Non-local transitions and ground state switching in the self-organization of vascular networks.

The model by D. Hu and D. Cai [Phys.

Shipping traffic through the Arctic Ocean: Spatial distribution, seasonal variation, and its dependence on the sea ice extent.

The reduction in sea ice cover with Arctic warming facilitates shipping through remarkably shorter shipping routes. Automatic identification system (AIS) is a powerful data source to monitor Arctic Ocean shipping. Based on the AIS data from an online platform, we quantified the spatial distribution of shipping through this area, its intensity, and the seasonal variation.

Bifurcations in adaptive vascular networks: Toward model calibration.

Transport networks are crucial for the functioning of natural and technological systems. We study a mathematical model of vascular network adaptation, where the network structure dynamically adjusts to changes in blood flow and pressure. The model is based on local feedback mechanisms that occur on different time scales in the mammalian vasculature.

Order-disorder transition in the zero-temperature Ising model on random graphs.

The zero-temperature Ising model is known to reach a fully ordered ground state in sufficiently dense random graphs. In sparse random graphs, the dynamics gets absorbed in disordered local minima at magnetization close to zero. Here, we find that the nonequilibrium transition between the ordered and the disordered regime occurs at an average degree that slowly grows with the graph size.

Patterning the insect eye: From stochastic to deterministic mechanisms.

While most processes in biology are highly deterministic, stochastic mechanisms are sometimes used to increase cellular diversity. In human and Drosophila eyes, photoreceptors sensitive to different wavelengths of light are distributed in stochastic patterns, and one such patterning system has been analyzed in detail in the Drosophila retina. Interestingly, some species in the dipteran family Dolichopodidae (the "long legged" flies, or "Doli") instead exhibit highly orderly deterministic eye patterns.

Cover-Encodings of Fitness Landscapes.

The traditional way of tackling discrete optimization problems is by using local search on suitably defined cost or fitness landscapes. Such approaches are however limited by the slowing down that occurs when the local minima that are a feature of the typically rugged landscapes encountered arrest the progress of the search process. Another way of tackling optimization problems is by the use of heuristic approximations to estimate a global cost minimum.

Competition in the presence of aging: dominance, coexistence, and alternation between states.

We study the stochastic dynamics of coupled states with transition probabilities depending on local persistence, this is, the time since a state has changed. When the system has a preference to adopt older states the system orders quickly due to the dominance of old states. When preference for new states prevails, the system can show coexistence of states or synchronized collective behavior resulting in long ordering times.

Anomalous scaling in an age-dependent branching model.

We introduce a one-parametric family of tree growth models, in which branching probabilities decrease with branch age τ as τ(-α). Depending on the exponent α, the scaling of tree depth with tree size n displays a transition between the logarithmic scaling of random trees and an algebraic growth. At the transition (α=1) tree depth grows as (logn)(2).

Boolean networks with veto functions.

Boolean networks are discrete dynamical systems for modeling regulation and signaling in living cells. We investigate a particular class of Boolean functions with inhibiting inputs exerting a veto (forced zero) on the output. We give analytical expressions for the sensitivity of these functions and provide evidence for their role in natural systems.

Temporal networks: slowing down diffusion by long lasting interactions.

Interactions among units in complex systems occur in a specific sequential order, thus affecting the flow of information, the propagation of diseases, and general dynamical processes. We investigate the Laplacian spectrum of temporal networks and compare it with that of the corresponding aggregate network. First, we show that the spectrum of the ensemble average of a temporal network has identical eigenmodes but smaller eigenvalues than the aggregate networks.

Prediction of lethal and synthetically lethal knock-outs in regulatory networks.

The complex interactions involved in regulation of a cell's function are captured by its interaction graph. More often than not, detailed knowledge about enhancing or suppressive regulatory influences and cooperative effects is lacking and merely the presence or absence of directed interactions is known. Here, we investigate to which extent such reduced information allows to forecast the effect of a knock-out or a combination of knock-outs.

Landscape encodings enhance optimization.

Hard combinatorial optimization problems deal with the search for the minimum cost solutions (ground states) of discrete systems under strong constraints. A transformation of state variables may enhance computational tractability. It has been argued that these state encodings are to be chosen invertible to retain the original size of the state space.

A measure of individual role in collective dynamics.

Identifying key players in collective dynamics remains a challenge in several research fields, from the efficient dissemination of ideas to drug target discovery in biomedical problems. The difficulty lies at several levels: how to single out the role of individual elements in such intermingled systems, or which is the best way to quantify their importance. Centrality measures describe a node's importance by its position in a network.

Stability of Boolean and continuous dynamics.

Regulatory dynamics in biology is often described by continuous rate equations for continuously varying chemical concentrations. Binary discretization of state space and time leads to Boolean dynamics. In the latter, the dynamics has been called unstable if flip perturbations lead to damage spreading.

Efficient exploration of discrete energy landscapes.

Many physical and chemical processes, such as folding of biopolymers, are best described as dynamics on large combinatorial energy landscapes. A concise approximate description of the dynamics is obtained by partitioning the microstates of the landscape into macrostates. Since most landscapes of interest are not tractable analytically, the probabilities of transitions between macrostates need to be extracted numerically from the microscopic ones, typically by full enumeration of the state space or approximations using the Arrhenius law.

Robustness of Boolean dynamics under knockouts.

The response to a knockout of a node is a characteristic feature of a networked dynamical system. Knockout resilience in the dynamics of the remaining nodes is a sign of robustness. Here we study the effect of knockouts for binary state sequences and their implementations in terms of Boolean threshold networks.

Reconstruction of pedigrees in clonal plant populations.

We present a Bayesian method for the reconstruction of pedigrees in clonal populations using co-dominant genomic markers such as microsatellites and single nucleotide polymorphisms (SNPs). The accuracy of the algorithm is demonstrated for simulated data. We show that the joint estimation of parameters of interest such as the rate of self-fertilization is possible with high accuracy even with marker panels of moderate power.

FRANz: reconstruction of wild multi-generation pedigrees.

Summary: We present a software package for pedigree reconstruction in natural populations using co-dominant genomic markers such as microsatellites and single nucleotide polymorphisms (SNPs). If available, the algorithm makes use of prior information such as known relationships (sub-pedigrees) or the age and sex of individuals. Statistical confidence is estimated by Markov Chain Monte Carlo (MCMC) sampling.

Universal scaling in the branching of the tree of life.

Understanding the patterns and processes of diversification of life in the planet is a key challenge of science. The Tree of Life represents such diversification processes through the evolutionary relationships among the different taxa, and can be extended down to intra-specific relationships. Here we examine the topological properties of a large set of interspecific and intraspecific phylogenies and show that the branching patterns follow allometric rules conserved across the different levels in the Tree of Life, all significantly departing from those expected from the standard null models.

Statistics of cycles in large networks.

The occurrence of self-avoiding closed paths (cycles) in networks is studied under varying rules of wiring. As a main result, we find that the dependence between network size and typical cycle length is algebraic, (h) proportional to Nalpha, with distinct values of for different wiring rules. The Barabasi-Albert model has alpha=1.

Stable and unstable attractors in Boolean networks.

Boolean networks at the critical point have been a matter of debate for many years as, e.g., the scaling of numbers of attractors with system size.

Topology of biological networks and reliability of information processing.

Survival of living cells and organisms is largely based on highly reliable function of their regulatory networks. However, the elements of biological networks, e.g.

Scaling in the structure of directory trees in a computer cluster.

We describe the topological structure and the underlying organization principles of the directories created by users of a computer cluster. Users create trees with a scale-free degree distribution whose properties are reproduced by a growth and preferential attachment mechanism with a single parameter. The degree distribution has a nonuniversal exponent associated with different values of the parameter.

Effective dimensions and percolation in hierarchically structured scale-free networks.

We introduce appropriate definitions of dimensions in order to characterize the fractal properties of complex networks. We compute these dimensions in a hierarchically structured network of particular interest. In spite of the nontrivial character of this network that displays scale-free connectivity among other features, it turns out to be approximately one dimensional.