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

2. The Innate Affinity of Genomes, Proteomes and Language

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)

Waseem, Muhammad, et al.. Language-independence of DisCoCirc’s Text Circuits: English and Urdu. arXiv:2208.10281. An Oxford University Computational Intelligence team, including Bob Coecke continue to finesse reasons why genomic and linguistic descriptive phases can be found to have a common character which arises from a communicative reality.

DisCoCirc is a newly proposed framework for representing the grammar and semantics of texts using compositional, generative circuits. While it advances the Categorical Distributional Compositional (DisCoCat) framework, it achieves radical new features toward eliminating grammatical differences between languages. In this paper we suggest that this is indeed the case for restricted fragments of English and Urdu. There is a simple translation from English grammar to Urdu grammar, and vice versa. We then show that differences in grammatical structure between English and Urdu - primarily relating to the ordering of words and phrases - vanish when passing to DisCoCirc circuits. (Abstract)

Wilson, Erin, et al. Genotype Specification Language. ACS Synthetic Biology. 5/6, 2016. We cite this entry by a nine member team including Darren Platt of Amyris Biotechnologies, Emeryville, CA as an example of how the nascent field of genoinformatics or genolinguistics is moving to respectfully reinvent a much better life, environment and sustainable planet. See also Double Dutch: A Tool for Designing Combinatorial Libraries of Biological Systems by Nicholas Roehner in this same issue.

We describe here the Genotype Specification Language (GSL), a language that facilitates the rapid design of large and complex DNA constructs used to engineer genomes. The GSL compiler implements a high-level language based on traditional genetic notation, as well as a set of low-level DNA manipulation primitives. The language allows facile incorporation of parts from a library of cloned DNA constructs and from the “natural” library of parts in fully sequenced and annotated genomes. GSL was designed to engage genetic engineers in their native language while providing a framework for higher level abstract tooling. To this end we define four language levels, Level 0 (literal DNA sequence) through Level 3, with increasing abstraction of part selection and construction paths. GSL targets an intermediate language based on DNA slices that translates efficiently into a wide range of final output formats, such as FASTA and GenBank. (Abstract)

Witzany, Gunther. Biocommunication and Natural Genome Editing. Dordrecht: Springer, 2010. A book-length exposition by the Austrian philosopher and editor (see next) of the linguistic turn to perceive living systems, across many nested whole scales, as most characterized by text-like, informational qualities. In an initial chapter an historic sequence of worldviews from “monistic-organismic” to “pluralistic-mechanistic” to this nascent “organic-morphological” phase is laid out. (Reading this synopsis, one is struck by an apparent Right to Left to Whole Brain passage for humankind that we retrace in our own lives.) The next chapters dutifully span flora and fauna from viral, genomic, fungal, bacterial, cellular, and honey bee realms so as to highlight their dialogic, quorum-sensing essence. Altogether with other recent postings (e.g. Beckner, et al) a sense of a natural genesis may accrue that is intrinsically textual as genetics and language meld in a singular evolutionary emergence. Since both modes are being found to express a self-organizing complex dynamics, this phenomenal propensity itself could take on the guise of a universe to human genetic code.

Current molecular biology as well as cell biology investigates its scientific object by using key terms such as genetic code, code without commas, misreading of the genetic, coding, open frame reading, genetic storage medium DNA, genetic information, genetic alphabet, genetic expression, messenger RNA, cell-to-cell communication….All these terms combine a linguistic and communication theoretical vocabulary with a biological one. In this book I try to introduce and appropriate model to exemplify this vocabulary (which is used in biology all the time without people thinking about it), on the basis of explanation and understanding of a linguistic action, the great variety of communicative actions. (v)

In parallel, the usage of a ‘language’-metaphor has increased since the mid-twentieth century with the growing knowledge about this genetic code. Most of the processes which evolve, constitute, conserve, rearrange the genetic storage medium DNA are terms which were originally used in linguistics such as coding, copying, transcription, translation, signaling, signal transduction, etc. Meanwhile the linguistic approach has also lost its metaphorical character and the similarity between linguistic languages/codes and the genetic storage medium are not only accepted but are fully adapted in bioinformatics, biolinguistics, protein linguistics, biohermeneutics and biosemiotics. (198-199)

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