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159. The DOE Systems Biology Knowledgebase (KBase)

Author

Arkin, Adam P, primary, Stevens, Rick L, additional, Cottingham, Robert W, additional, Maslov, Sergei, additional, Henry, Christopher S, additional, Dehal, Paramvir, additional, Ware, Doreen, additional, Perez, Fernando, additional, Harris, Nomi L, additional, Canon, Shane, additional, Sneddon, Michael W, additional, Henderson, Matthew L, additional, Riehl, William J, additional, Gunter, Dan, additional, Murphy-Olson, Dan, additional, Chan, Stephen, additional, Kamimura, Roy T, additional, Brettin, Thomas S, additional, Meyer, Folker, additional, Chivian, Dylan, additional, Weston, David J, additional, Glass, Elizabeth M, additional, Davison, Brian H, additional, Kumari, Sunita, additional, Allen, Benjamin H, additional, Baumohl, Jason, additional, Best, Aaron A, additional, Bowen, Ben, additional, Brenner, Steven E, additional, Bun, Christopher C, additional, Chandonia, John-Marc, additional, Chia, Jer-Ming, additional, Colasanti, Ric, additional, Conrad, Neal, additional, Davis, James J, additional, DeJongh, Matthew, additional, Devoid, Scott, additional, Dietrich, Emily, additional, Drake, Meghan M, additional, Dubchak, Inna, additional, Edirisinghe, Janaka N, additional, Fang, Gang, additional, Faria, José P, additional, Frybarger, Paul M, additional, Gerlach, Wolfgang, additional, Gerstein, Mark, additional, Gurtowski, James, additional, Haun, Holly L, additional, He, Fei, additional, Jain, Rashmi, additional, Joachimiak, Marcin P, additional, Keegan, Kevin P, additional, Kondo, Shinnosuke, additional, Kumar, Vivek, additional, Land, Miriam L, additional, Mills, Marissa, additional, Novichkov, Pavel, additional, Oh, Taeyun, additional, Olsen, Gary J, additional, Olson, Bob, additional, Parrello, Bruce, additional, Pasternak, Shiran, additional, Pearson, Erik, additional, Poon, Sarah S, additional, Price, Gavin A, additional, Ramakrishnan, Srividya, additional, Ranjan, Priya, additional, Ronald, Pamela C, additional, Schatz, Michael C, additional, Seaver, Samuel M D, additional, Shukla, Maulik, additional, Sutormin, Roman A, additional, Syed, Mustafa H, additional, Thomason, James, additional, Tintle, Nathan L, additional, Wang, Daifeng, additional, Xia, Fangfang, additional, Yoo, Hyunseung, additional, and Yoo, Shinjae, additional

Published
2016
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161. The DOE Systems Biology Knowledgebase (KBase)

Author

Arkin, Adam, Arkin, Adam, Stevens, Rick, Cottingham, Robert, Maslov, Sergei, Henry, Christopher, Dehal, Paramvir, Ware, Doreen, Perez, Fernando, Harris, Nomi, Canon, Shane, Sneddon, Michael, Henderson, Matthew, Riehl, William, Gunter, Dan, Murphy-Olson, Dan, Chan, Stephen, Kamimura, Roy, Brettin, Thomas, Meyer, Folker, Chivian, Dylan, Weston, David, Glass, Elizabeth, Davison, Brian, Kumari, Sunita, Allen, Benjamin, Baumohl, Jason, Best, Aaron, Bowen, Ben, Brenner, Steven, Bun, Christopher, Chandonia, John-Marc, Chia, Jer-Ming, Colasanti, Ric, Conrad, Neal, Davis, James, DeJongh, Matthew, Devoid, Scott, Dietrich, Emily, Drake, Meghan, Dubchak, Inna, Edirisinghe, Janaka, Fang, Gang, Faria, José, Frybarger, Paul, Gerlach, Wolfgang, Gerstein, Mark, Gurtowski, James, Haun, Holly, He, Fei, Jain, Rashmi, Joachimiak, Marcin, Keegan, Kevin, Kondo, Shinnosuke, Kumar, Vivek, Land, Miriam, Mills, Marissa, Novichkov, Pavel, Oh, Taeyun, Olsen, Gary, Olson, Bob, Parrello, Bruce, Pasternak, Shiran, Pearson, Erik, Poon, Sarah, Price, Gavin, Ramakrishnan, Srividya, Ranjan, Priya, Ronald, Pamela, Schatz, Michael, Seaver, Samuel, Shukla, Maulik, Sutormin, Roman, Syed, Mustafa, Thomason, James, Tintle, Nathan, Wang, Daifeng, Xia, Fangfang, Yoo, Hyunseung, Yoo, Shinjae, Arkin, Adam, Arkin, Adam, Stevens, Rick, Cottingham, Robert, Maslov, Sergei, Henry, Christopher, Dehal, Paramvir, Ware, Doreen, Perez, Fernando, Harris, Nomi, Canon, Shane, Sneddon, Michael, Henderson, Matthew, Riehl, William, Gunter, Dan, Murphy-Olson, Dan, Chan, Stephen, Kamimura, Roy, Brettin, Thomas, Meyer, Folker, Chivian, Dylan, Weston, David, Glass, Elizabeth, Davison, Brian, Kumari, Sunita, Allen, Benjamin, Baumohl, Jason, Best, Aaron, Bowen, Ben, Brenner, Steven, Bun, Christopher, Chandonia, John-Marc, Chia, Jer-Ming, Colasanti, Ric, Conrad, Neal, Davis, James, DeJongh, Matthew, Devoid, Scott, Dietrich, Emily, Drake, Meghan, Dubchak, Inna, Edirisinghe, Janaka, Fang, Gang, Faria, José, Frybarger, Paul, Gerlach, Wolfgang, Gerstein, Mark, Gurtowski, James, Haun, Holly, He, Fei, Jain, Rashmi, Joachimiak, Marcin, Keegan, Kevin, Kondo, Shinnosuke, Kumar, Vivek, Land, Miriam, Mills, Marissa, Novichkov, Pavel, Oh, Taeyun, Olsen, Gary, Olson, Bob, Parrello, Bruce, Pasternak, Shiran, Pearson, Erik, Poon, Sarah, Price, Gavin, Ramakrishnan, Srividya, Ranjan, Priya, Ronald, Pamela, Schatz, Michael, Seaver, Samuel, Shukla, Maulik, Sutormin, Roman, Syed, Mustafa, Thomason, James, Tintle, Nathan, Wang, Daifeng, Xia, Fangfang, Yoo, Hyunseung, and Yoo, Shinjae

Abstract

The U.S. Department of Energy Systems Biology Knowledgebase (KBase) is an open-source software and data platform designed to meet the grand challenge of systems biology — predicting and designing biological function from the biomolecular (small scale) to the ecological (large scale). KBase is available for anyone to use, and enables researchers to collaboratively generate, test, compare, and share hypotheses about biological functions; perform large-scale analyses on scalable computing infrastructure; and combine experimental evidence and conclusions that lead to accurate models of plant and microbial physiology and community dynamics. The KBase platform has (1) extensible analytical capabilities that currently include genome assembly, annotation, ontology assignment, comparative genomics, transcriptomics, and metabolic modeling; (2) a web-browser-based user interface that supports building, sharing, and publishing reproducible and well-annotated analyses with integrated data; (3) access to extensive computational resources; and (4) a software development kit allowing the community to add functionality to the system.

Published
2016

173. Влияние инфраструктуры на развитие нижегородской области

Author

Маслов Сергей Александрович, ФГАОУ ВО «Национальный исследовательский Нижегородский государственный университет им. Н.И. Лобачевского», Maslov Sergei Aleksandrovich, FSAEI of HE "Lobachevsky State Research University of Nizhniy Novgorod", Маслов Сергей Александрович, ФГАОУ ВО «Национальный исследовательский Нижегородский государственный университет им. Н.И. Лобачевского», Maslov Sergei Aleksandrovich, and FSAEI of HE "Lobachevsky State Research University of Nizhniy Novgorod"

Abstract

В статье рассматривается состояние потенциала инфраструктуры Нижегородской области, приводится динамика изменений в различных сферах инфраструктуры за последние пять лет, приводятся аргументы в необходимости дальнейшего развития инфраструктуры Нижегородской области.

Published
2014

176. 1 / f Noise in Bak-Tang-Wiesenfeld Models on Narrow Stripes

Author

Maslov, Sergei, Tang, Chao, and Zhang, Yi-Cheng

Abstract

We report our findings of a 1/f power spectrum for the total amount of sand in directed and undirected Bak-Tang-Wiesenfeld models confined to narrow stripes and driven locally. The underlying mechanism for the 1/f noise in these systems is an exponentially long configuration memory giving rise to a very broad distribution of time scales. Both models are solved analytically with the help of an operator algebra to explicitly show the appearance of the long configuration memory.

Published
1999

181. Unexpected links reflect the noise in networks.

Author

Yambartsev, Anatoly, Perlin, Michael A., Kovchegov, Yevgeniy, Shulzhenko, Natalia, Mine, Karina L., Xiaoxi Dong, Morgun, Andrey, Koonin, Eugene, Maslov, Sergei, and Takahashi, Daniel Yasumasa

Subjects
BIOLOGICAL networks, BIOLOGICAL variation, INFERENCE (Logic), STATISTICAL correlation, BAYESIAN analysis
Abstract

Background: Gene covariation networks are commonly used to study biological processes. The inference of gene covariation networks from observational data can be challenging, especially considering the large number of players involved and the small number of biological replicates available for analysis. Results: We propose a new statistical method for estimating the number of erroneous edges in reconstructed networks that strongly enhances commonly used inference approaches. This method is based on a special relationship between sign of correlation (positive/negative) and directionality (up/down) of gene regulation, and allows for the identification and removal of approximately half of all erroneous edges. Using the mathematical model of Bayesian networks and positive correlation inequalities we establish a mathematical foundation for our method. Analyzing existing biological datasets, we find a strong correlation between the results of our method and false discovery rate (FDR). Furthermore, simulation analysis demonstrates that our method provides a more accurate estimate of network error than FDR. Conclusions: Thus, our study provides a new robust approach for improving reconstruction of covariation networks. [ABSTRACT FROM AUTHOR]

Published
2016
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182. Large-Scale Public Transcriptomic Data Mining Reveals a Tight Connection between the Transport of Nitrogen and Other Transport Processes in Arabidopsis.

Author

Fei He, Karve, Abhijit A., Maslov, Sergei, Babst, Benjamin A., Ponomarenko, Mikhail P., and Xiaoxiao Sun

Subjects
PLANT metabolism, NITRATE transporters, ARABIDOPSIS
Abstract

Movement of nitrogen to the plant tissues where it is needed for growth is an important contribution to nitrogen use efficiency. However, we have very limited knowledge about the mechanisms of nitrogen transport. Loading of nitrogen into the xylem and/or phloem by transporter proteins is likely important, but there are several families of genes that encode transporters of nitrogenous molecules (collectively referred to as N transporters here), each comprised of many gene members. In this study, we leveraged publicly available microarray data of Arabidopsis to investigate the gene networks of N transporters to elucidate their possible biological roles. First, we showed that tissue-specificity of nitrogen (N) transporters was well reflected among the public microarray data. Then, we built coexpression networks of N transporters, which showed relationships between N transporters and particular aspects of plant metabolism, such as phenylpropanoid biosynthesis and carbohydrate metabolism. Furthermore, genes associated with several biological pathways were found to be tightly coexpressed with N transporters in different tissues. Our coexpression networks provide information at the systems-level that will serve as a resource for future investigation of nitrogen transport systems in plants, including candidate gene clusters that may work together in related biological roles. [ABSTRACT FROM AUTHOR]

Published
2016
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