describe a strongest parallel between these premier modes of vital, prescriptive content. After consideration from 1970 to 2000 to today, life’s evolutionary emergence can indeed be seen as endowed with deeply similar, Rosetta-like versions of genetic and linguistic informative codesl. We log this in with Siobhan Roberts review of cellular automata models such as John Conway’s Game of Life and Bert Chan’s Lenia Universe. Within a 21st century worldwise revolution, a natural genesis now well appears to have its own uniVerse to humanVerse ecosmomic code. In further regard, our Earthomo sapience may seem meant to achieve its sentient translation, and intentional continuance.

We propose a general mechanism for evolution to explain the diversity of genes and language. To quantify their common features and reveal hidden structures, several statistical properties and patterns are examined by way of a new method called the rank-rank analysis. We find that the older relation, "domain plays the role of word in gene language", is not rigorous, and propose to replace it by protein. Based on the correspondence between (protein, domain) and (word, syllgram), we discover that both genes and language share a common scaling structure and scale-free network. Like the Rosetta stone, this work may help decipher the secret behind non-coding DNA and unknown languages. (Abstract)

Among the topics of evolution, we are particularly interested in genes and natural languages. The fact that 20 kinds of codon, composed by three nucleotides in the set A, T, C, G encode genome sequence is similar to the human written text constituted by letters that form the alphabet. Therefore, it is intuitive to make an analogy between gene and language. When choosing the “space-time” of organism as nature and that of human as society, their inheritance of survival can be recorded in gene and language, respectively. (1)

The correspondence between gene and language may be the Rosetta Stone to decipher the language of genes. Scientists have applied linguistic formalisms to this goal, such as using Zipf’s and Shannon’s approach to quantify the linguistic features of non-coding DNA sequences, and exploring information hidden in genome with the aid of natural language processing (NLP). On the other hand, linguists have investigated the relationship between language and the natural selection, and discussed the language faculty in the broad and narrow sense from the viewpoint of biolinguistics. (1)

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