
IV. Ecosmomics: An Independent, UniVersal, Source CodeScript of Generative Complex Network Systems2. Biteracy: Natural Algorithmic Computation Raffaele, Giancarlo, et al. DNA Combinatorial Messages and Epigenomics. Theoretical Computer Science. Online July, 2018. With regard to chromatin organization and nucleosome occupancy in eukaryotic genomes, University of Palermo and IBM Watson Research computational biologists come to this ongoing project of parsing nature’s cosmos to children genetic program via this algorithmic (algorithome) realm. They emphasize how a general computational approach is especially apt going forward. All told, our human phenomenon may be the way that a selfdeciphering, sequencing, genesis uniVerse learns to read, realize and continue forth its own code. Epigenomics is the study of modifications on the genetic material of a cell that do not depend on changes in the DNA sequence, since those latter involve specific proteins around which DNA wraps. The result is that Epigenomic changes have a fundamental role in the proper working of each cell in Eukaryotic organisms. A particularly important aspect is the study of chromatin, a fiber composed of a DNAprotein complex. In more than thirty years of research in this area, Mathematics and Theoretical Computer Science have gained a prominent role, in terms of modeling and mining. Starting from some very basic notions of Biology, we illustrate recent advances on the organization and dynamics of chromatin. Then, we review contributions by Combinatorial and Informational Methodologies to the understanding of mechanisms determining the 10 nm fiber. (Abstract excerpt)
Roberts, Siobhan.
The Lasting Lessons of John Conway’s Game of Life.
New York Times.
December 28,
2020.
A science journalist and author of a 2015 Genius at Playbiography of the Princeton University mathematician reviews his prime computational innovation along with its ongoing implications. Sadly, John Conway passed away at age 82 in April 2020 from the COVID virus. When at Cambridge University in 1972 he sent several puzzles to Martin Gardner who was then an editor for Scientific American. His Game of Life entry was reviewed in the October issue, and has grown in popularity ever since. The game of life was simple: Place any configuration of cells on a grid, then watch what transpires according to three rules that dictate how the system plays out. Birth rule: An empty, or “dead,” cell with precisely three “live” neighbors (full cells) becomes live. Death rule: A live cell with zero or one neighbors dies of isolation; a live cell with four or more neighbors dies of overcrowding. Survival rule: A live cell with two or three neighbors remains alive. With each iteration, some cells live, some die and “Lifeforms” evolve, one generation to the next. (S. Roberts) Rondelez, Yannick and Damien Woods, eds. DNA Computing and Molecular Programming. Switzerland: Springer, 2016. The Lecture Notes in Computer Science 9818 Proceedings of the 22th International Conference on this title subject held in Munich, September 2016, which is summed up as: Research in DNA computing and molecular programming draws together mathematics, computer science, physics, chemistry, biology, and nanotechnology to address the analysis, design, and synthesis of informationbased molecular systems. For example, some entries are Hierarchical SelfAssembly of Fractals, A Scheme for Molecular Computation, and Chemical Reaction Network Implementations. Search also David Doty in Systems Chemistry for more. Rozenberg, Grzegorz, ed. Handbook of Natural Computing. Berlin: Springer, 2014. The editorinchief is a Polish computer scientist who has inspired this theoretical movement for decades. An Introduction he wrote is available on the Springer site, quoted below. The main topical areas covered are Cellular Automata, Neural, Evolutionary, Molecular, Quantum Computation, and Broader Perspectives. Natural Computing is the field of research that investigates humandesigned computing inspired by nature as well as computing taking place in nature, that is, it investigates models and computational techniques inspired by nature, and also it investigates, in terms of information processing, phenomena taking place in nature. Examples of the first strand include neural computation inspired by the function of the brain, evolutionary computation inspired by Darwinian evolution, cellular automata inspired by intercellular communications, swarm intelligence inspired by the behavior of groups of organisms, artificial immune systems, membrane computing, and amorphous computing inspired by morphogenesis. The second strand is represented by investigations into, among othrs, the computational nature of selfassembly, the computational nature of developmental processes, the computational nature of biochemical reactions, bacterial communication, brain processes, and the systems biology approach to bionetworks where cellular processes are treated in terms of communication and interaction. SalcedoSanz, Sancho. Modern MetaHeuristics Based on Nonlinear Physics Processes. Reports on Progress in Physics. Vol. 655, 2016. A technical contribution by a Universidad de Alcala, Madrid, computer scientist which gives the project of distilling natural search and optimize algorithms a deeper physical basis. While previous efforts draw from biology and evolution, see XinShe Yang herein, this study shows how the fundamental selforganized complexities can be an equally valuable source. For example, Boltzmann distribution, fractal structure generation, vortex search, coral reefs (abstract below) simulated annealing, Big BangBig Crunch (see abstract), galactic swarm, electromagnetic algorithms, and more, are described. These theories are implying a Darwinianlike cosmos, a metaheuristic universe, which spawns prolific varieties so that goodenough candidates can be selected. One then wonders, does it extend to myriad orbital planets such as our own, who need to choose and affirm by their own volition? Metaheuristic algorithms are problemsolving methods which try to find goodenough solutions to very hard optimization problems, at a reasonable computation time, where classical approaches fail, or cannot even been applied. Many existing metaheuristics approaches are natureinspired techniques, which work by simulating or modeling different natural processes in a computer. Historically, many of the most successful metaheuristic approaches have had a biological inspiration, such as evolutionary computation or swarm intelligence paradigms, but in the last few years new approaches based on nonlinear physics processes modeling have been proposed and applied with success. Nonlinear physics processes, modeled as optimization algorithms, are able to produce completely new search procedures, with extremely effective exploration capabilities in many cases, which are able to outperform existing optimization approaches. In this paper we review the most important optimization algorithms based on nonlinear physics, how they have been constructed from specific modeling of a real phenomena, and also their novelty in terms of comparison with alternative existing algorithms for optimization. (Abstract excerpt) Sarma, Gopal. Reductionism and the Universal Calculus. arXiv:1607.06725. A computer scientist and philosopher now in medical school (bio below) suggests that Leibniz’s prescience to seek and decipher a natural programmic source was mostly set aside because of a later scientific emphasis upon a reductive method, which then missed this quality. In the seminal essay, "On the unreasonable effectiveness of mathematics in the physical sciences," physicist Eugene Wigner poses a fundamental philosophical question concerning the relationship between a physical system and our capacity to model its behavior with the symbolic language of mathematics. In this essay, I examine an ambitious 16th and 17thcentury intellectual agenda from the perspective of Wigner's question, namely, what historian Paolo Rossi (Logic and the Art of Memory 2002) calls "the quest to create a universal language." While many elite thinkers pursued related ideas, the most inspiring and forceful was Gottfried Leibniz's effort to create a "universal calculus," a pictorial language which would transparently represent the entirety of human knowledge, as well as an associated symbolic calculus with which to model the behavior of physical systems and derive new truths. I suggest that a deeper understanding of why the efforts of Leibniz and others failed could shed light on Wigner's original question. (Abstract) Scalise, Dominic and Rebecca Shulman. Emulating Cellular Automata in Chemical ReactionDiffusion Networks. Natural Computing. 15/2, 2016. In an issue based on the DNA Computing 2014 conference (Google), Johns Hopkins computational theorists contribute to frontier realizations that along with genetic phenomena, chemical interactions can similarly be explicated by means of such complex dynamic Turinglike programs, from which orderly structures result. Search also Rondelez above, and Doty in Systems Chemistry for more. Although these advances seem to evince a material cosmic genesis which is brought into complex, vivifying emergence by virtue of an intrinsic, genomelike program. Chemical reactions and diffusion can produce a wide variety of static or transient spatial patterns in the concentrations of chemical species. Here we show that given simple, periodic inputs, chemical reactions and diffusion can reliably emulate the dynamics of a deterministic cellular automaton, and can therefore be programmed to produce a wide range of complex, discrete dynamics. We describe a modular reaction–diffusion program that orchestrates each of the fundamental operations of a cellular automaton: storage of cell state, communication between neighboring cells, and calculation of cells’ subsequent states. Reaction–diffusion based cellular automata could potentially be built in vitro using networks of DNA molecules that interact via branch migration processes and could in principle perform universal computation, storing their state as a pattern of molecular concentrations, or deliver spatiotemporal instructions encoded in concentrations to direct the behavior of intelligent materials. (Abstract excerpts) Siddique, Nazmul and Hojjat Adeli. Nature Inspired Computing. Cognitive Computation. 7/6, 2015. Akin to XinShe Yang herein, Ulster University, Londonderry and Ohio State University informatics researchers gather and introduce an array of physicsbased and biologybased algorithms that have arisen in the 21st century. In addition, the associated fields of neural networks, evolutionary computing and fuzzy logic are reviewed. Siddique, Nazmul and Hojjat Adeli. PhysicsBased Search and Optimization: Inspirations from Nature. Expert Systems. 33/6, 2016. In this Wiley journal, Ulster University, Londonderry and Ohio State University computer scientists gather for the first time a broad survey of dynamic iterative phenomena which occur even across cosmic and material realms, akin to many biological and behavioral examples cited herein (Yang). The novel insight is that nature’s universal (Darwinian) evolutionary process of myriad candidates from which a relative optimum result or condition is winnowed and selected can also be seen in universal effect. See, for example, Black Hole: A New Heuristic Optimization for Data Clustering by Abdolreza Hatamlou in Information Sciences (222/175, 2013). This paper presents a review of recently developed physics‐based search and optimization algorithms that have been inspired by natural phenomena. They include Big Bang–Big Crunch, black hole search, galaxy‐based search, artificial physics optimization, electromagnetism optimization, charged system search, colliding bodies optimization, and particle collision algorithm. (Abstract) Sloss, Andrew and Steven Gustafson. 2019 Evolutionary Algorithm Review. arXiv:1906.08870. Bellevue, WA software scientists post a thorough survey as the field of artificial intelligence, broadly conceived, becomes ever more biological in its basis. By turns, life’s genetically programmed development is broached as an “Idealized Darwinism.” Section 5.1 is an AutoConstructive Evolution, while 5.2 is Deep Neuroevolution and 5.3 SelfReplicating Neural Networks. In this review, we explore a new taxonomy of evolutionary algorithms and classifications that look at five main areas: the ability to manage the control of the environment with limiters, how to explain and repeat the search process, understandings of input and output causality within a solution, the ability to manage algorithm bias due to data or user design, and lastly, and how to add corrective measures. As many reviews of evolutionary algorithms exist, after motivating this new taxonomy, we briefly classify a broad range of algorithms and identify areas of future research. (Abstract excerpt) Smith, Eric and Supriya Krishnamurthy. Symmetry and Collective Fluctuations in Evolutionary Games. Bristol, UK: IOP Publishing, 2015. Santa Fe Institute and Stockholm University physicists engage a technical survey of Darwinian selectivity by way of game theories, an approach defined as the mathematics of interactions, transmission, and information, which engender statistical adaptive, Bayesian responses. A closing chapter then sees the intent and result as an evolving individuality. Sommaruga, Giovanni and Thomas Strahm, eds. Turing’s Revolution. Switzerland: Birkhauser, 2015. A follow up volume to the Alan Turing’s 2012 centenary collections (search his name) which situates computational theories in a continuity from AlKhwarizmi’s (c. 780850) algorithms to Leibniz’s (16461716) universal calculus to our 21st century global Internet society. By this view, the historic inklings of a deep mathematical source that is programmatic in kind can at last reach a definitive articulation. A persistent incentive (also for the great work alchemical project) was that if a natural code could be discerned, human beings could avail to make a better life and world. A theme running through the book is then a creative universe that somehow computes itself into being and becoming, wherein human beings have a central participatory role.
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