8 research outputs found
How motifs condition critical thresholds for tipping cascades in complex networks: Linking Micro- to Macro-scales
In this study, we investigate how specific micro interaction structures
(motifs) affect the occurrence of tipping cascades on networks of stylized
tipping elements. We compare the properties of cascades in Erd\"os-R\'enyi
networks and an exemplary moisture recycling network of the Amazon rainforest.
Within these networks, decisive small-scale motifs are the feed forward loop,
the secondary feed forward loop, the zero loop and the neighboring loop.
Of all motifs, the feed forward loop motif stands out in tipping cascades
since it decreases the critical coupling strength necessary to initiate a
cascade more than the other motifs. We find that for this motif, the reduction
of critical coupling strength is 11% less than the critical coupling of a pair
of tipping elements. For highly connected networks, our analysis reveals that
coupled feed forward loops coincide with a strong 90% decrease of the critical
coupling strength.
For the highly clustered moisture recycling network in the Amazon, we observe
regions of very high motif occurrence for each of the four investigated motifs
suggesting that these regions are more vulnerable. The occurrence of motifs is
found to be one order of magnitude higher than in a random Erd\"os-R\'enyi
network.
This emphasizes the importance of local interaction structures for the
emergence of global cascades and the stability of the network as a whole
Dynamics of Tipping Cascades on Complex Networks
Tipping points occur in diverse systems in various disciplines such as
ecology, climate science, economy or engineering. Tipping points are critical
thresholds in system parameters or state variables at which a tiny perturbation
can lead to a qualitative change of the system. Many systems with tipping
points can be modeled as networks of coupled multistable subsystems, e.g.
coupled patches of vegetation, connected lakes, interacting climate tipping
elements or multiscale infrastructure systems. In such networks, tipping events
in one subsystem are able to induce tipping cascades via domino effects. Here,
we investigate the effects of network topology on the occurrence of such
cascades. Numerical cascade simulations with a conceptual dynamical model for
tipping points are conducted on Erd\H{o}s-R\'enyi, Watts-Strogatz and
Barab\'asi-Albert networks. Additionally, we generate more realistic networks
using data from moisture-recycling simulations of the Amazon rainforest and
compare the results to those obtained for the model networks. We furthermore
use a directed configuration model and a stochastic block model which preserve
certain topological properties of the Amazon network to understand which of
these properties are responsible for its increased vulnerability. We find that
clustering and spatial organization increase the vulnerability of networks and
can lead to tipping of the whole network. These results could be useful to
evaluate which systems are vulnerable or robust due to their network topology
and might help to design or manage systems accordingly.Comment: 22 pages, 12 figure
Modelling nonlinear dynamics of interacting tipping elements on complex networks: the PyCascades package
Tipping elements occur in various systems such as in socio-economics, ecology and the climate system. In many cases, the individual tipping elements are not independent of each other, but they interact across scales in time and space. To model systems of interacting tipping elements, we here introduce the PyCascades open source software package for studying interacting tipping elements (https://doi.org/10.5281/zenodo.4153102). PyCascades is an object-oriented and easily extendable package written in the programming language Python. It allows for investigating under which conditions potentially dangerous cascades can emerge between interacting dynamical systems, with a focus on tipping elements. With PyCascades it is possible to use different types of tipping elements such as double-fold and Hopf types and interactions between them. PyCascades can be applied to arbitrary complex network structures and has recently been extended to stochastic dynamical systems. This paper provides an overview of the functionality of PyCascades by introducing the basic concepts and the methodology behind it. In the end, three examples are discussed, showing three different applications of the software package. First, the moisture recycling network of the Amazon rainforest is investigated. Second, a model of interacting Earth system tipping elements is discussed. And third, the PyCascades modelling framework is applied to a global trade network
Macrophages inhibit Coxiella burnetii by the ACOD1 âitaconate pathway for containment of Q fever
Infection with the intracellular bacterium Coxiella (C.) burnetii can cause chronic Q fever with severe complications and limited treatment options. Here, we identify the enzyme cis-aconitate decarboxylase 1 (ACOD1 or IRG1) and its product itaconate as protective host immune pathway in Q fever. Infection of mice with C. burnetii induced expression of several anti-microbial candidate genes, including Acod1. In macrophages, Acod1 was essential for restricting C. burnetii replication, while other antimicrobial pathways were dispensable. Intratracheal or intraperitoneal infection of Acod1-/- mice caused increased C. burnetii burden, weight loss and stronger inflammatory gene expression. Exogenously added itaconate restored pathogen control in Acod1-/- mouse macrophages and blocked replication in human macrophages. In axenic cultures, itaconate directly inhibited growth of C. burnetii. Finally, treatment of infected Acod1-/- mice with itaconate efficiently reduced the tissue pathogen load. Thus, ACOD1-derived itaconate is a key factor in the macrophage-mediated defense against C. burnetii and may be exploited for novel therapeutic approaches in chronic Q fever
How motifs condition critical thresholds for tipping cascades in complex networks: Linking micro- to macro-scales
In this study, we investigate how specific micro-interaction structures (motifs) affect the occurrence of tipping cascades on networks of stylized tipping elements. We compare the properties of cascades in Erd?s?RĂ©nyi networks and an exemplary moisture recycling network of the Amazon rainforest. Within these networks, decisive small-scale motifs are the feed forward loop, the secondary feed forward loop, the zero loop, and the neighboring loop. Of all motifs, the feed forward loop motif stands out in tipping cascades since it decreases the critical coupling strength necessary to initiate a cascade more than the other motifs. We find that for this motif, the reduction of critical coupling strength is 11% less than the critical coupling of a pair of tipping elements. For highly connected networks, our analysis reveals that coupled feed forward loops coincide with a strong 90% decrease in the critical coupling strength. For the highly clustered moisture recycling network in the Amazon, we observe regions of a very high motif occurrence for each of the four investigated motifs, suggesting that these regions are more vulnerable. The occurrence of motifs is found to be one order of magnitude higher than in a random Erd?s?RĂ©nyi network. This emphasizes the importance of local interaction structures for the emergence of global cascades and the stability of the network as a whole
Dynamics of tipping cascades on complex networks
Tipping points occur in diverse systems in various disciplines such as ecology, climate science, economy, and engineering. Tipping points are critical thresholds in system parameters or state variables at which a tiny perturbation can lead to a qualitative change of the system. Many systems with tipping points can be modeled as networks of coupled multistable subsystems, e.g., coupled patches of vegetation, connected lakes, interacting climate tipping elements, and multiscale infrastructure systems. In such networks, tipping events in one subsystem are able to induce tipping cascades via domino effects. Here, we investigate the effects of network topology on the occurrence of such cascades. Numerical cascade simulations with a conceptual dynamical model for tipping points are conducted on ErdĆs-RĂ©nyi, Watts-Strogatz, and BarabĂĄsi-Albert networks. Additionally, we generate more realistic networks using data from moisture-recycling simulations of the Amazon rainforest and compare the results to those obtained for the model networks. We furthermore use a directed configuration model and a stochastic block model which preserve certain topological properties of the Amazon network to understand which of these properties are responsible for its increased vulnerability. We find that clustering and spatial organization increase the vulnerability of networks and can lead to tipping of the whole network. These results could be useful to evaluate which systems are vulnerable or robust due to their network topology and might help us to design or manage systems accordingly
Modelling nonlinear dynamics of interacting tipping elements on complex networks: the PyCascades package
Tipping elements occur in various systems such as in socio-economics, ecology and the climate system. In many cases, the individual tipping elements are not independent of each other, but they interact across scales in time and space. To model systems of interacting tipping elements, we here introduce the PyCascades open source software package for studying interacting tipping elements (https://doi.org/10.5281/zenodo.4153102). PyCascades is an object-oriented and easily extendable package written in the programming language Python. It allows for investigating under which conditions potentially dangerous cascades can emerge between interacting dynamical systems, with a focus on tipping elements. With PyCascades it is possible to use different types of tipping elements such as double-fold and Hopf types and interactions between them. PyCascades can be applied to arbitrary complex network structures and has recently been extended to stochastic dynamical systems. This paper provides an overview of the functionality of PyCascades by introducing the basic concepts and the methodology behind it. In the end, three examples are discussed, showing three different applications of the software package. First, the moisture recycling network of the Amazon rainforest is investigated. Second, a model of interacting Earth system tipping elements is discussed. And third, the PyCascades modelling framework is applied to a global trade network
How motifs condition critical thresholds for tipping cascades in complex networks: Linking micro- to macro-scales
In this study, we investigate how specific micro-interaction structures (motifs) affect the occurrence of tipping cascades on networks of stylized tipping elements. We compare the properties of cascades in Erd?s?RĂ©nyi networks and an exemplary moisture recycling network of the Amazon rainforest. Within these networks, decisive small-scale motifs are the feed forward loop, the secondary feed forward loop, the zero loop, and the neighboring loop. Of all motifs, the feed forward loop motif stands out in tipping cascades since it decreases the critical coupling strength necessary to initiate a cascade more than the other motifs. We find that for this motif, the reduction of critical coupling strength is 11% less than the critical coupling of a pair of tipping elements. For highly connected networks, our analysis reveals that coupled feed forward loops coincide with a strong 90% decrease in the critical coupling strength. For the highly clustered moisture recycling network in the Amazon, we observe regions of a very high motif occurrence for each of the four investigated motifs, suggesting that these regions are more vulnerable. The occurrence of motifs is found to be one order of magnitude higher than in a random Erd?s?RĂ©nyi network. This emphasizes the importance of local interaction structures for the emergence of global cascades and the stability of the network as a whole