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

2. Biteracy: Natural Algorithmic Computation

Lamm, Ehud and Ron Unger. Biological Computation. London: Chapman & Hall, 2011. A Tel Aviv University philosopher of science and Bar-Ilan University computational biologist provide a comprehensive text on this salient, growing 21st century synthesis. Its sections are Introduction and Biological Background, Cellular Automata, Evolutionary Computation, Artificial Neural Networks, Molecular Computation, and The Never-Ending Story: the Interface between Biology and Computation.

Ldeborova, Lenka and Florent Krzakala. Statistical Physics of Inference: Thresholds and Algorithms. arXiv:1511.02476. With an initial nod to Pierre-Simon Laplace (second quote), University of Paris Saclay theorists engage a 21st century synthesis of these parallel methods. As one may peruse and imagine, a mathematical, genetic uniVerse trying to describe and realize itself out of an infinity of stochastic information may be implied.

Many questions of fundamental interest in todays science can be formulated as inference problems: Some partial, or noisy, observations are performed over a set of variables and the goal is to recover, or infer, the values of the variables based on the indirect information contained in the measurements. A growing body of work has shown that often we can understand these fundamental barriers by thinking of them as phase transitions in the sense of statistical physics. Moreover, it turned out that we can use the gained physical insight to develop new promising algorithms. Connection between inference and statistical physics is currently witnessing an impressive renaissance and we review here the current state-of-the-art, with a pedagogical focus on the Ising model which formulated as an inference problem we call the planted spin glass. In terms of applications we review two classes of problems: (i) inference of clusters on graphs and networks, with community detection as a special case and (ii) estimating a signal from its noisy linear measurements, with compressed sensing as a case of sparse estimation. (Abstract)

Our goal in this review is to describe recent developments in a rapidly evolving field at the interface between statistical inference and statistical physics. Statistical physics was developed to derive macroscopic properties of material from microscopic physical laws. Inference aims to discover structure in data. On the first look, these goals seem quite different. Yet, it was the very same man, Pierre Simon, Marquis de Laplace (1749{1827), who did one of the first sophisticated derivations of the gas laws within the caloric theory, and who also created the field of statistical inference [208]. This suggests there may be a link after all. In fact, the methods and tools of statistical physics are designed to describe large assemblies of small elements - such as atoms or molecules. These atoms can quite readily be replaced by other elementary constituents - bits, nodes, agents, neurons, data points. This simple fact, and the richness and the power of the methods invented by physicists over the last century, is at the roots of the connection, and the source of many fascinating work in the last decades. (First paragraph, 4)

Le Verge-Serandour, Mathieu and Karen Alim.. Le Verge-Serandour, Mathieu and Karen Alim. Physarum polycephalum: Smart Network Adaptation. Annual Review of Condensed Matter Physics. Volume 15, 2024. Center for Protein Assemblies; Technical University of Munich biophysicists provide a network neuroscience review of this cellular invertebrate whom is able to exhibit an advanced behavioral responses. Once again, an early, deep insistence of intelligent agency is evident as if a universal repertoire to access.

Life evolved organisms to adapt to their environment and autonomously exhibit behaviours. While complex behaviours are associated with the capability of neurons to process information, the unicellular organism Physarum polycephalum is able to solve complex tasks despite being a single cell shaped into a tubular network. In Physarum, smart behaviours arise as network tubes grow or shrink due to coupling, fluid flows and transport. From our physicist's perspective, we introduce the biology and active chemo-mechanics of this living matter entity (Abstract)

Physarum polycephalum, a giant single-cell slime mould, fascinates researchers with its sophisticated behaviour despite its simple build. The network-shaped body of Physarum plasmodia tops typical cell size reaching up to meters in length while enclosing thousands even millions of nuclei, which allows for complex behaviour similar to multi-cellular organisms. Here is a life form that beat the odds of 600 million years of evolution to thrive on earth today and combine traits of what later on became animals, plants and fungi in itself.

Levy, Pierre. The Philosophical Concept of Algorithmic Intelligence. Spanda Journal. Volume 2, 2014. The University of Ottawa Canada Research Chair in Collective Intelligence has been a pioneer theorist is this regard since the 1990s (search). Some twenty years on as he notes, this paper can present a work-in-progress of an “over-language” that a global cerebral faculty needs. As an Information Economy MetaLanguage or IEML, it remains an algebraic, computational “semantics,” but could add a “reflexivity” so the world brain can know that it knows. See also his Innovation in Coding in a later Spanda Creativity & Collective Enlightenment issue (VI/2, 2015). All these entreaties about an emerging 21st century “sapiensphere” (my term) seem to be getting closer to the actual witness, articulation, and hopeful avail of a palliative worldwide wisdom.

Li, Hui, et al. Multi-Level Formation of Complex Software Systems. Entropy. Online May, 2016. We cite this paper by Dalian Maritime University, China, information scientists as an example of how common network topologies are equally being found in computer programs. By turns, this inherence could infer that other locales such as genomes, connectomes, and an intelligent evolution could be seen to proceed and perform in an algorithmic manner.

Livnat, Adi. Simplification, Innateness, and the Absorption of Meaning from Context. Evolutionary Biology. Online March, 2017. Reviewed more in Systems Evolution, the University of Haifa theorist continues his project 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.

Livnat, Adi and Christos Papadimitriou. Sex as an Algorithm: The Theory of Evolution Under the Lens of Computation. Communications of the ACM. November, 2016. A University of Haifa evolutionary theorist and a UC Berkeley computer scientist compose a popular update on this digital Darwin synthesis. Its fertile promise is reported in this section, Systems Evolution, Intelligent Evolution, and elsewhere. Search each author for more papers and links.

Sexual reproduction is nearly ubiquitous in nature. Recent research at the interface of evolution and computer science has revealed that evolution under sex possesses a multifaceted computational nature - it can be seen as a coordination game between genes played according to powerful Multiplicative weights Update Algorithm; or as a randomized algorithm for deciding whether genetic variants perform well across all possible combinations; it allows mutation to process and transmit information;, and much more. Computational models and considerations are becoming an indispensable tool for unlocking the secrets of evolution. (Key Insights)

Manca, Vincenzo. The Principles of Informational Genomics. Theoretical Computer Science. 701/190, 2017. The University of Verona computer scientist complements his chapter Decoding Genetic Information with G. Franco in Computational Matter (S. Stepney, 2017) about novel perspectives of genetic activity in terms of their algorithmic, semantic, linguistic qualities.

The present paper investigates the properties of genomes directly related with their long linear structure. A systematic approach is introduced that is based on an integration of string analysis and information theory, applied and verified on real genomes. New concepts and results are given in connection with genome empirical entropies (and related indexes), genome dictionaries and distributions, word elongations, informational divergences, genome assemblies, and genome segmentations.

Marletto, Chiara. Constructor Theory of Life. arXiv:1407.0681. As the Abstract alludes, and many scientists now admit, the claim that selection alone is all that is needed, or going on, is simply inadequate. The Oxford University mathematician and collaborator with physicist David Deutsch (search) articulates another take on something else and more, with affinities to and roots in statistical physics, as nature’s informative source. From disparate entries such as cellular automata, computationalism, algorithmic nature and more, these efforts converge on some manner of an implicate, program-like code from which evolution arises, iterates and exemplifies. As noted for Gordana Dodig-Crnkovic 2014, there is a need for cross-translation and synthesis such as the title and the technical terms, along with an admission of a self-existing reality and procreation of which everything is an intended phenomenon.

Neo-Darwinian evolutionary theory explains how the appearance of purposive design in the sophisticated adaptations of living organisms can have come about without their intentionally being designed. The explanation relies crucially on the possibility of certain physical processes: mainly, gene replication and natural selection. In this paper I show that for those processes to be possible without the design of biological adaptations being encoded in the laws of physics, those laws must have certain other properties. The theory of what these properties are is not part of evolution theory proper, and has not been developed, yet without it the neo-Darwinian theory does not fully achieve its purpose of explaining the appearance of design.

To this end I apply Constructor Theory's new mode of explanation to provide an exact formulation of the appearance of design, of no-design laws, and of the logic of self-reproduction and natural selection, within fundamental physics. I conclude that self-reproduction, replication and natural selection are possible under no-design laws, the only non-trivial condition being that they allow digital information to be physically instantiated. This has an exact characterisation in the constructor theory of information. I also show that under no-design laws an accurate replicator requires the existence of a "vehicle" constituting, together with the replicator, a self-reproducer. (Abstract)

Conclusion: I have proved that the physical processes the theory of evolution relies upon are possible under no-design laws, provided that the latter allow for information media (and enough generic resources). Under such laws, accurate self-reproduction can occur, but only via von Neumann's replicator-vehicle logic; and a high fidelity replicator requires an accurate self-reproducer. My argument also highlights that all accurate constructors, under such laws, must contain knowledge - a special abstract constructor - in the form of a recipe, instantiated in a replicator. I have also extended von Neumann's model of the logic of self-reproduction to quantum theory. This informs further investigations of quantum effects in natural and artificial self-reproducers. Constructor theory has also expressed exactly within fundamental physics, the logic of self-reproduction, replication, and natural selection, and the appearance of design. This has promise for a deep unification in our understanding of life and physics. (25)

Mayfield, John. The Engine of Complexity: Evolution as Computation. New York: Columbia University Press, 2013. Reviewed more in Quickening Evolution, a book-length treatment of life’s temporal occasion, selection, and ramification seen as an algorithmic operation and optimization.

Miller, Julian, ed. Cartesian Genetic Programming. Berlin: Springer, 2011. The University of York editor, a cofounder of this method, has a doctorate in nonlinear mathematics. We cite to report an array of inherent natural computations which are in generative effect. An usage which drew our notice is Artificial Intelligence in Peer Review by Maciej Mrowinski, et al at arXiv:1712.01682, Abstract below, who find CGP to aid editorial processes.

Cartesian Genetic Programming (CGP) is a highly effective and increasingly popular form of genetic programming. It represents programs in the form of directed graphs, and a particular characteristic is that it has a highly redundant genotype–phenotype mapping, in that genes can be noncoding. It has spawned a number of new forms, each improving on the efficiency, among them modular, or embedded, CGP, and self-modifying CGP. It has been applied to many problems in both computer science and applied sciences. This book contains chapters written by the leading figures in the development and application of CGP, and it will be essential reading for researchers in genetic programming and for engineers and scientists solving applications using these techniques. It will also be useful for advanced undergraduates and postgraduates seeking to understand and utilize a highly efficient form of genetic programming. (Book)

With the volume of manuscripts submitted for publication growing every year, the deficiencies of peer review (e.g. long review times) are becoming more apparent. Editorial strategies, sets of guidelines designed to speed up the process and reduce editors workloads, are treated as trade secrets by publishing houses and are not shared publicly. To improve the effectiveness of their strategies, editors in small publishing groups are faced with undertaking an iterative trial-and-error approach. We show that Cartesian Genetic Programming, a nature-inspired evolutionary algorithm, can dramatically improve editorial strategies. The artificially evolved strategy reduced the duration of the peer review process by 30%, without increasing the pool of reviewers (in comparison to a typical human-developed strategy). Evolutionary computation has typically been used in technological processes or biological ecosystems. Our results demonstrate that genetic programs can improve real-world social systems that are usually much harder to understand and control than physical systems. (Abstract)

Miralavy, Iliya and Wolfgang Banzhaf. Spatial Artificial Chemistry Implementation of a Gene Regulatory Network Aimed at Generating Protein Concentration Dynamics. Artificial Life. 30/1, 2024. Michigan State University computer scientists provide a mid-2020s cross-integration between complex genomes, protein combinatorics and artificial chemical concepts (search WB). The paper is included in a special retrospective issue on the Artificial Life endeavor since the 1990s. See a lead essay What Is Artificial Life Today, and Where Should It Go? by Alan Dorin and Susan Stepney for more.

Gene regulatory networks are networks of interactions in organisms responsible for determining the production levels of proteins and peptides. Mathematical and computational models of gene regulatory networks have been proposed, some of them rather abstract and called artificial regulatory networks. In this contribution, a spatial model for gene regulatory networks is proposed that incorporates an artificial chemistry to realize the interaction between regulatory proteins called the transcription factors and the regulatory sites of simulated genes. The result is a system that is able to produce complex dynamics similar to those observed in nature. Here an analysis of the shows that such models are evolvable and can be directed toward desired protein dynamics. (Excerpt)

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