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  • 1
    In: 〈i〉WORD〈/i〉, Informa UK Limited, Vol. 55, No. 2 ( 2004-08), p. 235-320
    Type of Medium: Online Resource
    ISSN: 0043-7956 , 2373-5112
    RVK:
    Language: English
    Publisher: Informa UK Limited
    Publication Date: 2004
    detail.hit.zdb_id: 2783014-7
    detail.hit.zdb_id: 200471-9
    SSG: 7,11
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  • 2
    In: Head & Neck, Wiley, Vol. 33, No. 12 ( 2011-12), p. 1727-1734
    Type of Medium: Online Resource
    ISSN: 1043-3074
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2011
    detail.hit.zdb_id: 2001440-5
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  • 3
    Online Resource
    Online Resource
    MDPI AG ; 2022
    In:  International Journal of Molecular Sciences Vol. 23, No. 17 ( 2022-08-24), p. 9580-
    In: International Journal of Molecular Sciences, MDPI AG, Vol. 23, No. 17 ( 2022-08-24), p. 9580-
    Abstract: There is a growing appreciation in the fields of cell biology and developmental biology that cells collectively process information in time and space. While many powerful molecular tools exist to observe biophysical dynamics, biologists must find ways to quantitatively understand these phenomena at the systems level. Here, we present a guide for the application of well-established information theory metrics to biological datasets and explain these metrics using examples from cell, developmental and regenerative biology. We introduce a novel computational tool named after its intended purpose, calcium imaging, (CAIM) for simple, rigorous application of these metrics to time series datasets. Finally, we use CAIM to study calcium and cytoskeletal actin information flow patterns between Xenopus laevis embryonic animal cap stem cells. The tools that we present here should enable biologists to apply information theory to develop a systems-level understanding of information processing across a diverse array of experimental systems.
    Type of Medium: Online Resource
    ISSN: 1422-0067
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2019364-6
    SSG: 12
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  • 4
    Online Resource
    Online Resource
    International Information and Engineering Technology Association ; 2018
    In:  International Journal of Design & Nature and Ecodynamics Vol. 12, No. 4 ( 2018-01-01), p. 458-469
    In: International Journal of Design & Nature and Ecodynamics, International Information and Engineering Technology Association, Vol. 12, No. 4 ( 2018-01-01), p. 458-469
    Type of Medium: Online Resource
    ISSN: 1755-7437 , 1755-7445
    URL: Issue
    Language: English
    Publisher: International Information and Engineering Technology Association
    Publication Date: 2018
    detail.hit.zdb_id: 2492146-4
    SSG: 21
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  • 5
    In: eLife, eLife Sciences Publications, Ltd, Vol. 9 ( 2020-07-30)
    Abstract: Social animals continuously influence each other’s behavior. Most of these interactions simply consist in an individual immediately responding to the behavior of another in a predictable way. Still, when the same individuals interact over long periods, complex social interactions can arise. These can be difficult for scientists to study, because how animals behave at a given moment depends on their shared history. Certain species of ants and termites use smell and touch to do ‘tandem runs’ and move in pairs through the environment. Only ants, however, can learn a new route from their running partner. Understanding how this difference arises means examining how the animals interact and communicate over longer time scales. This requires new approaches to capture how information flows between the insects. Here, Valentini et al. used a scientific methodology known as information theory to study tandem running in one species of ants and two species of termites. Information theory provides a framework to quantify how information is shared, processed and stored. The flow of information between individuals was measured separately for different aspects of tandem running. At small time scales, ant and termite behavior appeared identical, but over longer periods, it was possible to distinguish between the two types of insects. In termites, only one individual in a pair sent information to the other to instruct the second termite where to go. By contrast, in ants, both members of the tandem communicated with each other in a way that was consistent with how humans acknowledge information they receive from other individuals. The approach used by Valentini et al. will be useful to researchers who study how complex and often cryptic social interactions develop over extended periods in social animals. This framework could also be applied in other systems such as groups of cells, or economic networks.
    Type of Medium: Online Resource
    ISSN: 2050-084X
    Language: English
    Publisher: eLife Sciences Publications, Ltd
    Publication Date: 2020
    detail.hit.zdb_id: 2687154-3
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  • 6
    In: Astrobiology, Mary Ann Liebert Inc, Vol. 18, No. 6 ( 2018-06), p. 663-708
    Type of Medium: Online Resource
    ISSN: 1531-1074 , 1557-8070
    Language: English
    Publisher: Mary Ann Liebert Inc
    Publication Date: 2018
    detail.hit.zdb_id: 2047736-3
    SSG: 12
    SSG: 16,12
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  • 7
    Online Resource
    Online Resource
    American Association for the Advancement of Science (AAAS) ; 2019
    In:  Science Advances Vol. 5, No. 1 ( 2019-01-04)
    In: Science Advances, American Association for the Advancement of Science (AAAS), Vol. 5, No. 1 ( 2019-01-04)
    Abstract: The application of network science to biology has advanced our understanding of the metabolism of individual organisms and the organization of ecosystems but has scarcely been applied to life at a planetary scale. To characterize planetary-scale biochemistry, we constructed biochemical networks using a global database of 28,146 annotated genomes and metagenomes and 8658 cataloged biochemical reactions. We uncover scaling laws governing biochemical diversity and network structure shared across levels of organization from individuals to ecosystems, to the biosphere as a whole. Comparing real biochemical reaction networks to random reaction networks reveals that the observed biological scaling is not a product of chemistry alone but instead emerges due to the particular structure of selected reactions commonly participating in living processes. We show that the topology of biochemical networks for the three domains of life is quantitatively distinguishable, with 〉 80% accuracy in predicting evolutionary domain based on biochemical network size and average topology. Together, our results point to a deeper level of organization in biochemical networks than what has been understood so far.
    Type of Medium: Online Resource
    ISSN: 2375-2548
    Language: English
    Publisher: American Association for the Advancement of Science (AAAS)
    Publication Date: 2019
    detail.hit.zdb_id: 2810933-8
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  • 8
    Online Resource
    Online Resource
    The Royal Society ; 2017
    In:  Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences Vol. 375, No. 2109 ( 2017-12-28), p. 20160337-
    In: Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, The Royal Society, Vol. 375, No. 2109 ( 2017-12-28), p. 20160337-
    Abstract: Over the last several hundred years of scientific progress, we have arrived at a deep understanding of the non-living world. We have not yet achieved an analogous, deep understanding of the living world. The origins of life is our best chance at discovering scientific laws governing life, because it marks the point of departure from the predictable physical and chemical world to the novel, history-dependent living world. This theme issue aims to explore ways to build a deeper understanding of the nature of biology, by modelling the origins of life on a sufficiently abstract level, starting from prebiotic conditions on Earth and possibly on other planets and bridging quantitative frameworks approaching universal aspects of life. The aim of the editors is to stimulate new directions for solving the origins of life. The present introduction represents the point of view of the editors on some of the most promising future directions. This article is part of the themed issue ‘Reconceptualizing the origins of life’.
    Type of Medium: Online Resource
    ISSN: 1364-503X , 1471-2962
    RVK:
    Language: English
    Publisher: The Royal Society
    Publication Date: 2017
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    detail.hit.zdb_id: 1462626-3
    SSG: 11
    SSG: 5,1
    SSG: 5,21
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  • 9
    In: Nature, Springer Science and Business Media LLC
    Abstract: Scientists have grappled with reconciling biological evolution 1,2 with the immutable laws of the Universe defined by physics. These laws underpin life’s origin, evolution and the development of human culture and technology, yet they do not predict the emergence of these phenomena. Evolutionary theory explains why some things exist and others do not through the lens of selection. To comprehend how diverse, open-ended forms can emerge from physics without an inherent design blueprint, a new approach to understanding and quantifying selection is necessary 3–5 . We present assembly theory (AT) as a framework that does not alter the laws of physics, but redefines the concept of an ‘object’ on which these laws act. AT conceptualizes objects not as point particles, but as entities defined by their possible formation histories. This allows objects to show evidence of selection, within well-defined boundaries of individuals or selected units. We introduce a measure called assembly ( A ), capturing the degree of causation required to produce a given ensemble of objects. This approach enables us to incorporate novelty generation and selection into the physics of complex objects. It explains how these objects can be characterized through a forward dynamical process considering their assembly. By reimagining the concept of matter within assembly spaces, AT provides a powerful interface between physics and biology. It discloses a new aspect of physics emerging at the chemical scale, whereby history and causal contingency influence what exists.
    Type of Medium: Online Resource
    ISSN: 0028-0836 , 1476-4687
    RVK:
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    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 120714-3
    detail.hit.zdb_id: 1413423-8
    SSG: 11
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  • 10
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2021
    In:  Scientific Reports Vol. 11, No. 1 ( 2021-03-22)
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 11, No. 1 ( 2021-03-22)
    Abstract: Biochemical reactions underlie the functioning of all life. Like many examples of biology or technology, the complex set of interactions among molecules within cells and ecosystems poses a challenge for quantification within simple mathematical objects. A large body of research has indicated many real-world biological and technological systems, including biochemistry, can be described by power-law relationships between the numbers of nodes and edges, often described as “scale-free”. Recently, new statistical analyses have revealed true scale-free networks are rare. We provide a first application of these methods to data sampled from across two distinct levels of biological organization: individuals and ecosystems. We analyze a large ensemble of biochemical networks including networks generated from data of 785 metagenomes and 1082 genomes (sampled from the three domains of life). The results confirm no more than a few biochemical networks are any more than super-weakly scale-free. Additionally, we test the distinguishability of individual and ecosystem-level biochemical networks and show there is no sharp transition in the structure of biochemical networks across these levels of organization moving from individuals to ecosystems. This result holds across different network projections. Our results indicate that while biochemical networks are not scale-free, they nonetheless exhibit common structure across different levels of organization, independent of the projection chosen, suggestive of shared organizing principles across all biochemical networks.
    Type of Medium: Online Resource
    ISSN: 2045-2322
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 2615211-3
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