(logo) Natural Genesis (logo text)
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

Recent Additions: New and Updated Entries in the Past 60 Days
Displaying entries 46 through 60 of 94 found.

Cosmomics: A Genomic Source Code is in Procreative Effect

Cosmic Code > 2015 universal

Tkacik, Gasper, et al. Thermodynamics and Signatures of Criticality in a Network of Neurons. Proceedings of the National Academy of Sciences. 112/11508, 2015. Seven theorists posted in Austria, France and the USA (Princeton) including Thierry Mora and William Bialek describe deep commonalities between far removed realms of condensed matter and cerebral matters by way of their energetic activities.

The activity of a brain—or even a small region of a brain devoted to a particular task—cannot be just the summed activity of many independent neurons. Here we use methods from statistical physics to describe the collective activity in the retina as it responds to complex inputs such as those encountered in the natural environment. We find that the distribution of messages that the retina sends to the brain is very special, mathematically equivalent to the behavior of a material near a critical point in its phase diagram. (Significance)

Cosmic Code > networks

Bianconi, Ginestra. Multilayer Networks: Structure and Function. Oxford: Oxford University Press, 2018. A Queen Mary University of London mathematician provides a comprehensive tutorial on these novel insights into how ubiquitous and deep nature’s organic and cerebral connectivities actually are. After a technical survey, it covers Communities, Centrality Measures, Interdependence, Epidemic Diffusion, and much more. See also Multiplex Networks: Basic Formalism and Structural Properties by Cozzo, Emanuele, et al (SpringerBriefs, 2018).

Multilayer networks is a rising topic in Network Science which characterizes the structure and the function of complex systems formed by several interacting networks. Multilayer networks research has been propelled forward by the wide realm of applications in social, biological and infrastructure networks and the large availability of network data, as well as by the significance of recent results, which have produced important advances. This book presents a comprehensive account of this emerging field by way of a theoretical and practical introduction to multilayer network science.

Ginestra Bianconi is Reader (Associate Professor) in Applied Mathematics and Director of the MSc in Network Science at the School of Mathematical Sciences, Queen Mary University of London. A physicist by training, since 2001 she has made network theory and its applications her central subject of investigation publishing more than one hundred papers. Currently her research focuses on multilayer networks, network geometry and percolation theory.

Cosmic Code > networks

Leli, Vito, et al. Deep Learning Super-Diffusion in Multiplex Networks. arXiv:1811.04104. As the Abstract details, VL, Saeed Osat and Timur Tlyachev, Skolkovo Institute of Science and Technology, Moscow and Jacob Biamonte, Deep Quantum AI, Moscow conceive a working method based on natural phenomena so to better analyze and design intricate nets of many kinds.

Complex network theory has shown success in understanding the emergent and collective behavior of complex systems. Many real-world complex systems were recently discovered to be more accurately modeled as multiplex networks in which each interaction type is mapped to its own network layer such as transportation networks, coupled social networks, metabolic and regulatory networks, etc. A salient physical phenomena emerging from multiplexity is super-diffusion via an accelerated diffusion by the multi-layer structure as compared to any single layer. Here we show that modern machine (deep) learning, such as fully connected and convolutional neural networks, can classify and predict the presence of super-diffusion in multiplex networks. (Abstract excerpts)

Cosmic Code > networks

Voitalov, ivan, et al. Scale-free Networks Well Done. arXiv:1811:02071. Northeastern University theorists including Dmitri Krioukov provide a further theoretical basis for the common, iterative presence of mathematical relation across all manner of natural and social networks.

We bring rigor to the vibrant activity of detecting power laws in empirical degree distributions in real-world networks. We first provide a definition of power-law distributions, equivalent to the definition of regularly varying distributions in statistics. This result allows the distribution to deviate from a pure power law arbitrarily but without affecting the power-law tail exponent. We identify three estimators of these exponents that are statistically consistent. Finally, we apply these estimators to a representative collection of synthetic and real-world data. (Abstract excerpt)

A power law is a relationship in which a relative change in one quantity gives rise to a proportional relative change in the other quantity, independent of the initial size of those quantities. (New England Complex Systems Institute)

Systems Evolution: A 21st Century Genesis Synthesis

Quickening Evolution

Blount, Zachary, et al. Contingency and Determinism in Evolution: Replaying Life’s Tape. Science. 362/655, 2018. Some three decades ago Stephen Jay Gould claimed that in a bare environment of contingent selection only, sans any inherent source to guide life’s development, human-like sentient beings would not appear a second time. This extended paper by ZB and Richard Lenski, Michigan State University, and Jonathan Losos, Washington University, St. Louis (search each), which weaves results from past projects, implies that much evidence since bodes well for an opposite view. An historic shift toward a deep predictability accrues due to a consistent convergence across many lineages, which is notable in niche constructions, digital runs that produce these trends, and many anatomic and physiological cases. See for example How Fish Get Their Stripes Again and Again by Hugo Gante in Science (362/396, 2018) and Scalable Continuous Evolution of Genes at Mutation Rates above Genomic Error Thresholds by Arjun Ravikumar, et al at bioRxiv (May 3, 2018).

Historical processes display some degree of “contingency,” meaning their outcomes are sensitive to seemingly inconsequential events that can change the future. Unlike many other natural phenomena, evolution is a historical process. Evolutionary change is often driven by natural selection which works upon variation that arises by random mutation. Moreover, evolution has taken place within a planetary environment with a particular history of its own. Here we replicate populations in evolutionary “replay” experiments which often show parallel changes, especially in overall performance, although idiosyncratic outcomes can affect which of several evolutionary paths is taken. Comparative biologists have found many notable examples of convergent adaptation to similar conditions, but quantification of how frequently such convergence occurs is difficult. On balance, the evidence indicates that evolution tends to be surprisingly repeatable among closely related lineages, but disparate outcomes become more likely as the footprint of history grows deeper. (Abstract excerpts)

Quickening Evolution

Cordero, Gerardo, et al. Gene Network Variation and Alternative Paths to Convergent Evolution in Turtles. Evolution & Development. 20/5, 2018. Iowa State University biologists including Fredric Janzen report by way of detailed studies how diverse turtle shell carapaces exhibit a persistent repetition of similar paths and forms.

Quickening Evolution

Erkurt, Murat. Emergence of Form in Embryogenesis. Journal of the Royal Society Interface. Vol.15/Iss.148, 2018. After noting a long history from Aristotle’s preformations to 19th century recapitulations, an Imperial College London mathematician factors in Turing reaction-diffusion, epigenetic influences, gene regulatory networks and a need to recognize “self-organizing operators.” Into the 21st century, by these novel sapiensphere additions life’s evolutionary developmental gestation at term gains a definitive credence.

The development of form in an embryo is the result of a series of topological and informational symmetry breakings. We introduce the vector–reaction–diffusion–drift (VRDD) system where the limit cycle of spatial dynamics is morphogen concentrations with Dirac delta-type distributions. We developed ‘fundamental forms’ from spherical blastula with a single organizing axis (rotational symmetry), double axis (mirror symmetry) and triple axis (no symmetry operator in three dimensions). Using our integrated simulation model with four layers (topological, physical, chemical and regulatory), we generated life-like forms such as hydra. Genotype–phenotype mapping was investigated with continuous and jump mutations. Our study can have applications in morphogenetic engineering, soft robotics and biomimetic design. (Abstract excerpt)

In this paper, we introduced the VRDD system as a novel concept which can generate bodyplans of fundamental forms by self-organization. We then elaborated on an FSM (finite-state machine) model of the genetic regulatory network. The result of VRDD combined with the FSM model is spatial cell differentiation during embryogenesis that can be used for hierarchical modelling of complicated forms. We have demonstrated that our concepts are capable of generating self-organized bodyplans from which we developed life-like organism forms in silico. (10)

Quickening Evolution

Ten Tusscher, Kirsten. Of Mice and Plants: Comparative Developmental Systems Biology. Developmental Biology. Online November, 2018. While affinities between Metazoan fauna creatures are well proven, flora vegetation has not been similarly studied, or compared with animals. A Utrecht University computational developmental biologist here provides an initial survey of commonalities amongst plants and with regard to organisms. In collaboration with Paulien Hogeweg at UU and others, a case can be made because new biological systems and network organizations found across flora and fauna appear to exemplify the same structural source. The implication of independent, recurrent principles and process then becomes evident. See also In Silico Evo-Devo: Reconstructing Stages in the Evolution of Animal Segmentation by KtT, Renske Vroomans and Paulien Hogeweg in BMC EvoDevo (7/14, 2016).

Multicellular animals and plants represent independent evolutionary experiments with complex multicellular body plans. Differences in their life history, a mobile versus sessile lifestyle, and predominant embryonic vs. postembryonic development, have led to highly different anatomies. However, many intriguing parallels exist. Extension of the vertebrate body axis and its segmentation into somites has a striking resemblance to plant root growth and the prepatterning of lateral root competent sites. Likewise, plant shoot phyllotaxis displays is akin to vertebrate limb and digit patterning. Both plants and animals use complex signalling systems with systemic and local signals to fine tune and coordinate organ growth. Identification of these striking examples of convergent evolution provides support for the existence of general design principles: the idea that for particular patterning demands, evolution is likely to arrive at highly similar developmental patterning mechanisms. (Abstract excerpts)

Somites are body segments containing the same internal structures, clearly visible in invertebrates but also present in embryonic stages of vertebrates. Somites are transient, segmentally organized structures. In the vertebrate embryo, the somites contribute to multiple tissues, including the axial skeleton, skeletal and smooth muscles, dorsal dermis, tendons, ligaments, cartilage and adipose tissue. (Web definitions)

Quickening Evolution > Systems Biology

Faragalla, Kyrillos, et al. From Gene List to Gene Network: Recognizing Functional Connections that Regulate Behavioral Traits. Journal of Experimental Zoology B. Online November, 2018. Western University, Ontario biologists in coauthor Graham Thompson’s group post a decisive review of the need to shift from a particulate nucleotide phase, which winds up with long tabulations, to equally real multiplex interrelations. The paper uniquely goes on to extend a “network ladder” of node first, interactions next onto protein, neuronal, social and ecosystem stages, which appear as emergent radiations of the same dynamic topology.

The study of social breeding systems is often gene focused, and the field of insect sociobiology has been successful at assimilating tools and techniques from molecular biology. One common output from sociogenomic studies is a gene list, which is readily generated from microarray, RNA sequencing, or other molecular screens. Gene lists, however, are limited because the tabular information does not explain how genes interact with each other, or how they change in real time circumstances. Here, we promote a view from molecular systems biology, where gene lists are converted into gene networks that better describe these functional connections that regulate behavioral traits. We argue that because network analyses are not restricted to “genes” as nodes, their implementation can connect multiple levels of biological organization into a single, progressively complex study system. (Abstract excerpt)

Quickening Evolution > Systems Biology

Peter, Isabelle and Eric Davidson. Genomic Control Process: Development and Evolution. Cambridge, MA: Academic Press, 2015. A CalTech biology professor and the geneticist (1937-2015, search) who was the founding theorist of gene regulatory networks provide a consummate volume to date of this major expansion of active genetic phenomena.

Chapter 1 explains different levels of control affecting developmental gene expression in animal cells, and an overview of the physical nature of the regulatory genome. The book goes on to provide in depth understandings of GRNs, how they generate the regulatory conditions, cis-regulatory functions operating at the network nodes, and the dynamics of transcriptional activity in development. The next Chapters apply network theory to embryonic development of all major kinds; development of adult body parts and organs; and to cell fate specification. Chapter 6 examines the conceptual richness that has derived from various approaches to predictive, quantitative models of GRNs and GRN circuits. In The final section the notes applications to bilaterian evolution, including the underlying explanation of hierarchical animal phylogeny, and more. (Publisher excerpt)

Quickening Evolution > Systems Biology

Shubin, Neil. Gene Regulatory Networks and Network Models in Development and Evolution. Proceedings of the National Academy of Sciences. 114/Vol. 23, 2017. An introduction to this September 2015 Sackler Colloquium organized by Shubin in honor and memory of Eric Davidson (1937-2015), the CalTech biologist (search both) who since the early 2000s studied and advocated the genomic and evolutionary importance of active nucleotide connectivities. Among the papers are Causes and Evolutionary Consequences of Primordial Germ-cell Specification in Metazoans, Gene Regulation During Drosophila Eggshell Patterning, Applying Gene Regulatory Network Logic to the Evolution of Social Behavior (search Baran) and Assessing Regulatory Information in Developmental Gene Regulatory Networks by Eric Davidson and Isabelle Peter (abstract next). This “conceptual revolution” is now in full force as many more entries in the new section attest.

Gene regulatory networks (GRNs) provide a transformation function between the static genomic sequence and the spatial specifications operating development. We address regulatory information at different levels of network organization from single node to subcircuit to large-scale GRNs and how design features such as architecture, hierarchical organization, and cis-regulatory logic contribute to developmental functions. Using subcircuits from the sea urchin endomesoderm GRN, we evaluate by Boolean modeling and in silico perturbations the import of circuit features. Thus, we begin to see how regulatory information encoded at individual nodes is integrated at all levels of network organization to control developmental process. (IP & ED Abstract excerpt)

Quickening Evolution > Systems Biology

Stephanou, Angelique, et al. Systems Biology, Systems Medicine, Systems Pharmacology. Acta Biotheretica. 66/4, 2018. University of Grenoble, North Wales Cancer Centre, University of Paris and University of Warwick system physicians advise how a turn to an integral “omics” perspective can much inform and guide these palliative services.

Systems biology is today such a widespread discipline that it becomes difficult to propose a clear definition of what it really is. For some, it remains restricted to the genomic field. For many, it designates the integrated approach or the corpus of computational methods employed to handle the vast amount of biological or medical data and investigate the complexity of the living. Systems biology, with its subfields of medicine, pharmacology and others, aims at making sense of complex observations/experimental and clinical datasets to improve our understanding of diseases and their treatments without putting aside the context in which they appear and develop.. (Abstract)

Quickening Evolution > Systems Biology

Uller, Tobias, et al. Developmental Bias and Evolution: A Regulatory Network Perspective. Genetics. 209/4, 2017. Five senior biologists, TU Lund University, Armin Moczek Indiana University, Richard Watson University of Southampton, Paul Brakefield Cambridge University and Kevin Laland University of St. Andrews propose a way to evoke life’s “directionality” by a factoring in novel appreciations of gene regulatory networks. Organism phenotypes as characteristics of an organism due to interactions of its genotype with its environment can thus be influenced and guided by this integrative quality. A prime feature is the presence of “analogous structures” which repeat, rise and further trace a homologous continuity.

Phenotypic variation is generated by the processes of development, with some variants arising more readily than others - a phenomenon known as “developmental bias.” Developmental bias and natural selection have often been portrayed as alternative explanations but developmental bias can evolve through natural selection, and bias and selection jointly influence phenotypic evolution. Here we describe recent theory on regulatory networks that explains why the influence of genetic and environmental perturbation on phenotypes is typically not uniform, and may even be biased toward adaptive phenotypic variation. We show how bias produced by developmental processes constitutes an evolving property able to impose direction on adaptive evolution and influence patterns of taxonomic and phenotypic diversity. We argue that it is not sufficient to accommodate developmental bias into evolutionary theory merely as a constraint on adaptation. A regulatory network perspective on phenotypic evolution thus helps to integrate the generation of phenotypic variation with natural selection, leaving evolutionary biology better placed to explain how organisms adapt and diversify. (Abstract excerpt)

Quickening Evolution > Intel Ev

Pinero, Jordi and Ricard Sole. Statistical Physics of Liquid Brains. bioRxiv. November 26, 2018. Institut de Biologia Evolutiva, Universitat Pompeu Fabra, Barcelona theorists post their paper from a December 2017 Santa Fe Institute seminar entitled Liquid Brains, Solid Brains, which was organized by Sole, Melanie Moses and Stephanie Forrest. (see fourth quote). A year later, an answer to its mission can be broached. A universal recurrence in kind across natural and social domains of the same generic complex network system does actually appear to be the case. While akin to genomes and ecosystems, an apt model is cerebral cognition, broadly conceived, by way of agental neurons and synaptic links in multiplex arrays. A definitive attribute is a cross-conveyance of viable intelligence and information, aka biological computation. This is how animal groupings from invertebrates to mammals to people achieve a collective decision-making. As 2020 nears, after centuries of intimation, decades of glimpses, a worldwide flow of convergent findings (a global brain learning on her/his own), augur for a realization and discovery forming in our midst.

Institut de Biologia Evolutiva, Universitat Pompeu Fabra, Barcelona theorists post their paper from a December 2017 Santa Fe Institute seminar entitled Liquid Brains, Solid Brains, which was organized by Sole, Melanie Moses and Stephanie Forrest. (see fourth quote). A year later, an answer to its mission can be broached. A universal recurrence in kind across natural and social domains of the same generic complex network system does actually appear to be the case. While akin to genomes and ecosystems, an apt model is cerebral cognition, broadly conceived, by way of agental neurons and synaptic links in multiplex arrays. A definitive attribute is a cross-conveyance of viable intelligence and information, aka biological computation. This is how animal groupings from invertebrates to mammals to people achieve a collective decision-making. As 2020 nears, after centuries of intimation, decades of glimpses, a worldwide flow of convergent findings (a global brain learning on her/his own), augur for a realization and discovery forming in our midst.

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

Earth Life > Common Code

Schack, Carolann, et al. Modularity is the Mother of Invention: A Review of Polymorphism in Bryozoans. Biological Reviews. Online November, 2018. Victoria University of Wellington, New Zealand biologists post a 35 page study of how pervasive nature’s evolutionary and biological employ of semi-autonomous modular units within larger assemblies such as bodies and brains actually is. Some two decades after their initial view by Gunter Wagner and others, this efficient structural composition, famously noted by Herbert Simon (search) in the 1960s, can now be well affirmed across the Metazoan lineages.

Modularity is a fundamental concept in biology. Most taxa within the colonial invertebrate phylum Bryozoa have achieved division of labour through the development of specialized modules (polymorphs), and this group is well exemplifies this phenomenon. We provide a comprehensive description of the diversity, morphology and function of these polymorphs and the significance of modularity to the evolutionary success of the phylum, which has >21000 described fossil and living species. Modular diversity likely arose from heterogeneous microenvironmental conditions, and repeated module clusters are an emergent property of zooid plasticity. (Abstract excerpt)

Previous   1 | 2 | 3 | 4 | 5 | 6 | 7  Next