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A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
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Recent Additions: New and Updated Entries in the Past 60 Days
Displaying entries 76 through 90 of 139 found.


Cosmomics: A Genomic Source Code in Procreative Effect

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

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)

Quickening Evolution > Intel Ev

Hasson, Uri, et al. Direct Fit to Nature: An Evolutionary Perspective on Biological and Artificial Neural Networks. Neuron. 105/3, 2020. In another example of cerebral cognition methods being readily applied everywhere, Princeton University neuroscientists point out how brain-based topologies and operations can have analytic utility in many other areas. Section headings include Interpolation and Extrapolation, Generalization Based on Partial and Big Data, and The Power of Adaptive Fit in Evolution. As the quotes allude, parallels can then be drawn between human cerebration and life’s neoDarwinian course whence organisms via sensory apparatus and activities must find a good enough way to survive and evolve. See also A Critique of Pure Learning and What Artificial Neural Networks can Learn from Animal Brains by Anthony Zador in Nature Communications (10/3770, 2019).

Evolution is a blind fitting process by which organisms become adapted to their environment. Does the brain use similar brute-force fitting processes to learn how to perceive and act upon the world? Recent advances in artificial neural networks have exposed the power of optimizing millions of synaptic weights over millions of observations to operate robustly in real-world contexts. These models do not learn simple, human-interpretable rules or representations of the world; rather, they use local computations to interpolate over task-relevant manifolds in a high-dimensional parameter space. Similar to evolutionary processes, over-parameterized models can be simple and parsimonious, as they provide a versatile, robust solution for learning a diverse set of functions. This new family of direct-fit models are a radical challenge to many of the theoretical assumptions in psychology and neuroscience. (Abstract)

Evolution Is an Iterative Optimization Process over Many Generations: Evolution by natural selection is a mindless optimization process by which organisms are adapted over many generations according to environmental constraints. This artistic rendition of the phylogenetic tree highlights how all living organisms on Earth can be traced back to the same ancestral organisms. Humans and other mammals descend from shrew-like mammals that lived 150 million years ago; mammals, birds, reptiles, amphibians, and fish share a common ancestor; and all plants and animals derive from bacteria-like microorganisms that originated more than 3 billion years ago. (Figure 3, evogeneao.com)

Earth Life Emergence: Development of Body, Brain, Selves and Societies

Earth Life > Common Code

Amgalan, Anar, et al. Unique Scales Preserve Self-Similar Integrate-and-Fire Functionality of Neuronal Clusters. arXiv:2002.10568. SUNY Stony Brook and UM Amherst computational neuroscientists including Hava Siegelmann post a strongest statement to date of the actual presence of a “functional scale-invariance or fractality” which spans its dynamic multiplex architecture. In regard, here is another current affirmation of a microcosmic instantiation of nature’s universal genetic complexities in our very own cerebral faculty.

Identifying the brain's neuronal cluster size as nodes in a network computation is critical to both neuroscience and artificial intelligence. Experiments support many forms and sizes of neural clustering, while neural mass models (NMM) assume scale-invariant functionality. Here, we use simulations within a fMRI network to show that a brains stay structurally self-similar continuously across scales. As such, we propose a coarse-graining of network of neurons to ensemble-nodes, with multiple spikes making up its ensemble-spike, and time re-scaling factor defining its ensemble-time step. The fractal-like spatiotemporal structure and function that emerges allows strategic choices across experimental scales for computational modeling, along with regulatory constraints on developmental and/or evolutionary "growth spurts" in brain size. (Abstract excerpt)

Earth Life > Common Code

Keil, Petr, et al. Macroecological and Macroevolutionary Patterns Emerge in the Universe of GNU/Linux Operating Systems. Ecography. 41/11, 2018. When we first posted this section in the early 2000s, any notice of environmental regularities was sparsely evident. In these later 2010s, eight European theoretical ecologists based at the German Centre for Intergrative Biodiversity Research, Leipzig not only aver their wide, constant presence, indeed an untangled bank, but go on to find a cross-affinity with computer software. A true universality across nature’s diversities is becoming patently apparent. See also Evolution in the Debian GNU/Linux Software Network: Analogies and Differences with Gene Regulatory Networks by Pablo Villegas, et al in the Journal of the Royal Society Interface (February 2020) which cites this paper.

What leads to classically recognized patterns of biodiversity remains an open question. Here, we employ analogies between GNU/Linux operating systems, and biodiversity. We demonstrate that patterns of the Linux universe generally match macroecological patterns. Moreover, the composition of functional traits (software packages) exhibits significant phylogenetic signal. The emergence of macroecological patterns across Linux suggests that the patterns are produced independently of the system identity, which points to the possibility of non‐biological drivers of fundamental biodiversity patterns. (Abstract excerpt)

To explain these patterns, we can invoke uniquely ecological and evolutionary processes: the patterns could be an outcome of assembly rules, natural selection, behavior, species interactions, or interplay between specific functional traits and environments. However, it has been demonstrated that some of the patterns are not unique to ecological and evolutionary systems, and often emerge in other complex systems. Examples are: species‐abundance distributions of music festival setlists, frequency distributions of components of software, latitudinal gradients of language diversity, species–area relationships in corporations, industrial codes, and minerals. However, structural constraints are not the only way that biological and non‐biological systems can resemble one another – similarity may also arise from analogous underlying processes. (2)

Earth Life > Common Code

Schwab, Julian, et al. Concepts in Boolean Network Modeling. Computational and Structural Biotechnology Journal. March, 2020. Ulm University system biochemists contribute some latest verifications of Stuart Kauffman’s first 1969 notice that living nature can be described by these mathematical topologies. The paper reviews their technical features and a few biological applications. In closing it notes that in contrast to a reductive focus, if in addition the presence of these real interconnective dynamics is allowed, then an integral model of life’s evolutionary animation can be achieved. The 140 references from this composite 21st century endeavor (search Villani, et al) well augurs for a 2020 discovery.

Boolean network models are one of the simplest models to study complex dynamic behavior in biological systems. They can be applied to unravel the mechanisms regulating the properties of the system or to identify promising intervention targets. Since its introduction by Stuart Kauffman in 1969 for describing gene regulatory networks, various biologically based networks and tools for their analysis were developed. Here, we summarize and explain the concepts for Boolean network modeling. We also present application examples and guidelines to work with and analyze Boolean network models. (Abstract)

Boolean networks are well-studied discrete models of biological networks such as gene regulatory networks where DNA segments in a cell interact with each other indirectly through their RNA and protein expression products or with other substances in the cell, thereby governing the rates at which genes in the network are transcribed into mRNA. (Google BN)

Earth Life > Common Code

Villegas, Pablo, et al. Evolution in the Debian GNU/Linux Software Network: Analogies and Differences with Gene Regulatory Networks. Journal of the Royal Society Interface. February, 2020. In this visionary, consummate year, University of Granada, Spain including Miguel Munoz (search) proceed to recognize many structural and operational parallels between these widely separate domains as they both engage in information processing and conveyance. Convergent comparisons such as this quite imply the reality of an independent mathematical program with a generic neural and genomic essence across all natural and social realms. See also Keil, Petr, et al. Macroecological and Macroevolutionary Patterns Emerge in the Universe of GNU/Linux Operating Systems by Petr Keil et al in Ecography (41/11, 2018).

Gene regulatory networks GRN as they process information in the cell display non-trivial architectural features such as scale-free degree distributions, high modularity and low average distance between connected genes. Such networks result from complex evolutionary and adaptive processes difficult to track empirically. On the other hand, the developmental (or evolutionary) stages of open-software networks that result from self-organized growth across versions are well known. Here, we study the evolution of the Debian GNU/Linux software network, focusing on changes of key structural and statistical features over time. Our results show that this has led to a structure in which the out-degree distribution is scale-free and the in-degree distribution is a stretched exponential. These features resemble closely those shown by GRNs, which suggests the existence of common adaptive pathways for the architectural design of information-processing networks. (Abstract)

Understanding the collective properties stemming from the interactions of a large number of units such as genes, proteins or metabolites is of paramount importance in biology. Theoretical work focusing on the changes over time of self-organizing networks can provide key information about these natural systems. Particularly, network theory provides us with a highly insightful systems-level perspective to extremely complicated biological problems, which has helped advance knowledge in fields such as neuroscience, ecology and epidemiology. The study of information processing in living systems has greatly benefited from this network perspective, complementing parallel endeavours for the analysis of single pathways, and providing a much richer understanding of collective phenomena emerging from a large number of basic inter-related units. (1)

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