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

Recent Additions: New and Updated Entries in the Past 60 Days
Displaying entries 46 through 60 of 120 found.

Cosmomics: A Genomic Source Code in Procreative Effect

Cosmic Code > networks

Boguna, Marian, et al. Network Geometry. arXiv:2001.03241. Six senior complexity scientists including Dmitri Krioukov and Shlomo Havlin offer a January 2020 posting which couldl be a bidecadal capsule of how much studies of nature’s innate node/link multiplex anatomy and physiology has been found in vivifying evidence from physical depths and galactic clusters and to evolutionary bodies, brains, groupings and onto economies and cultures. This entry describes how “fractal self-similarities, diffusion dynamics, and functional modularity” have been found from a chemical-space renormalization to cellular communities across life’s biota, as shown in intricate displays. Into the 2010s, an increasing implication is the presence of an independent, mathematic source in exemplary manifestation at each and every scale and instance. See also Geometric Origins of Self-Similarity in the Evolution of Real Networks by this group at 1912.00704 and Scale-free Networks Revealed from Finite-size Scaling at 1805.09512.

Networks are natural geometric objects. Yet the discrete metric structure of shortest path lengths in a network is not the only reservoir of geometric distances. Other forms of network-related topologies are continuous latent spaces underlying many networks, and the effective geometry induced by dynamical processes. A growing amount of evidence shows that the three approaches are well related. Network geometry is thus quite efficient in discovering hidden symmetries, such as scale-invariance, and other fundamental physical and mathematical properties, along with a variety of applications from the understanding how the brain works to routings in the Internet. Here, we review theoretical and practical developments in network geometry in the last two decades, and offer perspectives on future research for this novel complexity frontier. (Abstract)

Cosmic Code > networks

Chavalarias, David. From Inert Matter to the Global Society: Life as Multi-level Networks of Processes. Philosophical Transactions of the Royal Society B. February, 2020. In this Unifying the Essential Concepts of Biological Networks issue, a Parisian cognitive scientist (bio below) illumes a cosmic to congress synthesis due to the generative activity of self-organization, autopoiesis, biocatalysis, recurrent scales, and more. This view leads a “triple closure” (see Abstract) made up of member components, active relations, and an integral unity. Life’s evolutionary basis and intent is then seen as a constant fulfillment of this iconic, triune whole. A consequentl rise of cerebral cognition, collective intelligence, and cultural learning can also be observed. As this universe to human course proceeds, our global phase is seen to be emerging into a “humanity-organism.” In closing, it is noted that this worldwide advance must not be left to chance, rather a common, informed, popular, concerted effort is imperative to bring to fruition.

A billion years have passed since the first life forms appeared. Since then, life has continued to form complex associations between emergent levels of interconnection. Advances in molecular chemistry and theoretical biology based on a systems view can now conceptualize life’s origins and complexity from three notions of closure: processes, autocatalysis and constraints. This integral paradigm can then trace the physical levels of the organization of matter from physics to biology and society without resorting to reductionism. The phenomenon of life thus becomes a contingent complexification until life emerges as a network of auto-catalytic process networks, organized in a multi-level manner. A living systems approach inevitably reflects on cognition; and on the deep changes that affects humanity by way of our cultural evolution. (Abstract Excerpt)

Humanity, by becoming an ‘organism’, is becoming de facto mortal. Cultural evolution as we used to think of it, is over. It will become more similar to a process of adaptation and learning at the level of humanity that can lead to its disappearance at any time. The new humanity – organism is alone on its evolutionary path and we can ask ourselves if we can afford to have it guided by random trials and errors. The kinds of collective cognition and behaviours that humanity will adopt in this new phase of its existence will determine its chances of survival in the future. (9)

David Chavalarias is the director of the Complex Systems Institute of Paris and Vice-President of the Complex Systems Society. He holds a PhD from the Ecole Polytechnique in cognitive sciences and attended the Ecole Normale Supérieure de Cachan in Mathematics and Computer Sciences. Currently permanent researcher at the National Center for Scientific Research (CNRS) in France, he studies the social and cognitive dynamics, both. His interdisciplinary research includes: quantitative epistemology, information visualization, modelling of the cultural dynamics, socio-semantic networks modelling, scientific discovery processes and cognitive economics.

Cosmic Code > networks

Filan, Daniel, et al. Neural Networks are Surprisingly Modular. arXiv:2003.04881. UC Berkeley and Boston University computer engineers find a way to emphasize and increase the practical presence of these local, clustered concentrations of specific cognitive functions in net topologies, just as biological systems draw upon nested modularities for their development and sustenance. Once again, the tacit assumption is a ready transferability of this independent, iconic source as manifest in connectomic and genomic phenomena.

The learned weights of a neural network are often considered devoid of scrutable internal structure. In order to discern structure in these weights, we introduce a measurable notion of modularity for multi-layer perceptrons (MLPs), and investigate their modular structure as trained on datasets of small images. A "module" as we conceive, is a set of neurons with strong internal connectivity but weak external connectivity. We find that MLPs that undergo training and weight pruning are significantly more modular than random networks. (Abstract excerpt)

Cosmic Code > networks

Kitsak, Maksim. Latent Geometry for Complementary Driven Networks. arXiv:2003.06665. A Northeastern University, Network Science Institute physicist elucidates another innate proclivity that networlds everywhere commonly appear to possess. As the abstract notes, reciprocal forms and/or actions seem to be drawn together so as to conceive a beneficial, more effective whole.

Networks of interdisciplinary teams, biological interactions as well as food webs are examples of networks that are shaped by complementarity principles: connections in these networks are preferentially established between nodes with complementary properties. We propose a geometric framework for this property by first noting that traditional methods which embed networks into latent metric spaces are not applicable. We then consider a cross-geometric representation which (i) follows naturally from the complementarity rule, (ii) is consistent with the metric property of the latent space, (iii) reproduces structural properties of real complementarity-driven networks and (iv) allows for prediction of missing links with accuracy surpassing similarity-based methods. (Abstract excerpt)

Cosmic Code > networks

Kostic, Daniel, et al. Unifying the Essential Concepts of Biological Networks. Philosophical Transactions of the Royal Society B. February, 2020. DR, University of Bordeaux, Claus Hilgetaf, University Medical Center Hamburg, and Marc Tittgemeyer, MPI Metabolism Research introduce a special issue with this integrative title. Its content is composed of both life science and philosophical considerations since both views need join together. For example, see General Theory of Topological Explanations and Explanatory Asymmetry by D. Kostic, Hierarchy and Levels by William Bechtel, Exploring Modularity by Maria Serban, and Network Architectures Supporting Learnability by Perry Zurn and Danielle Bassett, From Inert Matter to Global Society by David Chavalarias and Evolving Complexity by Richard Sole and Sergi Valverde (search for these last three).

Over the last decades, network-based approaches have become highly popular in diverse fields of biology, including neuroscience, ecology, molecular biology and genetics. While these approaches continue to grow very rapidly, some of their conceptual and methodological aspects still require a programmatic foundation. This challenge particularly concerns the question of whether a generalized account of explanatory, organizational and descriptive levels of networks can be applied universally across biological sciences. To this end, this highly interdisciplinary theme issue focuses on the definition, motivation and application of key concepts in biological network science, such as explanatory power of distinctively network explanations, network levels and network hierarchies. (Abstract)

Our organism constantly integrates information about the internal state with external environmental cues to adapt behavioural and autonomic responses to ensure physiological homeostasis. The Translational Neurocircuitry Group investigates how the human brain represents, integrates and prioritizes these internal and external signals to initiate adequate behavioural and physiological responses with a special focus on circuit-level models, metabolic mechanisms and human cognition. (Marc Tittgemeyer)

Cosmic Code > networks

Liu, Chuang, et al. Computational Network Biology. Physics Reports. December, 2019. A seven member international team posted in China, Switzerland and the USA (Ruth Nussinov, National Cancer Institute) provide an 80 page tutorial across scientific techniques and real applications as life’s intricate anatomy and physiology becomes understood by these revolutionary 2010s features.

Biological entities are involved in intricate and complex interactions, in which uncovering the biological information from the network concepts are of great significance. In this review, we summarize the recent developments of this vital, copious field, first introducing various types of biological network structural properties. We then review the network-based approaches, ranging from metrics to machine-learning methods, and how to use these algorithms to gain new insights. We highlight the application in neuroscience, human disease, and drug developments and discuss some major challenges and future directions. (Abstract excerpt)

Cosmic Code > networks

Mokhlissi, Raihana, et al. The Structural Properties and Spanning Trees Entropy of the Generalized Fractal Scale-Free Lattice. Journal of Complex Networks. Online August, 2019. RM, Dounia Lotfi, and Mohamed El Marraki, Mohammed V University, Rabat, Morocco and Joyati Debnath, Winona State University, USA mathematicians post a sophisticated description of nature’s innate geometries. While invisible, their linkages are truly present as they unite and vivify all the overt objects and entities.

Enumerating all the spanning trees of a complex network is theoretical defiance for mathematicians, electrical engineers and computer scientists. In this article, we propose a generalization of the Fractal Scale-Free Lattice and study its structural properties. As its degree distribution follows a power law, we prove that the proposed generalization does not affect the scale-free property. In addition, we use equivalent transformations to count the number of spanning trees in the generalized Fractal Scale-Free Lattice. Finally, in order to evaluate the robustness of the generalized lattice, we compute and compare its entropy with other complex networks. (Abstract)

Dr. Joyati Debnath is a Full Professor of Mathematics and Statistics at Winona State University. She received an M. S. in Pure Mathematics and Ph. D. in Applied Mathematics from Iowa State University. She received numerous Honors and Awards including the Best Teaching Award from Iowa State University, and the Outstanding Woman of Education Award. Dr. Debnath has research interest in the areas of Topological Graph Theory, Integral Transform Theory, Partial Differential Equations and Boundary Value Problem, Associations of Variables, Discrete Mathematics, and Software Engineering Metrics. (WSU page)

Cosmic Code > networks

Siebert, Bram, et al. The Role of Modularity in Self-Organization Dynamics in Biological Networks. arXiv:2003.12311. University of Limerick and University of Bristol theorists including Malbor Asliani post another 2020 example of how much the presence of these complexity features are commonly accepted as a working explanation. But a contradiction remains between this self-assembling natural reality with universal node/link, modular, system viabilities at every and an older “Ptolemaic” paradigm (Brian Greene 2020) seems to be unaware of these revolutionary findings.

Interconnected ensembles of biological entities are some of the most complex systems that modern science has encountered so far. Many biological networks are now known to be constructed in a hierarchical way with two main properties: short average paths that join two distant nodes (neuronal, species, or protein patches) and a high proportion of nodes in modular aggregations. Here we show that network modularity is vital for the formation of self-organising patterns of functional activity. We show that spatial patterns at the modular scale can develop in this case, which may explain how spontaneous order in biological networks follows their modular structures. We test our results on real-world networks to confirm the important role of modularity for macro-scale patterns. (Abstract excerpt)

Cosmic Code > networks

Testolin, Alberto, et al. Deep Learning Systems as Complex Networks. Journal of Complex Networks. Online June, 2019. University of Padova physicists including Samir Suweis exemplify this historic synthesis, two decades into the 21st century, whence many diverse fields come together and reinforce each other. Herein self-organizing complexities are present in both cerebral architectures and physical substrates and thus serve to unite the disparate phases. See also Emergence of Network Motifs in Deep Neural Networks by this group in Entropy (22/204, 2020).

Thanks to the availability of large digital datasets and much computational power, deep learning algorithms can learn representations of data over multiple levels of abstraction. These machine-learning methods have aided challenging cognitive tasks such as visual object recognition, speech processing, natural language understanding and automatic translation. Deep belief networks (DBNs) can also discover intricate structures in large datasets in an unsupervised way. While these self-organizing systems apply within the framework of statistical mechanics, their internal functioning and emergent dynamics remains opaque. In this article, we propose to study DBNs using complex network techniques to gain insights into the structural and functional properties of the computational graph resulting from the learning process. (Abstract edits)

Cosmic Code > networks

Zurn, Perry and Danielle Bassett. Network Architectures Supporting Learnability. Philosophical Transactions of the Royal Society B. February, 2020. In this special Unifying the Essential Concepts of Biological Networks issue, American University, Washington and University of Pennsylvania neuroscientists enter an innovative survey which joins a universe context from its physical, energetic basis with our manifest human neural net phase so as to trace a central essence and pathway of intelligent personal and societal learning and active knowledge. The paper cites the self-similarity of nested hierarchies, modularity, scalar transitions, shared information, metabolism, and more by which to achieve better represented models of this animate evolution. At each instance and stage, the relational, communicative topologies as they join pieces (particles, neurons, creatures) are seen to have a primary significance.

Human learners acquire complex interconnected networks of relational knowledge. The capacity for such learning naturally depends on two factors: the informational structure of the knowledge network and the architecture of a computational brain that encodes and processes it. That is, learning is reliant on integrated networks at both epistemic and computational levels, or the conceptual and neural. Here we discuss emerging work on network constraints on the learnability of relational knowledge, and statistical physics principles of thermodynamics and information theory to offer an explanatory model. We highlight similarities between the learnability of relational networks and the physical constraints on the development of interconnected patterns in neural systems, both leading to hierarchically modular networks. Finally, we broach a unified approach to hierarchies and levels in biological networks by proposing epistemological norms for analysing the computational brain and social epistemes, and for developing pedagogical principles conducive to curious thought. (Abstract excerpt)

Systems Evolution: A 21st Century Genesis Synthesis

Quickening Evolution

Manrubia, Susanna, et al. From Genotypes to Organisms: State of the Art and Perspectives of a Cornerstone in Evolutionary Dynamics. arXiv:2002:00363. Eighteen coauthors including Jose Cuesta, Sebastian Ahnert, Lee Altenbery, Paulien Hogeweg, Ard Louis, and Joshua Payne (search each) post a 44 page composite paper with 383 references from a CECAM (search) workshop at the University of Zaragoza in March 2019. The endeavor was an attempt to meld rapidly moving fields such as RNA and protein structures, gene regulatory and metabolic networks, computational algorithms, synthetic biology and so on as they may come together to explain how a phenotype creature arises or “maps” from a genomic source. A notice of “universal” occurrences is apparent, along with much evidence that generative forces are indeed in play before any selective effects. Into 2020, this is a good example of a filling in and acknowledgement of a “natural genesis” that this website has long sought to document.

Understanding how genotypes map onto phenotypes, fitness, and eventually organisms is a major missing piece in a fully predictive theory of evolution. Though we are far from achieving a complete picture of these relationships, our understanding of simpler aspects such as structures induced in the space of genotypes by sequences traced to molecular genotype-phenotype maps has revealed important facts about the dynamical description of evolutionary processes. Empirical evidence supporting such relevant features as phenotypic bias is growing as well, while the synthesis of concept and experiment leads to questioning the nature of evolutionary dynamics. This work reviews with a critical and constructive attitude our current knowledge of how genotypes map onto phenotypes and organismal functions, and discusses theoretical and empirical avenues to broaden and improve this comprehension. (Abstract excerpt)

In other words, natural selection can only act on variation that has been pre-sculpted by the GP map. (14) We have identified a patchwork of processes that in principle are able to shape the variational properties of the GP map for phenotypes at the level of whole organisms, where complex integration leaves us unable to derive the properties from physical first-principles. This is an area in which evolutionary theory needs much greater development. At levels of complexity where reductionist modelling is impossible, we have surveyed efforts that attempt to analyse how evolutionary processes shape the GP map. The body of results described, while not a fully fleshed-out theory, is sufficient to demonstrate that this process-based approach can inform a research program for the GP map at the whole organism level. (32)

Quickening Evolution

Nghe, Philippe, et al. Predicting Evolution Using Regulatory Architecture. Annual Review of Biophysics. Volume 49, 2020. Seven bioscientists based in France, the Netherlands, UK, and USA consider how the latest convergent flow of systems, genomic, network, and more theories and methods, along with new instruments, seem to suggest an inherent, directional predictability for life’s long proactive development.

The causes of evolutionary constraint have remained somewhat elusive. Recently, a range of innovative approaches have leveraged mechanistic information on regulatory networks and cellular biology. These methods combine systems biology with population and single-cell models and new genetic tools which have been applied to a range of complex cellular functions and engineered networks. We review these developments, which are revealing the physical causes of epistasis at different levels of biological organization such as molecular recognition, a single regulatory network, and between networks. These advances seem to provide new indications of predictable features of evolutionary constraint. (Abstract excerpt)

Quickening Evolution > Systems Biology

Gazestani, Vahid and Nathan Lewis. From Genotype to Phenotype: Augmenting Deep Learning with Networks and Systems Biology. Current Opinion in Systems Biology. 15/68, 2019. UC San Diego bioscientists consider a timely synthesis of these genetic, systems, network, and AI aspects and methods, which appear to have a natural, innate affinity with each other. A subsection is Generalizability, Transferability, and Interpretability as our worldwise learning phase proceeds to reconstruct how we came to be, so as to better go forward.

Cells, as complex systems, consist of diverse interacting biomolecules arranged in dynamic hierarchical modules. Recent advances in deep methods now allow one to encode this existing knowledge in the architecture of the learning procedure. By encoding biological networks this way, one can develop flexible techniques that propagate information through the molecular networks to successfully classify cell states. Moreover, this flexibility can be harnessed to model the hierarchical structure of real biological systems, efficiently converting gene-level data to pathway-level information with an impact on the cell phenotype. (Abstract excerpt)

Quickening Evolution > Biosemiotics

Marcello, Barbieri. The Semantic Theory of Language. Biosystems. January, 2020. The University of Ferrera embryologist has been a veteran contributor (search) to the biosemiotic view that living systems are most distinguished by a series of code-like activities. But this vital perspective still seems to be in a formative phase as it morphs into various interpretations. The paper opens by saying that since Aristotle language has served to link sounds and meaning by way of phonetic and cognitive aspects. As the Abstract cites, recently N. Chomsky added a nuance that Marcello doesn’t approve. In his broader scope, harking back to C. Peirce (1839-1914), the founder of a semiotic philosophy, a further revision is proposed to sort all this out into the 2020s. An emphasis is put on three main genetic, neural and symbolic codes, which are then coordinated with the unique human feature that babies are born in such an immature state that they require a long post period to mature.

Traditional linguistics was based on the idea that language links sounds and meaning. Later on due to Noam Chomsky, this view has been replaced by the idea that children learn a language because of an innate mechanism to do so. But there is still no evidence that such a device exists. Another process is the ability of higher animals to interpret what goes on in the world, which is not based on fixed rules but on a process that Charles Peirce called abduction. This allows us to generalize into the semantic view of language, a theory that language is an activity which gives meaning to sounds. This can give us a new framework for studying the origin of language without resorting to a certain device. Herein, the origin of language is compared with the origin of life and of mind, because those mega-transitions generated the three code families that we find in Nature – organic neural and cultural. (Abstract excerpt)

Abductive reasoning (also called abduction, or abductive inference), is a form of logical inference which starts with an observation or set of observations and then seeks to find the simplest and most likely explanation for the observations. This process, unlike deductive reasoning, yields a plausible conclusion but does not positively verify it.

Quickening Evolution > Intel Ev

Csermely, Peter, et al. Learning of Signaling Networks. arXiv:2001.11679. We cite this paper by five Semmelweis University, Budapest system scientists as an example of how cerebral facilities can be easily grafted onto and evident in all manner of genetic and metabolic anatomy and physiology, because they naturally spring from and manifest one, same source. By this perception, life’s long evolutionary development can increasingly appear as an oriented encephalization and cosmic education.

Molecular processes of neuronal learning have been well-described. However, learning mechanisms of non-neuronal cells have not been fully understood. Here, we discuss molecular mechanisms of cellular learning, including conformational memory of intrinsically disordered proteins and prions, signaling cascades, protein translocation, RNAs, and chromatin memory. We hypothesize that these processes constitute the learning of signaling networks and correspond to a generalized Hebbian learning process of single, non-neuronal cells. We then discuss how cellular learning may open novel directions in drug design and inspire new artificial intelligence methods. (Abstract)

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