Publication Date:
2016-02-19
Description:
Resilience, a system's ability to adjust its activity to retain its basic functionality when errors, failures and environmental changes occur, is a defining property of many complex systems. Despite widespread consequences for human health, the economy and the environment, events leading to loss of resilience--from cascading failures in technological systems to mass extinctions in ecological networks--are rarely predictable and are often irreversible. These limitations are rooted in a theoretical gap: the current analytical framework of resilience is designed to treat low-dimensional models with a few interacting components, and is unsuitable for multi-dimensional systems consisting of a large number of components that interact through a complex network. Here we bridge this theoretical gap by developing a set of analytical tools with which to identify the natural control and state parameters of a multi-dimensional complex system, helping us derive effective one-dimensional dynamics that accurately predict the system's resilience. The proposed analytical framework allows us systematically to separate the roles of the system's dynamics and topology, collapsing the behaviour of different networks onto a single universal resilience function. The analytical results unveil the network characteristics that can enhance or diminish resilience, offering ways to prevent the collapse of ecological, biological or economic systems, and guiding the design of technological systems resilient to both internal failures and environmental changes.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Gao, Jianxi -- Barzel, Baruch -- Barabasi, Albert-Laszlo -- England -- Nature. 2016 Feb 18;530(7590):307-12. doi: 10.1038/nature16948.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Center for Complex Network Research, Department of Physics, Northeastern University, Boston, Massachusetts 02115, USA. ; Department of Mathematics, Bar-Ilan University, Ramat-Gan 52900, Israel. ; Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Harvard University, Boston, Massachusetts 02215, USA. ; Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA. ; Center for Network Science, Central European University, Budapest 1051, Hungary.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26887493" target="_blank"〉PubMed〈/a〉
Keywords:
Adaptation, Physiological
;
*Ecosystem
;
Gene Expression Regulation
;
Gene Regulatory Networks/*genetics
;
*Models, Biological
Print ISSN:
0028-0836
Electronic ISSN:
1476-4687
Topics:
Biology
,
Chemistry and Pharmacology
,
Medicine
,
Natural Sciences in General
,
Physics
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