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

2. Biteracy: Natural Algorithmic Computation

Moya, Andres. The Calculus of Life: Towards a Theory of Life. Berlin: SpringerBriefs in Biology, 2015. An edition in Springer Briefs in Biology by a Universidad de los Andes, Bogota professor of bioeconomics with a 2013 doctorate from UC Davis. As Moya’s website notes, his work divides between these scientific frontiers and avail to mitigate the chronic social violence of Columbia. At this late frontier, the endeavor is to move beyond an old view of chance and tinkering via systems biology to an innate natural logic and computation. A Turing-type algorithmic mathematics is then engaged from biochemicals to cellular dynamics.

This book explores the exciting world of theoretical biology and is divided into three sections. The first section examines the roles played by renowned scientists such as Jacob, Monod, Rosen, Turing, von Bertalanffy, Waddington and Woodger in developing the field of theoretical biology. The second section, aided with numerous examples, supports the idea that logic and computing are suitable formal languages to describe and understand biological phenomena. The third and final section is, without doubt, the most intellectually challenging and endeavors to show the possible paths we could take to compute a cell - the basic unit of life - or the conditions required for a predictive theory of biological evolution; ultimately, a theory of life in the light of modern Systems Biology. The work aims to show that modern biology is closer than ever to making Goethe's dream come true and that we have reached a point where synthetic and analytical traditions converge to shed light on the living being as a whole.

My current projects focus on the psychological consequences of violence and their implications for economic behavior and poverty dynamics, but also include an impact evaluation of a rural development project, the design of an index insurance program for small coffee farmers, and the design and evaluation of the land restitution program for victims of violence in Colombia. (AM webpage)

Navlakha, Saket and Ziv Bar-Joseph. Algorithms in Nature: The Convergence of Systems Biology and Computational Thinking. Molecular Systems Biology. 7/Art.546, 2011. Along with other entries in this new section, Carnegie Mellon University computer scientists advance this cross-fertilization of evolutionary and genetic programs as they inform their field while biological science is changing by way of nonlinear dynamics. The authors have also posted a website, www.algorithmsinnature.org, with visuals and resources, see quote below.

Computer science and biology have enjoyed a long and fruitful relationship for decades. Biologists rely on computational methods to analyze and integrate large data sets, while several computational methods were inspired by the high-level design principles of biological systems. Recently, these two directions have been converging. In this review, we argue that thinking computationally about biological processes may lead to more accurate models, which in turn can be used to improve the design of algorithms. We discuss the similar mechanisms and requirements shared by computational and biological processes and then present several recent studies that apply this joint analysis strategy to problems related to coordination, network analysis, and tracking and vision. We also discuss additional biological processes that can be studied in a similar manner and link them to potential computational problems. (Abstract)

The mutual relationship between biology and computer science goes back a long time. However, until recently, the application of computational methods to study biological processes and the influence of biological systems on the design of algorithms remained mostly separate. The former involved developing computational methods to solve specific biological problems, while the latter primarily viewed biological systems as motivation to derive new optimization methods. With the improvement in our ability to study detailed aspects of molecular, cellular, and organism-level biological systems, it is now possible to unite these two directions. This direction involves thinking about biological processes as algorithms that nature has designed to solve difficult real- world problems under a variety of constraints and conditions. (8)

Computer science and biology have shared a long history together. For many years, computer scientists have designed algorithms to process and analyze biological data (e.g. microarrays), and likewise, biologists have discovered several operating principles that have inspired new optimization methods (e.g. neural networks). Recently, these two directions have been converging based on the view that biological processes are inherently algorithms that nature has designed to solve computational problems. This website documents new studies that have taken a joint computational-biological approach to study the algorithmic properties of biological processes across all levels of life (molecular, cellular, and organism). (Website)

Neagu, Daniel. Special Issue on Computational Intelligence Algorithms and Applications. Soft Computing. 20/2921, 2016. The University of Bradford, UK, editor introduces this edition. Typical entries are Handwritten Chinese Character Recognition, and Ant Colony Optimization. We note this journal, see citation below, to record the growing perception of algorithms everywhere, which increasingly become an evolutionary arrow.

In computer science, soft computing (sometimes referred to as computational intelligence, though CI does not have an agreed definition) is the use of inexact solutions to computationally hard tasks such as the solution of NP-complete problems, for which there is no known algorithm that can compute an exact solution in polynomial time. Soft computing differs from conventional (hard) computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty, partial truth, and approximation. In effect, the role model for soft computing is the human mind. The principal constituents of Soft Computing (SC) are Fuzzy Logic (FL), Evolutionary Computation (EC), Machine Learning (ML) and Probabilistic Reasoning (PR), with the latter subsuming belief networks, chaos theory and parts of learning theory. (Wikipedia)

Newman, Stuart. Form, function, mind: what doesn't compute (and what might). arXiv:2310.13910. This latest paper (search) by the New York Medical College, Valhalla, NY biophilosopher has several notable aspects. It is a review of his endeavors to discern further structural and physical influences which are involved in life’s cellular development. In 2023, he worries over shifting views of just how computational is biological phenomena, in confluence with genetic sources. But however this vital domain sorts out, our new procreative ecosmos is quite graced by some manner of procreative program.

The applicability of computational and dynamical systems models to organisms is scrutinized, using examples from developmental biology and cognition. Organic morphogenesis is dependent on the inherent material properties of tissues, a non-computational modality, but cell differentiation, which utilizes chromatin-based revisable memory banks and program-like function-calling, has a quasi-computational basis. Multi-attractor dynamical models are argued to be misapplied to global properties of development. Proposals are made for treating brains and other nervous tissues as novel forms of excitable matter with inherent properties which enable the intensification of cell-based basal cognition capabilities present throughout the tree of life. (Excerpt)

The foregoing text introduces this essay on the bases of biological form and function and their relation to computation. My main claim is that key aspects of living organisms, their morphogenesis (generation of form), and aspects of sensory and cognitive capacities, are expressions of the inherencies of the materials of which they are composed. Other features, including the differentiation of specialized cells and retrieval of stored patterns in the brain, utilize quasi-computational storage, data addressing and function calling processes. (3-4)

Nichol, Daniel, et al. Model Genotype-Phenotype Mappings and the Algorithmic Structure of Evolution. Journal of the Royal Society Interface. 16/20190332, 2019. Oxford University and H. Lee Moffitt Cancer Center, FL computer scientists and mathematical oncologists including Peter Jeavons describe an advanced complex systems biology method which joins cellular components into a dynamic synthesis from genes to metabolism. Then a novel program-like factor is brought into play to better quantify and express the metastasis invasions. To so reflect, from our late vantage Earth life’s contingent evolution seems yet to reach a global cumulative knowledge which can be fed back to contain, heal and prevent. We are given to perceive some manner of palliative, self-medicating procreation, which seems meant to pass on to our intentional continuance. What an incredible scenario is being revealed to us.

Cancers are complex dynamic systems that undergo evolution and selection. Personalized medicine in the clinic increasingly relies on predictions of tumour response to one or more therapies, which are complicated by the phenotypic evolution of the tumour. The emergence of resistant phenotypes is not predicted from genomic data, since the relationship between genotypes and phenotypes, termed genotype–phenotype (GP) mapping, is neither injective nor functional. We review mapping models within a generalized evolutionary framework that relates genotype, phenotype, environment and fitness. The GP-mapping provides a pathway for understanding the potential routes of evolution taken by cancers, which will be necessary knowledge for improving personalized therapies. (Abstract excerpt)

Olhede, Sofia and P. J. Wolfe. The Growing Ubiquity of Algorithms in Society. Philosophical Transactions of the Royal Society A. Vol.376/Iss.2128, 2018. University College London and Purdue University computer scientists introduce an issue of papers from a discussion meeting about this invisible but major role that computational methods are taking on in 21st century societies. Topics such as the governance of data, transparency of algorithms, legal and ethical frameworks for automated decision-making and public impacts of hidden programs are considered for both pro and con aspects.

Palazzi, Maria, et al. Online Division of Labour: Emergent Structures in Open Source Software. Nature Scientific Reports. 9/13890, 2019. Five Internet Interdisciplinary Institute (IN3), Universitat Oberta de Catalunya computer scientists cleverly apply complex system phenomena to the multi-player process of software code development. By so doing they find this artificial field to exhibit the same features of self-organization, diverse task allotment, a scalar, nested structure and more as every other natural and social realm. Once again, another life-like domain with this common vitality is revealed, which altogether strongly implies the presence of a natural, seed-like, generative program.

The development Open Source Software depends on the participation and commitment of volunteer developers to progress on a particular task. Several strategies are in effect, but little is known on how these diverse groupings self-organise to work together: any number of contributors can join in a decentralised, distributed, and asynchronous manner. It is helpful to then see some efficient hierarchy and division of labour must be in place within human biological and cognitive limits. We analyze popular GitHub open source projects with regard to three key properties: nestedness, modularity and in-block nestedness. These typify the emergence of heterogeneities among contributors, subgroups working on specific files, and the whole activity. From a complex network perspective, our conclusions create a link between bio-cognitive constraints, group formation and online working environments. (Abstract)

Papadimitriou, Christos. Algorithms, Complexity, and the Sciences. Proceedings of the National Academy of Sciences. 111/15881, 2014. The Simons Institute for the Theory of Computing, UC Berkeley, researcher is a leading spokesperson for perceptions that nature is actually suffused by iterative programs from which physical matter and life’s quickening evolution result. By this general entry, one could discern two broad phenomenal realms of a mathematical program-like source and a manifest complex reality. When we wonder might they be imagined as genotype and phenotype?

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 on-going 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 self-deciphering, 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 DNA-protein 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)

Richardson, Alex, et al. Learning spatio-temporal patterns with Neural Cellular Automata. arXiv:2310.14809. University of Edinburgh biophysicists including Richard Blythe present a latest program iteration for more effective pattern recognition by way of these mathematical computations. The entry could be in Natural Algorithms, or Earthifical Intelligence. And as one logs in, it may seem that we at work are forming an actual global brain facility.

Neural Cellular Automata (NCA) are a powerful combination of machine learning and mechanistic modelling. We train NCA to learn complex dynamics from time series of images and Partial Differential Equations (PDE) trajectories. Our method is designed to identify underlying local rules that govern large scale emergent behaviours. We extend NCA to view structures within th(search)e same system, as well as learning rules for Turing pattern formation in nonlinear (PDEs). (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 on-going 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 first quote cites the simple automata algorithm sequence. By running these rules, one can generate all manner of complex life forms as they self-organize into individualities, divisions of labor, groupings, and more. For a wider survey, Roberts asked computer scholars such as Susan Stepney, Stephen Wolfram, Daniel Dennett and Rudy Rucker for their thoughts, we include other comments below. But an overall mindset or interpretation seems to pervade that such natural programs are ultimately unpredictable. In our current mindset a reliable, thematic guidance is not seen to be written in, nor does it exist.

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 “Life-forms” evolve, one generation to the next. (S. Roberts)

Complexity arises from simplicity! We are used to the idea that anything complex must arise out of something more complex. But the Game of Life shows us that complex virtual “organisms” arise out of the interaction of a few simple rules. (Brian Eno)

In this moment in time, it’s important to emphasize that inherent unpredictability is a feature of life in the real world as well as in the Game of Life. We have to figure out ways to flourish in spite of the vicarious uncertainty we constantly live with. (Melanie Mitchell)

The name Conway chose — the Game of Life — frames his invention as a metaphor. But I’m not sure that he anticipated how relevant Life would become, and that in 50 years we’d all be playing an emergent game of life and death. (William Poundstone)

In normal times, we can keep building stuff one component upon another, but in harder times like this pandemic, we need something that is more resilient and can prepare for the unpreparable. That would need changes in our “rules of life,” which we take for granted. (Bert Chan – see his Lenia and Expanded Universe paper above for extensive displays of this cellular method.)

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 information-based molecular systems. For example, some entries are Hierarchical Self-Assembly of Fractals, A Scheme for Molecular Computation, and Chemical Reaction Network Implementations. Search also David Doty in Systems Chemistry for more.

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