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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 98 found.


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

Earth Life > Phenomenon > Human Societies

Danku, Zsuzsa, et al. Knowing the Past Improves Cooperation in the Future. Nature Scientific Reports. 9/262, 2019. ZD and Attila Szolnoki, Hungarian Academy of Sciences, with Matjaz Perc, University of Maribor, Slovenia, achieve a mathematical basis as to why a reference to past experiences of successes and failures can improve cooperative responses going forward to new behavioral situations.

Cooperation is the cornerstone of human evolutionary success. Like no other species, we champion the sacrifice of personal benefits for the common good, and we work together to achieve what we are unable to achieve alone. Knowledge and information from past generations is thereby often instrumental in ensuring we keep cooperating rather than deteriorating to less productive ways of coexistence. Here we present a mathematical model based on evolutionary game theory that shows how using the past as the benchmark for evolutionary success, rather than just current performance, significantly improves cooperation in the future. Cooperation is promoted because information from the past disables fast invasions of defectors, thus enhancing the long-term benefits of cooperative behavior. (Abstract)

Earth Life > Phenomenon > Human Societies

Hall, Gavin and William Bialek. The Statistical Mechanics of Twitter. arXiv:1812.07029. As a global science proceeds on its electronic own, cross-informative networks are forming between widely separate fields. Here is an exemplary paper by Princeton University theorists which reports a connection in kind between webwide social chatter and physical dynamics. It is noted that this public verbose media tends to critical attractor modes. Once more across a broad stretch from uniVerse to usVerse, a common, recurrent, biterate conservation and discourse goes on. See also Searching for Collective Behavior in a Small Brain by W. Bialek and colleagues (1810.07623).

We build models for the distribution of social states in Twitter communities which can be defined by the participation vs. silence of individuals in conversations that surround key words. We approximate the joint distribution of these binary variables using the maximum entropy principle, finding the least structured models that match the mean probability of individuals tweeting and their pairwise correlations. These models provide accurate, quantitative descriptions of higher order structure in these social networks. The parameters of these models seem poised close to critical surfaces in the space of possible models, and we observe scaling behavior of the data under coarse-graining. These results suggest that simple models, grounded in statistical physics, may provide a useful point of view on the larger data sets now emerging from complex social systems. (Abstract)

Earth Life > Phenomenon > Human Societies

Muthukrishna, Michael, et al. The Cultural Brain Hypothesis: How Culture Drives Brain Expansion, Sociality, and Life History. PLoS Computational Biology. November, 2018. London School of Economics, University of British Columbia, Arizona State University and Harvard University (Joseph Henrich) system anthropologists trace and verify the presence of a tandem interplay between human sociality, cerebral capacity, and a resultant common knowledge resource. Once again, as life and mind evolves and advances an oriented transitional ratcheting seems at work toward a more effective individual and group cognizance and viable survival.

In the last few million years, the hominin brain more than tripled in size. Comparisons across evolutionary lineages suggest that this may be part of a broader trend toward larger, more complex brains in many taxa. Efforts to understand the evolutionary forces driving brain size have focused on climatic, ecological, and social factors. Here, building on research on learning, we analytically and computationally model two closely related hypotheses: The Cultural Brain Hypothesis and the Cumulative Cultural Brain Hypothesis. The Cultural Brain Hypothesis posits that brains have been selected for their ability to store and manage information. The model reveals relationships between brain size, group size, innovation, social learning, mating structures, and the length of the juvenile period. We then derive the Cumulative Cultural Brain Hypothesis for the conditions that favor an autocatalytic take-off characteristic of human evolution. The resultant evolutionary pathway may help explain the rapid expansion of human brains and other aspects of our species’ life history and psychology. (Abstract)

Earth Life > Phenomenon > Human Societies

Thurner, Stefan. Virtual Social Science. arXiv:1811.08156. The Medical University of Vienna systems theorist opens with reference to the French philosopher Auguste Comte (1798-1857) who is seen as the original conceiver of a relation between social behavior and a physical basis, which he dubbed a sociophysics. With colleagues, into the 21st century and later 2010s, it may at last be possible to quantify a mathematical. formative relation between uniVerse and human. The novel achievement is aided by vast amounts of data from societal interactions such as iphone records, media postings, and especially multiplayer video games. Once again the generic node/link network topologies are in effect such that our communal discourse (socioinformatic) becomes akin to everywhere else since each and all spring from the same cosmo/physical source. This is a grand confirmation. Whenever will it be possible to conceive a global sapiensphere learning on her/his own?

Can we describe social systems quantitatively and predictively, when we know all the actions, interactions, and states of individuals? We interpret human societies as co-evolutionary systems of individuals and their interactions. Based on unique data of a society of computer game players, where all actions and interactions between all players are known, we show that this might indeed be possible. Within this framework we address a number of sociological classics, including formation of social networks, strength of relations, group formation, hierarchical organization, aggression management, gender differences, mobility, and wealth-inequality. We discover behavioral and organizational patterns of the homo sapiens and its society that were not visible with traditional methodology from the social sciences. (Abstract)

Earth Life > Phenomenon > Macrohistory

Christian, David. Origin Story: A Big History of Everything. New York: Little Brown, 2018. The Macquarie University, Australia scholar (search) is the original conceiver in the early 2000s of the now popular academic field of “big history” whence our human phase is joined with the entire course of life’s cosmic, galactic, stellar, and planetary evolution. This is neatly told by a series of eight Thresholds from the big bang to an anthropocene currency. This long retrospective view opens upon salient themes and vectors such as an increase of relative complexity, informational knowledge, and a main trend of a rising collective intelligence. As Vladimir Vernadsky once saw, a noosphere of shared global reason appears to be forming over the biosphere. One might tnote that while Pierre Teilhard took a similar view, he read an oriented rise to our phenomenal humanity. Here the evolutionary passage is said to go on by Darwinian selection alone via an interplay of physical necessity and chaotic chance.

Earth Life > Phenomenon > major

Andersson, Claes and Petter Tornberg. Toward a Macroevolutionary Theory of Human Evolution: The Social Protocell. Biological Theory. Online December, 2018. Within a context of the major transitions in individuality scale, Chalmers University of Technology, Sweden systems scholars achieve an overdue perception whereof societal groupings can take on a guise akin to life’s original protocells. As early hominins form symbiotic bands, they achieve adaptive internal reciprocities as cellular wholes within Wholes. A tacit principle is an emergent recurrence of the same pattern and process. In each case, a bounded unit leads which then fosters cooperation, knowledge gain and selfhood in community. By way of this nested procession, life’s rise accrues “new channels of inheritance” and an oriented direction. In regard, this website has been citing a “social protocell” for some time, especially in Ecovillages. See also Group-Level Social Knowledge by Elizabeth Hobson, et al at arXiv:1810.07215 and The Cultural Brain Hypothesis by Michael Muthukrishna et al in PLoS Computational Biology (Nov. 2018) for other takes.

Despite remarkable empirical and methodological advances, our theoretical understanding of the evolutionary processes that made us human remains fragmented and contentious. Here, we make the radical proposition that the cultural communities within which Homo emerged may be understood as a novel exotic form of organism. The argument begins from a deep congruence between robust features of Pan community life cycles and protocell models of the origins of life. We argue that if a cultural tradition, meeting certain requirements, arises in the context of such a “social protocell,” the outcome will be an evolutionary transition in individuality. By so doing, traditions and hominins coalesce into a macroscopic bio-socio-technical system, with an organismal organization that is culturally inherited. We refer to this hypothetical evolutionary individual as a “sociont.” We go on to hypothesize that the fate of the hominin would be mutualistic coadaptation into a part-whole relation with the sociont. (Abstract excerpt)

We also thereby move in the direction of unifying human evolution with the larger issue of major evolutionary transitions in natural history (MET). The dramatic evolutionary, ecological and environmental impact of the advent of Home sapiens hereby falls more squarely into the larger natural historical pattern of evolutionary disruptions resulting from bouts of innovation on this fundamental level. (2)

Earth Life > Phenomenon > major

Gabora, Liane and Cameron Smith. Exploring the Psychological Basis for Transitions in the Archaeological Record. arXiv:1812.06590. The University of British Columbia and Portland State University team continues their innovative studies upon the evolutionary advent of unlimited human creativity. These native abilities which seem deeply innate while infinite in their potential are then attributed to two major cognitive transitions.

Pedia Sapiens: A Genesis Future on Earth and in the Heavens

Future > Old Earth > Climate

Fan, Jingfang, et al. Climate Network Percolation Reveals the Expansion and Weakening of the Tropical Component under Global Warming. Proceedings of the National Academy of Sciences. 115/E12128, 2018. Senior scientists from Israel and Germany including Shlomo Havlin and Hans Schellnhuber provide a good example of how complex systems theory helps explain and predict regional and planetary weather. Here it is shown that presence of connected clusters in dynamic network structures from epidemics to magnetism can similarly characterize climatic phenomena, See also Percolation Framework to Describe El Nino Conditions by this group in Chaos (27/035807, 2017).

Senior scientists from Israel and Germany including Shlomo Havlin and Hans Schellnhuber provide a good example of how complex systems theory helps explain and predict regional and planetary weather. Here it is shown that presence of connected clusters in dynamic network structures from epidemics to magnetism can similarly characterize climatic phenomena, See also Percolation Framework to Describe El Nino Conditions by this group in Chaos (27/035807, 2017)

Future > New Earth > Mind Over Matter

Jha, Dipendra, et al. ElemNet: Deep Learning the Chemistry of Materials from only Elemental Composition. Nature Scientific Reports. 8/17593, 2018. We add this entry by Northwestern University and University of Chicago researchers as an example going forward of how materials science from physical compounds to complex biochemicals are being treated as due to a mathematical program (aka genotype). In regard, they are also becoming amenable to analysis and design by cerebral, multi-layer network processes.

The field of computational molecular sciences (CMSs) has made innumerable contributions to the understanding of the molecular phenomena that underlie and control chemical processes, which is manifested in a large number of community software projects and codes. The CMS community is now poised to take the next transformative steps of better training in modern software design and engineering methods and tools, increasing interoperability through more systematic adoption of agreed upon standards and accepted best-practices, overcoming unnecessary redundancy in software effort. This in turn will have future impact on the software that will be created to address grand challenge science that we illustrate here: the formulation of diverse catalysts, descriptions of long-range charge and excitation transfer, and development of structural ensembles for intrinsically disordered proteins. (Abstract)

Future > New Earth > Mind Over Matter

Krylov, Anna, et al. Perspective Computational Chemistry Software and its Advancement as Illustrated through Three Grand Challenge Cases for Molecular Science. Journal of Chemical Physics. 149/18, 2018. 15 scientists from California, Iowa, Texas, Virginia, New York, New Jersey, and Germany post another example of how our human co-creation of novel materials is taking on an organic form by way of an informative program at work. I was variously engaged in this field since the 1960s, so can appreciate how revolutionary this added “genotype” dimension is, which we are just beginning to appreciate. See also ElemNet: Deep Learning the Chemistry of Materials from only Elemental Composition by Dipendra Jha, et al herein for a similar entry.

15 scientists from California, Iowa, Texas, Virginia, New York, New Jersey, and Germany post another example of how our human co-creation of novel materials is taking on an organic form by way of an informative program at work. I was variously engaged in this field since the 1960s, so can appreciate how revolutionary this added “genotype” dimension is, which we are just beginning to appreciate. See also ElemNet: Deep Learning the Chemistry of Materials from only Elemental Composition by Dipendra Jha, et al herein for a similar entry.

Future > New Earth > second genesis

Davies, Jamie. Real-World Synthetic Biology. Life. 9/1, 2019. A paper by the University of Edinburgh morphologist (search) for Ricard Sole’s Synthetic Biology from Living Computers to Terraformation issue, see second quote. Similar to Manuel Porcar’s entry herein, at this epic turning point it is vital to get clear on and use consistent life-like metaphors and terms. Thus specific engineering and industrial control methods are not seen as appropriate. Davies proceeds to graphically propose self-organizing organic procedures by way of complex adaptive systems.

Authors often assert that a key feature of 21st-century synthetic biology is its use of an engineering design approach using predictive models, modular architecture, construction using well-characterized parts, and rigorous testing using standard metrics. This article examines whether this is, or even should be, the case. A brief survey of synthetic biology projects that have reached, or are near to, commercial application show very few of these attributes. Instead, they featured much trial and error, and the use of specialized, custom components and assays. I conclude that the engineering approach should not be used to define or constrain synthetic biological endeavour, and that in fact conventional engineering has more to gain by expanding and embracing more biological ways of working. (Davies Abstract excerpts)

Over the last two decades, synthetic biology has emerged as a novel field with major impact on both basic science and biomedical research. By moving beyond the classical approaches of genetic engineering, synthetic circuits implemented within living cells allow to redesign nature from the molecular and cellular levels to multicellular scales. Synthetic microorganisms have been built to explore cooperation and conflict in microbial interactions as well as the rise of multicellularity. Complex computational tasks have also been created de novo and used to expand the cognitive potential of cellular assemblies. In parallel with all these already promising results, synthetic biology is being considered as a potential path to artificially modify microbiomes and even terraform Mars biosphere. (R. Sole proposal)

Future > New Earth > second genesis

Dien, Vivian, et al. Progress Toward a Semi-Synthetic Organism with an Unrestricted Expanded Genetic Alphabet. Journal of the American Chemical Society. 140/47, 2018. A six person team from Floyd Romesberg’s laboratory at the Scripps Research Institute, La Jolla, CA describe a highly technical exercise by which to begin modifications and enhancements of life’s original four letter genomic code. The work was noted in the popular press as Life, Rewritten by James Crow (New Scientist, Dec. 8, 2018), see also the Biondi and Benner entry above.

We have developed a family of unnatural base pairs (UBPs), exemplified by the pair formed between dNaM and dTPT3, for which pairing is mediated not by complementary hydrogen bonding but by hydrophobic and packing forces. These UBPs enabled the creation of the first semisynthetic organisms (SSOs) that store increased genetic information and use it to produce proteins containing noncanonical amino acids. The results demonstrate the importance of evaluating synthetic biology “parts” in their in vivo context and the ability of hydrophobic and packing interactions to replace the complementary hydrogen bonding that underlies the replication of natural base pairs. The identification of dMTMO-dTPT3 and especially dPTMO-dTPT3 represents significant progress toward the development of SSOs able to store and retrieve increased information. (Abstract excerpt)

Future > New Earth > second genesis

Falk, Johannes , et al. Context in Synthetic Biology. Journal of Chemical Physics. 180/024106, 2019. Technical University of Dormstadt biophysicists including Barbara Drossel scope out ways to intentionally carry forth the latest complexity theories so we can proceed to make Earth life better. It might also be noted that a 2001 paper, Biological Evolution and Statistical Physics, by B. Drossel in Advances in Physics (50/2) was one of the first of its integrative kind in a physics journal (second quote) which can show how far and fast this global project has grown and advanced.

Synthetic biology aims at designing modular genetic circuits that can be assembled according to the desired function. When embedded in a cell, a circuit module becomes a small subnetwork within a larger environmental network, and its dynamics is therefore affected by potentially unknown interactions with the environment. It is well-known that the presence of the environment not only causes extrinsic noise but also memory effects, which means that the dynamics of the subnetwork is affected by its past states via a memory function that is characteristic of the environment. We study several generic scenarios for the coupling between a small module and a larger environment, with the environment consisting of a chain of mono-molecular reactions. By mapping the dynamics of this coupled system onto random walks, we are able to give exact analytical expressions for the arising memory functions. Hence, our results give insights into the possible types of memory functions and thereby help to better predict subnetwork dynamics. (Falk Abstract)

This review is an introduction to theoretical models and mathematical calculations for biological evolution, aimed at physicists. The methods in the field are naturally very similar to those used in statistical physics, although the majority of publications have appeared in biology journals. The review has three parts, which can be read independently. The first part deals with evolution in fitness landscapes and includes Fisher's theorem, adaptive walks, quasispecies models, effects of finite population sizes, and neutral evolution. The second part studies models of coevolution, including evolutionary game theory, kin selection, group selection, sexual selection, speciation, and coevolution of hosts and parasites. The third part discusses models for networks of interacting species and their extinction avalanches. (Drossel 2001 Abstract)

Future > New Earth > second genesis

Porcar, Manuel. The Hidden Charm of Life. Life. 9/1, 2019. An entry by a University of Valencia, Spain integrative biologist for a Synthetic Biology from Living Computers to Terraformation issue, see Jamie Davies herein for more. It begins by noting a present metaphor mix of machine and organic analogies and metaphors that it would do well going forward. As an example, “factory” is often applied to cellular metabolism. See also Is Research on Synthetic Cells Moving to the Next Level? by Pasquale Stano in this issue for another take.

Synthetic biology is an engineering view on biotechnology, which has revolutionized genetic engineering. The field has seen a constant development of metaphors that tend to highlight the similarities of cells with machines. I argue here that living organisms, particularly bacterial cells, are not machine-like, engineerable entities, but, instead, factory-like complex systems shaped by evolution. A change of the comparative paradigm in synthetic biology from machines to factories, from hardware to software, and from informatics to economy is discussed. (Abstract)

Future > New Earth > second genesis

Yang, Kevin, et al. Machine Learning in Protein Enginering. arXiv:1811.10775. Caltech biochemists including Frances Arnold, who co-received the 2018 Nobel Prize in Chemistry for this breakthrough work, explain in tutorial fashion the agile utility and procreative promise of this novel computational method.

Machine learning-guided protein engineering is a new paradigm that enables the optimization of complex protein functions. Machine-learning methods use data to predict protein function without requiring a detailed model of the underlying physics or biological pathways. They accelerate protein engineering by learning from information contained in all measured variants and using it to select variants that are likely to be improved. In this review, we introduce the steps required to collect protein data, train machine-learning models, and use trained models to guide engineering. (Abstract)

Protein engineering seeks to design or discover proteins whose properties, useful for technological, scientific, or medical applications, have not been needed or optimized in nature. We can envision the mapping of protein sequence to a desired function or functions as a “fitness landscape” over the high-dimensional space of possible protein sequences. The fitness represents a protein’s performance: expression level, catalytic activity, or other properties of interest to the protein engineer. The landscape determines the range of properties available to different sequences as well as the ease with which they can be optimized. Protein engineering seeks to identify sequences corresponding to high fitnesses on these landscapes. (1)

Inspired by natural evolution, directed evolution climbs a fitness landscape by accumulating beneficial mutations in an iterative protocol of mutation and selection, as illustrated in Figure 1a. The first step is sequence diversification using techniques such as random mutagenesis, site-saturation mutagenesis, or recombination to generate a library of modified sequences starting from the parent sequence(s). The second step is screening or selection to identify variants with improved properties for the next round of diversification. The steps are repeated until fitness goals are achieved. (2)

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