Results 1  10
of
43
Recent advances in clustering methods for protein interaction networks
 BMC genomics, 11(Suppl 3):S10
, 2010
"... Recent advances in clustering methods for protein interaction networks ..."
Abstract

Cited by 16 (0 self)
 Add to MetaCart
(Show Context)
Recent advances in clustering methods for protein interaction networks
Synchronization in Complex Networks of Phase Oscillators: A Survey
, 2014
"... The emergence of synchronization in a network of coupled oscillators is a fascinating subject of multidisciplinary research. This survey reviews the vast literature on the theory and the applications of complex oscillator networks. We focus on phase oscillator models that are widespread in realworl ..."
Abstract

Cited by 13 (0 self)
 Add to MetaCart
The emergence of synchronization in a network of coupled oscillators is a fascinating subject of multidisciplinary research. This survey reviews the vast literature on the theory and the applications of complex oscillator networks. We focus on phase oscillator models that are widespread in realworld synchronization phenomena, that generalize the celebrated Kuramoto model, and that feature a rich phenomenology. We review the history and the countless applications of this model throughout science and engineering. We justify the importance of the widespread coupled oscillator model as a locally canonical model and describe some selected applications relevant to control scientists, including vehicle coordination, electric power networks, and clock synchronization. We introduce the reader to several synchronization notions and performance estimates. We propose analysis approaches to phase and frequency synchronization, phase balancing, pattern formation, and partial synchronization. We present the sharpest known results about synchronization in networks of homogeneous and heterogeneous oscillators, with complete or sparse interconnection topologies, and in finitedimensional and infinitedimensional settings. We conclude by summarizing the limitations of existing analysis methods and by highlighting some directions for future research.
Statistical Density Prediction in Traffic Networks
, 2008
"... Recently, modern tracking methods started to allow capturing the position of massive numbers of moving objects. Given this information, it is possible to analyze and predict the traffic density in a network which offers valuable information for traffic control, congestion prediction and prevention. ..."
Abstract

Cited by 10 (2 self)
 Add to MetaCart
(Show Context)
Recently, modern tracking methods started to allow capturing the position of massive numbers of moving objects. Given this information, it is possible to analyze and predict the traffic density in a network which offers valuable information for traffic control, congestion prediction and prevention. In this paper, we propose a novel statistical approach to predict the density on any edge of such a network at some time in the future. Our method is based on shorttime observations of the traffic history. Therefore, knowing the destination of each traveling individual is not required. Instead, we assume that the individuals will act rationally and choose the shortest path from their starting points to their destinations. Based on this assumption, we introduce a statistical approach to describe the likelihood of any given individual in the network to be located at a certain position at a certain time. Since determining this likelihood is quite expensive when done in a straightforward way, we propose an efficient method to speed up the prediction which is based on a suffixtree. In our experiments, we show the capability of our approach to make useful predictions about the traffic density and illustrate the efficiency of our new algorithm when calculating these predictions.
Fundamental statistical features and selfsimilar properties of tagged networks
 New Journal of Physics
"... Abstract. We investigate the fundamental statistical features of tagged (or annotated) networks having a rich variety of attributes associated with their nodes. Tags (attributes, annotations, properties, features, etc.) provide essential information about the entity represented by a given node, thus ..."
Abstract

Cited by 9 (0 self)
 Add to MetaCart
(Show Context)
Abstract. We investigate the fundamental statistical features of tagged (or annotated) networks having a rich variety of attributes associated with their nodes. Tags (attributes, annotations, properties, features, etc.) provide essential information about the entity represented by a given node, thus, taking them into account represents a significant step towards a more complete description of the structure of large complex systems. Our main goal here is to uncover the relations between the statistical properties of the node tags and those of the graph topology. In order to better characterise the networks with tagged nodes, we introduce a number of new notions, including tagassortativity (relating link probability to node similarity), and new quantities, such as node uniqueness (measuring how rarely the tags of a node occur in the network) and tagassortativity exponent. We apply our approach to three large networks representing very different domains of complex systems. A number of the tag related quantities display analogous behaviour (e.g., the networks we studied are tagassortative, indicating possible universal aspects of tags versus topology), while some other features, such as the distribution of the node uniqueness, show variability from network to network allowing for pinpointing large scale specific features of realworld complex networks. We also find that for each network the topology and the tag distribution are scale invariant, and this selfsimilar property of the networks can be well characterised by the tagassortativity exponent, which is specific to each system. PACS numbers: 02.70.Rr, 05.10.a, 87.16.Yc, 89.20.a, 89.75.HcFundamental statistical features and selfsimilar properties of tagged networks 2 1.
Engineering Principles in Biomolecular Systems: From Retroactivity to Modularity
"... Abstract — Modularity plays a fundamental role in the prediction of the behavior of a system from the behavior of its components, guaranteeing that the properties of individual components do not change upon interconnection. Just as electrical, hydraulic, and other physical systems often do not displ ..."
Abstract

Cited by 8 (3 self)
 Add to MetaCart
(Show Context)
Abstract — Modularity plays a fundamental role in the prediction of the behavior of a system from the behavior of its components, guaranteeing that the properties of individual components do not change upon interconnection. Just as electrical, hydraulic, and other physical systems often do not display modularity, nor do many biochemical systems, and specifically, genetic and signaling networks. Here, we study the effect of interconnections on the input/output dynamic characteristics of transcriptional components, focusing on a concept, which we call “retroactivity ” that plays a role similar to impedance in electrical circuits. In order to attenuate the effect of retroactivity on a system dynamics, we propose to design insulation devices based on a feedback mechanism inspired by the design of amplifiers in electronics. In particular, we introduce a biomolecular realization of an insulation device based on phosphorylation. 1 I.
The Rôle of Control and System Theory in Systems Biology ∗ Preamble
, 2007
"... The use of new technology and mathematics to study the systems of nature is one of the most significant scientific trends of the century. Driven by the need for more precise scientific understand, advances in automated measurement are providing rich new sources of biological and physiological data. ..."
Abstract

Cited by 7 (1 self)
 Add to MetaCart
(Show Context)
The use of new technology and mathematics to study the systems of nature is one of the most significant scientific trends of the century. Driven by the need for more precise scientific understand, advances in automated measurement are providing rich new sources of biological and physiological data. This data provides information with which to create mathematical models of increasing sophistication and realism models that can emulate the performance of biological and physiological systems with sufficient accuracy to advance our understanding of living systems and disease mechanisms. New measurement and modelling methods set the stage for control and systems theory to play their rôle in seeking out the mechanisms and principles that regulate life. It is of inestimable importance for the future of control as a discipline that this rôle is performed in the correct manner. If we handle the area wisely then living systems will present a seemly boundless range of important new problems just as physical and engineering systems have done in previous centuries. But there is a crucial difficulty. Faced with a bewildering array of
ENTROPY BOUNDS FOR HIERARCHICAL MOLECULAR NETWORKS
, 2008
"... In this paper we derive entropy bounds for hierarchical networks. More precisely, starting from a recently introduced measure to determine the topological entropy of nonhierarchical networks, we provide bounds for estimating the entropy of hierarchical graphs. Apart from bounds to estimate the en ..."
Abstract

Cited by 4 (3 self)
 Add to MetaCart
(Show Context)
In this paper we derive entropy bounds for hierarchical networks. More precisely, starting from a recently introduced measure to determine the topological entropy of nonhierarchical networks, we provide bounds for estimating the entropy of hierarchical graphs. Apart from bounds to estimate the entropy of a single hierarchical graph, we see that the derived bounds can also be used for characterizing graph classes. Our contribution is an important extension to previous results about the entropy of nonhierarchical networks because for practical applications hierarchical networks are playing an important role in chemistry and biology. In addition to the derivation of the entropy bounds, we provide a numerical analysis for two special graph classes, rooted trees and generalized trees, and demonstrate hereby not only the computational feasibility of our method but also learn about its characteristics and interpretability with respect to data analysis.
Convex Graph Invariants
, 2010
"... The structural properties of graphs are usually characterized in terms of invariants, which are functions of graphs that do not depend on the labeling of the nodes. In this paper we study convex graph invariants, which are graph invariants that are convex functions of the adjacency matrix of a graph ..."
Abstract

Cited by 3 (2 self)
 Add to MetaCart
(Show Context)
The structural properties of graphs are usually characterized in terms of invariants, which are functions of graphs that do not depend on the labeling of the nodes. In this paper we study convex graph invariants, which are graph invariants that are convex functions of the adjacency matrix of a graph. Some examples include functions of a graph such as the maximum degree, the MAXCUT value (and its semidefinite relaxation), and spectral invariants such as the sum of the k largest eigenvalues. Such functions can be used to construct convex sets that impose various structural constraints on graphs, and thus provide a unified framework for solving a number of interesting graph problems via convex optimization. We give a representation of all convex graph invariants in terms of certain elementary invariants, and describe methods to compute or approximate convex graph invariants tractably. We also compare convex and nonconvex invariants, and discuss connections to robust optimization. Finally we use convex graph invariants to provide efficient convex programming solutions to graph problems such as the deconvolution of the composition of two graphs into the individual components, hypothesis testing between graph families, and the generation of graphs with certain desired structural properties.
Internet Accessible
 Lanzhou University, Lanzhou
, 1998
"... Genetic variants of p27 and p21 as predictors for risk of second primary malignancy in patients with index squamous cell carcinoma of head and neck ..."
Abstract

Cited by 2 (2 self)
 Add to MetaCart
(Show Context)
Genetic variants of p27 and p21 as predictors for risk of second primary malignancy in patients with index squamous cell carcinoma of head and neck
in Modeling in Systems Biology
"... Petri netbased method for the analysis of the dynamics of signal propagation in signaling pathways ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
(Show Context)
Petri netbased method for the analysis of the dynamics of signal propagation in signaling pathways