6 found
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  1.  38
    Weak links: the universal key to the stability of networks and complex systems.Peter Csermely - 2009 - London: Springer.
    How can our societies be stabilized in a crisis? Why do we enjoy and understand Shakespeare? Why are fruitflies uniform? How do omnivorous eating habits aid our survival? What makes the Mona Lisa's smile beautiful? How do women keep their social structures intact? -- Could there possibly be a single answer to all these questions? This book shows that the statement 'weak links stabilize complex systems' provides the key to understanding each of these intriguing puzzles, and many others too. The (...)
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  2.  23
    Ageing as a price of cooperation and complexity.Huba J. M. Kiss, Ágoston Mihalik, Tibor Nánási, Bálint Őry, Zoltán Spiró, Csaba Sőti & Peter Csermely - 2009 - Bioessays 31 (6):651-664.
    The network concept is increasingly used for the description of complex systems. Here, we summarize key aspects of the evolvability and robustness of the hierarchical network set of macromolecules, cells, organisms and ecosystems. Listing the costs and benefits of cooperation as a necessary behaviour to build this network hierarchy, we outline the major hypothesis of the paper: the emergence of hierarchical complexity needs cooperation leading to the ageing (i.e. gradual deterioration) of the constituent networks. A stable environment develops cooperation leading (...)
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  3.  9
    A rejtett hálózatok ereje: mi segíti a világ stabilitását?Peter Csermely - 2005 - Budapest: Vince.
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  4.  28
    Chaperone‐percolator model: a possible molecular mechanism of Anfinsen‐cage–type chaperones.Peter Csermely - 1999 - Bioessays 21 (11):959-965.
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  5.  6
    Chaperone-percolator model: a possible molecular mechanism of Anfinsen-cage-type chaperones.Peter Csermely - 1999 - Bioessays 21 (11):959-965.
  6.  47
    The Wisdom of Networks: A General Adaptation and Learning Mechanism of Complex Systems.Peter Csermely - 2018 - Bioessays 40 (1):1700150.
    I hypothesize that re-occurring prior experience of complex systems mobilizes a fast response, whose attractor is encoded by their strongly connected network core. In contrast, responses to novel stimuli are often slow and require the weakly connected network periphery. Upon repeated stimulus, peripheral network nodes remodel the network core that encodes the attractor of the new response. This “core-periphery learning” theory reviews and generalizes the heretofore fragmented knowledge on attractor formation by neural networks, periphery-driven innovation, and a number of recent (...)
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