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A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
Table of Contents
Genesis Vision
Learning Planet
Organic Universe
Earth Life Emerge
Genesis Future
Recent Additions

III. Ecosmos: A Revolutionary Fertile, Habitable, Solar-Bioplanet, Incubator Lifescape

3. Supramolecular Systems Chemistry

Estrada, Ernesto. What is a mathematician doing…in a chemistry class?.. Foundations of Chemistry. February, 2024. The Institute of Cross-Disciplinary Physics and Complex Systems IFISC (UIB-CSIC) Palma de Mallorca polyscholar is also editor of the Journal of Complex Networks. In this interdisciplinary contribution, describes his experience he scopes out a deep affinity between these dimensions. The guiding theme once again is a sense of common analogies that necessarily infuse each approach.

The way of thinking of mathematicians and chemists in their disciplines seems to have very different levels of abstractions. While the firsts are involved in the most abstract sciences, the seconds are engaged in a mainly experimental field. Yet many luminaries of the mathematics universe have studied chemistry as their subject. Here I make note of mathematicians who were involved in chemistry from a biographical perspective. I found analogies between code-breaking and molecular structure elucidation, inspiration for statistics in quantitative analytical chemistry, and topology in the study of some organic molecules. (Excerpt)

Another coincident way of thinking between an organic chemist and a mathematician is about the importance that patterns play in their research. Chemists search for patterns in the physical properties of the molecule under study, in its chemical reactivity, in groups and motifs. Mathematicians search for numerical patterns of shape, motion, behavior, and so on. Once a pattern is identified either in chemistry or in mathematics, the researcher can proceed to the clarification of the systematic rule which is behind that pattern. (22)

Ghosh, Abhik and Paul Kiparsky. Grammar of the Elements. American Scientist. November-December, 2019. Once in a while, a truly unique contribution comes to light. Here an Arctic University of Norway chemist and a Stanford University linguist, each veteran scholars, make a good case that Dmitri Menddeleev’s periodic table drew inspiration for its form and phrases from Sanskrit. It seems that both he and Otto von Bohtlingk, who wrote a German edition about this ancient Indian script, lived in St. Petersburg in the 1870s and knew each other. Akin to Antoine Lavoisier who used linguistic metaphors, its tabular frame and generative grammar, traced to the 4th century BCE philologist Panini, served as an initial guide for sorting and arraying the 70 or so atomic elements at the time. See also Mendeleev’s Predictions: Success and Failure by Philip Stewart in Foundations of Chemistry (21/1, 2019), Challenges for the Periodic Systems of Elements by Guillermo Restrepo in Chemistry: A European Journal (November 2019), and Mendeleev and earlier The Periodic Table by Subhash Kak at arXiv:0411080. At its 150th anniversary, this deep affinity reveals an innate connection between chemical matter and linguistic forms, so as to infer a textual uniVerse which we peoples seem meant to learn, read and write.

Giuseppone, Nicolas. Toward Self-Constructing Materials: A Systems Chemistry Approach. Accounts of Chemical Research. 45/12, 2012. An overview image for the article depicts a “functional feedback loop” which combines Determinism with Contingency, by way of information cycle in space and time due to (supra)molecular, creative self-organization. The University of Strasbourg, Institut Charles Sadron, systems chemist director adds veracity to natural nonlinear dynamics as they serve to engender life’s embryonic development. But the gist and aim of the work, and laboratory is, by virtue of these insights, to further life’s progress through “designing the next generation of “smart, synergistic” materials’ in the service of people and planet. So might we look to the advent, as broached on occasion, of a “second genesis,” whence the human phenomenon, as we are meant to do, can commence a new intentional creation?

The mechanisms of evolution going from divided, to condensed, then self-organized, and in fine leading to thinking natter are considered as central questions to be ultimately addressed by science. For instance, statistical physics, information theory, nonlinear dynamics, or systems biology, have elaborated theoretical models and experimental probes to describe the emergence of structures (over scale) and their self-organization properties (over time) which in occur in complex systems. Several kinds of complex systems have been intensively studied for their various interests, going from spatial fractals to cellular automata, and from social, gene, or neural networks to living cells. In particular, emergence and self-organization often appear from the collective behavior (integration) of interactions in multicomponent systems, which thus exhibit advanced functionalities that their single components could not produce individually. (2178-2179)

Goh, Garrett, et al. Deep Learning for Computational Chemistry. Journal of Computational Chemistry. 38/16, 2017. Pacific Northwest National Laboratory mathematicians proceed with applications of this AI machine to brain-based revolution to advance studies and formulations for a more sustainable nature. Structural, quantum, material design, and other aspects are considered as we peoples initiate a new atomic creation (although still unawares to us.)

The rise and fall of artificial neural networks is well documented in the scientific literature. Yet, we are now seeing a resurgence of interest in deep learning, a machine learning algorithm based on multilayer neural networks. By providing an overview of the variety of emerging applications of deep neural networks, we highlight its ubiquity and broad applicability to a wide range of challenges in the field, including quantitative structure activity relationship, virtual screening, protein structure prediction, quantum chemistry, materials design, and property prediction. Coupled with the maturity of GPU‐accelerated computing for training deep neural networks and the exponential growth of chemical data on which to train these networks on, we anticipate that deep learning algorithms will be a valuable tool for computational chemistry. (Abstract excerpts)

Grzybowski, Bartosz, et al. From Dynamic Self-Assembly to Networked Chemical Systems. Chemical Society Reviews. 46/5647, 2017. In this Systems Chemistry issue, IBS Center for Soft and Living Matter, UNIST, Korea and AGH University of Science and Technology, Poland (search lead author, who has a doctorate with George Whitesides at Harvard) researchers specify and trace out this 21st century, organically active, reconception via applications of these natural complexity principles. In so doing, it is affirmed that “all forms of life are self-assembling,” which their project then aims to intentionally facilitate and carry forth. One such way would be an avail of “networked DNA.”

Although dynamic self-assembly, DySA, is a relatively new area of research, the past decade has brought numerous demonstrations of how various types of components – on scales from (macro) molecular to macroscopic – can be arranged into ordered structures thriving in non-equilibrium, steady states. At the same time, none of these dynamic assemblies has so far proven practically relevant, prompting questions about the field's prospects and ultimate objectives. The main thesis of this Review is that formation of dynamic assemblies cannot be an end in itself – instead, we should think more ambitiously of using such assemblies as control elements (reconfigurable catalysts, nanomachines, etc.) of larger, networked systems directing sequences of chemical reactions or assembly tasks. (Abstract excerpt)

Grzybowski, Bartosz, et al. Systems Chemistry: A Web Themed Issue. Chemical Communications. 50/14924, 2014. As this field evolves and expands, an Introduction to a collection with papers such as Coupled chemical oscillators and emergent system properties by Irving Epstein and Constitutional self-selection from dynamic combinatorial libraries in aqueous solution through supramolecular interactions by Jordi Solà, et al.

Grzybowski, Bartosz, et al. The ‘Wired’ Universe of Organic Chemistry. Nature Chemistry. 1/1, 2009. Northwestern University chemists compare the sequential history of chemical knowledge since the 1800s to the growing complex networks of the global Internet. This inaugural issue also contains Teetering towards Chaos and Complexity by Bruce Gibb which prods the field, just seven years ago, to attend more to nature’s intrinsic, lively nonlinearity and emergence.

The millions of reactions performed and compounds synthesized by organic chemists over the past two centuries connect to form a network larger than the metabolic networks of higher organisms and rivalling the complexity of the World Wide Web. Despite its apparent randomness, the network of chemistry has a well-defined, modular architecture. The network evolves in time according to trends that have not changed since the inception of the discipline, and thus project into chemistry's future. Analysis of organic chemistry using the tools of network theory enables the identification of most 'central' organic molecules, and for the prediction of which and how many molecules will be made in the future. Statistical analyses based on network connectivity are useful in optimizing parallel syntheses, in estimating chemical reactivity, and more. (Abstract)

Grzykowski, Bartosz, et al. The ‘Wired’ Universe of Organic Chemistry. Nature Chemistry. 1/4, 2009. As the quote details, Northwestern University chemists advocate and document an expansive array of intrinsic, generative, network topologies of chemical reactions. A resource in this regard is the (Luis) Amaral Lab for Complex Systems and Systems Biology at Northwestern.

The millions of reactions performed and compounds synthesized by organic chemists over the past two centuries connect to form a network larger than the metabolic networks of higher organisms and rivalling the complexity of the World Wide Web. Despite its apparent randomness, the network of chemistry has a well-defined, modular architecture. Analysis of organic chemistry using the tools of network theory enables the identification of most 'central' organic molecules, and for the prediction of which and how many molecules will be made in the future. Statistical analyses based on network connectivity are useful in optimizing parallel syntheses, in estimating chemical reactivity, and more. (Abstract, 31)

Hastings, Janna, et al. The Chemical Information Ontology: Provenance and Disambiguation for Chemical Data on the Biological Semantic Web. PLoS One. 6/10, 2011. A group from the European Bioinformatics Institute, Carleton University, Uppsala University, and Cambridge University, that includes Leonid Chepelev and Michel Dumontier, contributes to extensive effort across systems biology and biochemistry, in this infant age of computational sequencing, toward a common, effective “language” to serve such natural translations. A website listed for more online OWL resources is named “semantic chemistry.”

Cheminformatics is the application of informatics techniques to solve chemical problems in silico. There are many areas in biology where cheminformatics plays an important role in computational research, including metabolism, proteomics, and systems biology. One critical aspect in the application of cheminformatics in these fields is the accurate exchange of data, which is increasingly accomplished through the use of ontologies. Ontologies are formal representations of objects and their properties using a logic-based ontology language. (Abstract)

Heylighen, Francis, et al. Chemical Organization Theory as a Universal Modeling Framework for Self-Organization, Autopoiesis and Resilience. pespmc1.vub.ac.be/Papers/COT-applicationsurvey. In 2015, with Shima Beigi and Tomas Veloz, Vrije Universiteit Brussel, Evolution, Complexity & Cognition Group (ecco.vub.ac.be), researchers propose an independent complex dynamic system that appears in similar effect everywhere across nature and society. While the paper opens by saying that John Holland’s complex adaptive systems via many interacting elements is in common use, another substantial version can be drawn from the University of Jena, Germany, biochemist Peter Dittrich and colleagues. By these later theories, a further measure of computational, modular, autopoietic, and resilience qualities can accrue. For original entries by PD, et al see Molecular Codes in Biological and Chemical Reaction Networks (2013) and Thermodynamics of Random Reaction Networks (2015) in PLoS One (search Dittrich) and e.g., Chemical Organization Theory in the Bulletin of Mathematical Biology (69/1199, 2007).

Chemical Organization Theory (COT) is a recently developed formalism inspired by chemical reactions. Because of its simplicity, generality and power, COT seems able to tackle a wide variety of problems in the analysis of complex, self-organizing systems across multiple disciplines. The elements of the formalism are resources and reactions, where a reaction maps a combination of resources onto a new combination. The resources on the input side are “consumed” by the reaction, which “produces” the resources on the output side. Thus, a reaction represents an elementary process that transforms resources into new resources. Reaction networks tend to self-organize into invariant subnetworks, called “organizations”, which are attractors of their dynamics. These are characterized by closure (no new resources are added) and self-maintenance (no existing resources are lost). Thus, they provide a simple model of autopoiesis: the organization persistently recreates its own components. Organizations can be more or less resilient in the face of perturbations, depending on properties such as the size of their basin of attraction or the redundancy of their reaction pathways. Concrete applications of organizations can be found in autocatalytic cycles, metabolic or genetic regulatory networks, ecosystems, sustainable development, and social systems. (Abstract)

Hill, Craig and Djamaladdin Musaev, eds. Complexity in Chemistry and Beyond. Berlin: Springer, 2013. The editors are Emory University chemists, these proceedings from a NATO Science for Peace and Security 2012 conference held in Baku, Azerbaijan. An overview by the University of Augsburg philosopher Klaus Mainzer alludes that such a nascent “supramolecular chemistry,” by way intrinsic self-organization and self-assembly, implies that biology seems to be inherently coded into elementary particulate, atomic matter. By way of these autocatalytic “potentialities” of material systems, an old “creation ex nihilo” does not hold, indeed something rather than nothing is going on in this a quickening cosmos from molecules to minds we have found. For a concurrent accord, see herein Young Sun, Early Earth and the Origins of Life by Muriel Gargaud, et al, which avers the same vitality.

Complexity occurs in biological and synthetic systems alike. This general phenomenon has been addressed in recent publications by investigators in disciplines ranging from chemistry and biology to psychology and philosophy. Studies of complexity for molecular scientists have focused on breaking symmetry, dissipative processes, and emergence. Investigators in the social and medical sciences have focused on neurophenomenology, cognitive approaches and self-consciousness. Complexity in both structure and function is inherent in many scientific disciplines of current significance and also in technologies of current importance that are rapidly evolving to address global societal needs. (Publisher)

The theory of complex dynamical systems is an interdisciplinary methodology to model nonlinear processes in nature and society. In the age of globalization, it is the answer to increasing complexity and sensitivity of human life and civilization (e.g., life science, environment and climate, globalization, information flood). Complex systems consist of many microscopic elements (molecules, cells, organisms, agents, citizens) interacting in nonlinear manner and generating macroscopic order. Self-organization means the emergence of macroscopic states by the nonlinear interactions of microscopic elements. Chemistry at the boundary between physics and biology analyzes the fascinating world of molecular self-organization. Supramolecular chemistry describes the emergence of extremely complex molecules during chemical evolution on Earth. Information dynamics is an important topic to understand molecular self-organization. Besides the methodology of mathematical and computer-assisted models, there are practical and ethical consequences: Be sensible to critical equilibria in nature and society (butterfly effect). Find the balance between self-organization, control, and governance of complex systems in order to support a sustainable future of mankind. (Abstract, “Challenges of Complexity in Chemistry and Beyond,” Klaus Mainzer)

Kais, Sabre, ed. Quantum Information and Computation for Chemistry. Advances in Chemical Physics. Volume 154, 2014. A good example of the current expansion and recast of quantum phenomena by way of a communicative essence, and its melding and merging with other physical, molecular, and biological realms. A typical chapter is Introduction to Quantum Algorithms for Physics and Chemistry by Man-Hong Yung, et al.

This volume of the series explores the latest research findings, applications, and new research paths from the quantum information science community. It examines topics in quantum computation and quantum information that are related to or intersect with key topics in chemical physics. The reviews address both what chemistry can contribute to quantum information and what quantum information can contribute to the study of chemical systems, surveying both theoretical and experimental quantum information research within the field of chemical physics. (Summary)

This chapter introduces the basic concepts of digital quantum simulation. The study of the computational complexity of problems in quantum simulation helps us better understand how quantum computers can surpass classical computers. The chapter briefly summarizes a few important examples of complexity classes of decision problems. Quantum algorithms are procedures for applying elementary quantum logic gates to complete certain unitary transformations of the input state. The steps involved in carrying out a digital quantum simulation consist of three parts: state preparation, time evolution, and measurement of observables. The chapter provides an overview of state preparation and simulation of time evolution. (Yung)

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