Control of complex networks requires both structure and dynamics

The study of network structure has uncovered signatures of the organization of complex systems. However, there is also a need to understand how to control them; for example, identifying strategies to revert a diseased cell to a healthy state, or a mature cell to a pluripotent state. Two recent methodologies suggest that the controllability of complex systems can be predicted solely from the graph of interactions between variables, without considering their dynamics: structural controllability and minimum dominating sets. We demonstrate that such structure-only methods fail to characterize controllability when dynamics are introduced. We study Boolean network ensembles of network motifs as well as three models of biochemical regulation: the segment polarity network in Drosophila melanogaster, the cell cycle of budding yeast Saccharomyces cerevisiae, and the floral organ arrangement in Arabidopsis thaliana. We demonstrate that structure-only methods both undershoot and overshoot the number and which sets of critical variables best control the dynamics of these models, highlighting the importance of the actual system dynamics in determining control. Our analysis further shows that the logic of automata transition functions, namely how canalizing they are, plays an important role in the extent to which structure predicts dynamics.Comment: 15 pages, 6 figure

Modularity and the spread of perturbations in complex dynamical systems

We propose a method to decompose dynamical systems based on the idea that modules constrain the spread of perturbations. We find partitions of system variables that maximize 'perturbation modularity', defined as the autocovariance of coarse-grained perturbed trajectories. The measure effectively separates the fast intramodular from the slow intermodular dynamics of perturbation spreading (in this respect, it is a generalization of the 'Markov stability' method of network community detection). Our approach captures variation of modular organization across different system states, time scales, and in response to different kinds of perturbations: aspects of modularity which are all relevant to real-world dynamical systems. It offers a principled alternative to detecting communities in networks of statistical dependencies between system variables (e.g., 'relevance networks' or 'functional networks'). Using coupled logistic maps, we demonstrate that the method uncovers hierarchical modular organization planted in a system's coupling matrix. Additionally, in homogeneously-coupled map lattices, it identifies the presence of self-organized modularity that depends on the initial state, dynamical parameters, and type of perturbations. Our approach offers a powerful tool for exploring the modular organization of complex dynamical systems

Element-centric clustering comparison unifies overlaps and hierarchy

Clustering is one of the most universal approaches for understanding complex data. A pivotal aspect of clustering analysis is quantitatively comparing clusterings; clustering comparison is the basis for many tasks such as clustering evaluation, consensus clustering, and tracking the temporal evolution of clusters. In particular, the extrinsic evaluation of clustering methods requires comparing the uncovered clusterings to planted clusterings or known metadata. Yet, as we demonstrate, existing clustering comparison measures have critical biases which undermine their usefulness, and no measure accommodates both overlapping and hierarchical clusterings. Here we unify the comparison of disjoint, overlapping, and hierarchically structured clusterings by proposing a new element-centric framework: elements are compared based on the relationships induced by the cluster structure, as opposed to the traditional cluster-centric philosophy. We demonstrate that, in contrast to standard clustering similarity measures, our framework does not suffer from critical biases and naturally provides unique insights into how the clusterings differ. We illustrate the strengths of our framework by revealing new insights into the organization of clusters in two applications: the improved classification of schizophrenia based on the overlapping and hierarchical community structure of fMRI brain networks, and the disentanglement of various social homophily factors in Facebook social networks. The universality of clustering suggests far-reaching impact of our framework throughout all areas of science

After Wildfire: Range Recovery

In the grip of drought, livestock producers often must deal with the additional impact of wildfire. While drought conditions develop gradually and can be anticipated, losses due to wildfire are sudden and devastating

Can the string scale be related to the cosmic baryon asymmetry?

In a previous work, a mechanism was presented by which baryon asymmetry can be generated during inflation from elliptically polarized gravitons. Nonetheless, the mechanism only generated a realistic baryon asymmetry under special circumstances which requires an enhancement of the lepton number from an unspecified GUT. In this note we provide a stringy embedding of this mechanism through the Green-Schwarz mechanism, demonstrating that if the model-independent axion is the source of the gravitational waves responsible for the lepton asymmetry, one can observationally constrain the string scale and coupling.Comment: 12 Pages, typo corrected in the tex

Historical comparison of gender inequality in scientific careers across countries and disciplines

There is extensive, yet fragmented, evidence of gender differences in academia suggesting that women are under-represented in most scientific disciplines, publish fewer articles throughout a career, and their work acquires fewer citations. Here, we offer a comprehensive picture of longitudinal gender discrepancies in performance through a bibliometric analysis of academic careers by reconstructing the complete publication history of over 1.5 million gender-identified authors whose publishing career ended between 1955 and 2010, covering 83 countries and 13 disciplines. We find that, paradoxically, the increase of participation of women in science over the past 60 years was accompanied by an increase of gender differences in both productivity and impact. Most surprisingly though, we uncover two gender invariants, finding that men and women publish at a comparable annual rate and have equivalent career-wise impact for the same size body of work. Finally, we demonstrate that differences in dropout rates and career length explain a large portion of the reported career-wise differences in productivity and impact. This comprehensive picture of gender inequality in academia can help rephrase the conversation around the sustainability of women's careers in academia, with important consequences for institutions and policy makers.Comment: 23 pages, 4 figures, and S

CANA: A python package for quantifying control and canalization in Boolean Networks

Logical models offer a simple but powerful means to understand the complex dynamics of biochemical regulation, without the need to estimate kinetic parameters. However, even simple automata components can lead to collective dynamics that are computationally intractable when aggregated into networks. In previous work we demonstrated that automata network models of biochemical regulation are highly canalizing, whereby many variable states and their groupings are redundant (Marques-Pita and Rocha, 2013). The precise charting and measurement of such canalization simplifies these models, making even very large networks amenable to analysis. Moreover, canalization plays an important role in the control, robustness, modularity and criticality of Boolean network dynamics, especially those used to model biochemical regulation (Gates and Rocha, 2016; Gates et al., 2016; Manicka, 2017). Here we describe a new publicly-available Python package that provides the necessary tools to extract, measure, and visualize canalizing redundancy present in Boolean network models. It extracts the pathways most effective in controlling dynamics in these models, including their effective graph and dynamics canalizing map, as well as other tools to uncover minimum sets of control variables.Comment: Submitted to the Systems Biology section of Frontiers in Physiolog

Spring Drought Effects on Rangeland Forage Yield from Clayey Ecological Sites in Western South Dakota

Understanding the historical influence of seasonal precipitation, especially spring precipitation, and stocking rate on forage yield would be desirable for planning purposes. The objectives of this study were to examine the historical precipitation pattern and how it influenced forage yield on pastures that were stocked at light, moderate, and heavy stocking rates for 15 years at the Cottonwood Range and Livestock Research Station in western South Dakota. Weather data from 1909 to 2004 at the station were analyzed to determine the frequency of occurrence of below (≤75 of mean), normal, and above normal (\u3e125% of mean) spring precipitation (April, May, June). Additional data from the station provided for an examination of the relationships between weather and forage yield from pastures grazed at three stocking rates. Forage yield and precipitation data were collected from 1945 to 1960 from pastures continuously grazed from May to November at 0.25, 0.40, and 0.60 AUM/acre. Analysis of variance was used to test influence of spring precipitation (spring drought and non-spring drought) and stocking rate (light, moderate, and heavy) on forage yield. Below normal, normal, and above normal spring precipitation occurred 29, 48, and 23% of the time, respectively. Forage yield in spring drought years was 420 lb/ac less (P \u3c 0.01) than in non-spring drought years. Lightly stocked pastures had 38 and 71% more (P \u3c 0.01) forage than moderate and heavily stocked pastures. Spring droughts reduced forage yield (P \u3c 0.01) in light, moderate, and heavily stocked pastures by 20, 27, and 35%, respectively. Forage yield from lightly stocked pastures during spring droughts was similar to heavily stocked pastures in non-spring drought years. Our study indicates that spring precipitation should guide stocking rate decisions made during the growing season. Light and moderate stocking rates reduce the impact of spring drought on forage yield more than heavy stocking rates