Conedy: A scientific tool to investigate complex network dynamics

Rothkegel, Alexander; Lehnertz, Klaus

doi:10.1063/1.3685527

Cited by 19 publications

(17 citation statements)

References 70 publications

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“…Conedy, a Python-interfaced C++ program capable to handle various dynamics including differential equations and oscillators (http://www.conedy.org; Rothkegel & Lehnertz, 2012). …”

Section: An Inventory Of Network Analysis Tools Helping Drug Designmentioning

confidence: 99%

Structure and dynamics of molecular networks: A novel paradigm of drug discovery

Csermely

Korcsmáros

Kiss

et al. 2013

Pharmacology & Therapeutics

821

661

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Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only gives a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The “central hit strategy” selectively targets central node/edges of the flexible networks of infectious agents or cancer cells to kill them. The “network influence strategy” works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.

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“…Conedy, a Python-interfaced C++ program capable to handle various dynamics including differential equations and oscillators (http://www.conedy.org; Rothkegel & Lehnertz, 2012). …”

Section: An Inventory Of Network Analysis Tools Helping Drug Designmentioning

confidence: 99%

Structure and dynamics of molecular networks: A novel paradigm of drug discovery

Csermely

Korcsmáros

Kiss

et al. 2013

Pharmacology & Therapeutics

821

661

View full text Add to dashboard Cite

show abstract

“…However, there are also a handful of programs for non-Boolean dynamics. ITM-Probe [17] is based on random-walks; PerturbationAnalyzer [18] is a Cytoscape plug-in using the law of mass action; BIOCHAM [19] and Conedy [20] are more complex network dynamic tools. Turbine combines several advantages of the former options with large versatility, richness of output data, efficient use of memory and fast running time enabling the analysis of large networks ( Table S6 of Text S1 ).…”

Section: Discussionmentioning

confidence: 99%

“…However, to our knowledge, no stand-alone program exists, which can easily integrate any dynamical models together with any types of starting perturbations, and can also provide the complete time-domain simulation results, not only the summative end-result. Recently the complex network dynamics simulation tool, Conedy was introduced [20]. Conedy is a Python extension enabling researchers already using computational dynamics to add networks to their repertoire.…”

Section: Introductionmentioning

confidence: 99%

Perturbation Centrality and Turbine: A Novel Centrality Measure Obtained Using a Versatile Network Dynamics Tool

Szalay

Csermely

2013

PLoS ONE

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Analysis of network dynamics became a focal point to understand and predict changes of complex systems. Here we introduce Turbine, a generic framework enabling fast simulation of any algorithmically definable dynamics on very large networks. Using a perturbation transmission model inspired by communicating vessels, we define a novel centrality measure: perturbation centrality. Hubs and inter-modular nodes proved to be highly efficient in perturbation propagation. High perturbation centrality nodes of the Met-tRNA synthetase protein structure network were identified as amino acids involved in intra-protein communication by earlier studies. Changes in perturbation centralities of yeast interactome nodes upon various stresses well recapitulated the functional changes of stressed yeast cells. The novelty and usefulness of perturbation centrality was validated in several other model, biological and social networks. The Turbine software and the perturbation centrality measure may provide a large variety of novel options to assess signaling, drug action, environmental and social interventions.

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“…(1) represents an integrate-and-fire oscillator 53,63,64 endowed with refractory periods H n and time delay s between firing and receiving of a pulse. Numerical simulations were performed with Conedy, 65 which uses eventdriven algorithms to realize the dynamics. To account for transients, we ignored the first 10 3 time steps of observation.…”

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confidence: 99%

Transitions between dynamical behaviors of oscillator networks induced by diversity of nodes and edges

Werner

Lehnertz

2015

Chaos: An Interdisciplinary Journal of Nonlinear Science

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We study the impact of dynamical and structural heterogeneity on the collective dynamics of large small-world networks of pulse-coupled integrate-and-fire oscillators endowed with refractory periods and time delay. Depending on the choice of homogeneous control parameters (here, refractoriness and coupling strength), these networks exhibit a large spectrum of dynamical behaviors, including asynchronous, partially synchronous, and fully synchronous states. Networks exhibit transitions between these dynamical behaviors upon introducing heterogeneity. We show that the probability for a network to exhibit a certain dynamical behavior (network susceptibility) is affected differently by dynamical and structural heterogeneity and depends on the respective homogeneous dynamics.

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Conedy: A scientific tool to investigate complex network dynamics

Abstract: The following article has been accepted by Chaos. After it is published, it will be found at http://link.aip.org/link/?cha.

Cited by 19 publications

References 70 publications

Structure and dynamics of molecular networks: A novel paradigm of drug discovery

Structure and dynamics of molecular networks: A novel paradigm of drug discovery

Perturbation Centrality and Turbine: A Novel Centrality Measure Obtained Using a Versatile Network Dynamics Tool

Transitions between dynamical behaviors of oscillator networks induced by diversity of nodes and edges

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