Evidence for Network Evolution in an
            <i>Arabidopsis</i>
            Interactome Map

Dreze, Matija; Carvunis, Anne‐Ruxandra; Charloteaux, Benoît; Galli, Mary; Pevzner, Samuel; Taşan, Murat; Ahn, Yong‐Yeol; Balumuri, Padmavathi; Barabási, Albert‐László; Bautista, Vanessa; Braun, Pascal; Byrdsong, Danielle; Chen, Huaming; Chesnut, Jonathan D.; Cusick, Michael E.; Dangl, Jeffery L.; Reyes, Chris de los; Dricot, Amélie; Duarte, Melissa; Ecker, Joseph R.; Fan, Changyu; Gai, Lantian; Gebreab, Fana; Ghoshal, Gourab; Gilles, Patrick N.; Gutierrez, Bryan J.; Hao, Tong; Hill, David E.; Kim, Christopher J.; Kim, Rosa Cheuk; Lurin, Claire; MacWilliams, Andrew; Matrubutham, Uday; Milenković, Tijana; Mirchandani, Jyotika; Monachello, Dario; Moore, Jonathan D.; Mukhtar, M. Shahid; Olivares, Eric C.; Patnaik, Suswapna; Poulin, Matthew M.; Pržulj, Nataša; Quan, Rosa; Rabello, Sabrina; Ramaswamy, Gopalakrishna; Reichert, Patrick; Rietman, Edward A.; Rolland, Thomas; Romero, Viviana; Roth, Frederick P.; Balaji, S.; Schmitz, Robert J.; Shinn, Paul; Spooner, William H.; Stein, Joshua C.; Swamilingiah, Geetha M.; Tam, Stanley; Vandenhaute, Jean; Vidal, Marc; Waaijers, Selma; Ware, Doreen; Weiner, Evan M.; Wu, S. H.; Yazaki, Junshi

doi:10.1126/science.1203877

Cited by 821 publications

(445 citation statements)

References 42 publications

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“…Nevertheless, the resolution of a signaling or interaction network depends on the extent of coregulatory data available to inform the graph edges. Our analysis required that we compare the coregulation and modularity of the same set of genes; however, inclusion of genes linked to the synaptic network that are not shared between the comparison groups would likely improve the coregulation signal as gene innovation and duplication can affect network structure through dynamic interactome rewiring (35). Although these limitations increase the likelihood of detecting biologically spurious correlations and may contribute to the apparent modularity observed in some random gene sets, the ability of the community detection algorithm to partition genes into their respective cellular machines indicates a functional correlate of the structural communities derived simply from transcriptional coregulation (Fig.…”

Section: Discussionmentioning

confidence: 99%

Functionalization of a protosynaptic gene expression network

Conaco

Bassett

Zhou

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

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Assembly of a functioning neuronal synapse requires the precisely coordinated synthesis of many proteins. To understand the evolution of this complex cellular machine, we tracked the developmental expression patterns of a core set of conserved synaptic genes across a representative sampling of the animal kingdom. Coregulation, as measured by correlation of gene expression over development, showed a marked increase as functional nervous systems emerged. In the earliest branching animal phyla (Porifera), in which a nearly complete set of synaptic genes exists in the absence of morphological synapses, these "protosynaptic" genes displayed a lack of global coregulation although small modules of coexpressed genes are readily detectable by using network analysis techniques. These findings suggest that functional synapses evolved by exapting preexisting cellular machines, likely through some modification of regulatory circuitry. Evolutionarily ancient modules continue to operate seamlessly within the synapses of modern animals. This work shows that the application of network techniques to emerging genomic and expression data can provide insights into the evolution of complex cellular machines such as the synapse. synapse evolution | community detection | developmental transcriptome |

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Section: Discussionmentioning

confidence: 99%

Functionalization of a protosynaptic gene expression network

Conaco

Bassett

Zhou

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

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“…Such a mapping is represented by a kernel matrix [148]. A kernel matrix, K, is a symmetric, positive semi-definite matrix 7 with entries…”

Section: Kernel-based Methodsmentioning

confidence: 99%

Methods for biological data integration: perspectives and challenges

Gligorijević¹,

Pržulj²

2015

J. R. Soc. Interface.

238

157

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Rapid technological advances have led to the production of different types of biological data and enabled construction of complex networks with various types of interactions between diverse biological entities. Standard network data analysis methods were shown to be limited in dealing with such heterogeneous networked data and consequently, new methods for integrative data analyses have been proposed. The integrative methods can collectively mine multiple types of biological data and produce more holistic, systems-level biological insights. We survey recent methods for collective mining (integration) of various types of networked biological data. We compare different stateof-the-art methods for data integration and highlight their advantages and disadvantages in addressing important biological problems. We identify the important computational challenges of these methods and provide a general guideline for which methods are suited for specific biological problems, or specific data types. Moreover, we propose that recent non-negative matrix factorization-based approaches may become the integration methodology of choice, as they are well suited and accurate in dealing with heterogeneous data and have many opportunities for further development.

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“…Interactome data are available for increasing numbers of tissues in different plant species (35,113) but may still underestimate the capacity of these interactions to vary in response to environmental and developmental changes. There is indeed increasing evidence that enzymes of central metabolism can be regulated by docking, interactions with scaffold proteins, or relocalization on membranes.…”

Section: Plant Metabolism At the Genomic Transcriptomic And Proteommentioning

confidence: 99%

Moving Toward a Comprehensive Map of Central Plant Metabolism

Sulpice¹,

McKeown

2015

Annu. Rev. Plant Biol.

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Decades of intensive study have led to the discovery of the main pathways involved in central metabolism but only some of the pathways and regulatory networks in which they are embedded. In this review, we discuss techniques used to assemble these pathways into a systems biology framework that can enable accurate modeling of the response of central metabolism to changes, including ways to perturb metabolic systems and assemble the resulting data into a meaningful network. Critically, these networks are of such size and complexity that it is possible to derive them only if data from different groups can be comprehensively and meaningfully combined. We conclude that it is essential to establish common standards for the description of experimental conditions and data collection and to store this information in databases to which the whole community can contribute.

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Evidence for Network Evolution in an Arabidopsis Interactome Map

Cited by 821 publications

References 42 publications

Functionalization of a protosynaptic gene expression network

Functionalization of a protosynaptic gene expression network

Methods for biological data integration: perspectives and challenges

Moving Toward a Comprehensive Map of Central Plant Metabolism

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