A unifying framework for fast randomization of ecological networks with fixed (node) degrees

Carstens, Corrie Jacobien; Berger, Annabell; Strona, Giovanni

doi:10.1016/j.mex.2018.06.018

Cited by 11 publications

(13 citation statements)

References 42 publications

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“…The purpose of this second randomisation is to test what motifs found in the first randomisation remain significant if the proportion of links within the regulatory set and their degrees are fixed, hence highlighting their importance in regulation. This randomisation was implemented following an extension of the curveball algorithm for directed networks (Carstens et al ., 2018). We verified that 10 5 steps of the algorithm were sufficient to ensure an unbiased search.…”

Section: Methodsmentioning

confidence: 99%

A gene regulatory network critical for axillary bud dormancy directly controlled by Arabidopsis BRANCHED1

van Es,

Muñoz‐Gasca,

Romero‐Campero

et al. 2023

New Phytologist

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Summary The Arabidopsis thaliana transcription factor BRANCHED1 (BRC1) plays a pivotal role in the control of shoot branching as it integrates environmental and endogenous signals that influence axillary bud growth. Despite its remarkable activity as a growth inhibitor, the mechanisms by which BRC1 promotes bud dormancy are largely unknown. We determined the genome‐wide BRC1 binding sites in vivo and combined these with transcriptomic data and gene co‐expression analyses to identify bona fide BRC1 direct targets. Next, we integrated multi‐omics data to infer the BRC1 gene regulatory network (GRN) and used graph theory techniques to find network motifs that control the GRN dynamics. We generated an open online tool to interrogate this network. A group of BRC1 target genes encoding transcription factors (BTFs) orchestrate this intricate transcriptional network enriched in abscisic acid‐related components. Promoter::β‐GLUCURONIDASE transgenic lines confirmed that BTFs are expressed in axillary buds. Transient co‐expression assays and studies in planta using mutant lines validated the role of BTFs in modulating the GRN and promoting bud dormancy. This knowledge provides access to the developmental mechanisms that regulate shoot branching and helps identify candidate genes to use as tools to adapt plant architecture and crop production to ever‐changing environmental conditions.

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Section: Methodsmentioning

confidence: 99%

A gene regulatory network critical for axillary bud dormancy directly controlled by Arabidopsis BRANCHED1

van Es,

Muñoz‐Gasca,

Romero‐Campero

et al. 2023

New Phytologist

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“…Curveball is a novel approach that instead considers the whole neighborhoods of randomly drawn node pairs. Its Markov chain converges to a uniform distribution, and experiments suggest that it requires less steps than the established ES [47]. Since trades however are more expensive, we study Curveball's practical runtime by introducing the first e icient Curveball algorithms: the I/O-e icient EM-CB for simple undirected graphs and its internal memory pendant IM CB.…”

Section: Discussionmentioning

confidence: 99%

“…Similarly to techniques of [47,48], our proof of Theorem 3.5 requires a small positive probability that any global switch collapses into a single switch. We implement this by independently rejecting each switch with probability P L .…”

Section: Global Edge Switching Markov Chain (G-es-mc)mentioning

confidence: 99%

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Uniform sampling of simple graphs with prescribed degrees and experimental algorithms for external memory

Hung The

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Die Emergenz digitaler Netzwerke ist auf die ständige Entwicklung und Transformation neuer Informationstechnologien zurückzuführen. Dieser Strukturwandel führt zu äußerst komplexen Systemen in vielen verschiedenen Lebensbereichen. Es besteht daher verstärkt die Notwendigkeit, die zugrunde liegenden wesentlichen Eigenschaften von realen Netzwerken zu untersuchen und zu verstehen. In diesem Zusammenhang wird die Netzwerkanalyse als Mittel für die Untersuchung von Netzwerken herangezogen und stellt beobachtete Strukturen mithilfe mathematischer Modelle dar. Hierbei, werden in der Regel parametrisierbare Zufallsgraphen verwendet, um eine systematische experimentelle Evaluation von Algorithmen und Datenstrukturen zu ermöglichen. Angesichts der zunehmenden Menge an Informationen, sind viele Aspekte der Netzwerkanalyse datengesteuert und zur Interpretation auf effiziente Algorithmen angewiesen. Algorithmische Lösungen müssen daher sowohl die strukturellen Eigenschaften der Eingabe als auch die Besonderheiten der zugrunde liegenden Maschinen, die sie ausführen, sorgfältig berücksichtigen. Die Generierung und Analyse massiver Netzwerke ist dementsprechend eine anspruchsvolle Aufgabe für sich. Die vorliegende Arbeit bietet daher algorithmische Lösungen für die Generierung und Analyse massiver Graphen. Zu diesem Zweck entwickeln wir Algorithmen für das Generieren von Graphen mit vorgegebenen Knotengraden, die Berechnung von Zusammenhangskomponenten massiver Graphen und zertifizierende Grapherkennung für Instanzen, die die Größe des Hauptspeichers überschreiten. Unsere Algorithmen und Implementierungen sind praktisch effizient für verschiedene Maschinenmodelle und bieten sequentielle, Shared-Memory parallele und/oder I/O-effiziente Lösungen.

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“…There are several methods to reorganize them without altering row and column totals. One of these approaches is the so-called Curveball algorithm, published in [25,26,82]. We can use this algorithm as follows (exemplified by Fig.…”

Section: Sa With Curveball Algorithmmentioning

confidence: 99%

Texture, Nonogram, and Convexity Priors in Binary Tomography

Szűcs

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I would also like to thank Zsófia Beján-Gábriel for correcting this thesis from a linguistic point of view.Last but not least, I would like to thank my family, especially my mother, my father, my grandmother, and my significant other for supporting me during my studies. Without their support, this PhD thesis would not have been possible.Finally, I would like to thank the Doctoral School of Computer Science and the Department of Image Processing and Computer Graphics of the University of Szeged for providing me with a productive work environment.

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A unifying framework for fast randomization of ecological networks with fixed (node) degrees

Abstract: Graphical abstract

Cited by 11 publications

References 42 publications

A gene regulatory network critical for axillary bud dormancy directly controlled by Arabidopsis BRANCHED1

A gene regulatory network critical for axillary bud dormancy directly controlled by Arabidopsis BRANCHED1

Uniform sampling of simple graphs with prescribed degrees and experimental algorithms for external memory

Texture, Nonogram, and Convexity Priors in Binary Tomography

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