A modified galactose network model with implications for growth

Autor: Monti, Michele, Matos, Marta R A, Choi, Jeong-Mo, Ferry, Michael S, Borek, Bartlomiej

The yeast galactose network has provided many insights into how eukaryotic gene circuits regulate metabolic function. However, there is currently no consensus model of the network that incorporates protein dilution due to cellular growth. We address

Externí odkaz: http://arxiv.org/abs/1601.03235

GRASP: a computational platform for building kinetic models of cellular metabolism.

Autor: Matos, Marta R A¹, Saa, Pedro A^2,3, Cowie, Nicholas¹, Volkova, Svetlana¹, Leeuw, Marina de¹, Nielsen, Lars K^1,4 lars.nielsen@uq.edu.au

Publikováno v: Bioinformatics Advances. 2022, Vol. 2 Issue 1, p1-3. 3p.

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Inferring causal molecular networks: empirical assessment through a community-based effort

Autor: Hill, Steven M, Heiser, Laura M, Graim, Kiley, Jiang, Xia, Kacprowski, Tim, Kaderali, Lars, Kang, Mingon, Kannan, Venkateshan, Kellen, Michael, Kikuchi, Kaito, Kim, Dong-Chul, Kitano, Hiroaki, Knapp, Bettina, Bivol, Adrian, Komatsoulis, George, Koeppl, Heinz, Krämer, Andreas, Kursa, Miron Bartosz, Kutmon, Martina, Lee, Wai Shing, Li, Yichao, Liang, Xiaoyu, Liu, Zhaoqi, Liu, Yu, Wang, Haizhou, Long, Byron L, Lu, Songjian, Lu, Xinghua, Manfrini, Marco, Matos, Marta R A, Meerzaman, Daoud, Mills, Gordon B, Min, Wenwen, Mukherjee, Sach, Müller, Christian Lorenz, Zhu, Fan, Neapolitan, Richard E, Nesser, Nicole K, Noren, David P, Norman, Thea, Oliva, Baldo, Opiyo, Stephen Obol, Pal, Ranadip, Palinkas, Aljoscha, Paull, Evan O, Planas-Iglesias, Joan, Afsari, Bahman, Poglayen, Daniel, Qutub, Amina A, Saez-Rodriguez, Julio, Sambo, Francesco, Sanavia, Tiziana, Sharifi-Zarchi, Ali, Slawek, Janusz, Sokolov, Artem, Song, Mingzhou, Spellman, Paul T, Danilova, Ludmila V, Streck, Adam, Stolovitzky, Gustavo, Strunz, Sonja, Stuart, Joshua M, Taylor, Dane, Tegnér, Jesper, Thobe, Kirste, Toffolo, Gianna Maria, Trifoglio, Emanuele, Unger, Michael, Favorov, Alexander V, Wan, Qian, Welch, Lonnie, Wong, Chris K, Wu, Jia J, Xue, Albert Y, Yamanaka, Ryota, Yan, Chunhua, Zairis, Sakellarios, Zengerling, Michael, Zenil, Hector, Zhang, Shihua, Zhang, Yang, Zi, Zhike, Hu, Chenyue W, Cokelaer, Thomas, Bisberg, Alexander J, Consortium, HPN-DREAM, Gray, Joe W, Friend, Stephen, Fertig, Elana J, Guan, Yuanfang, Al-Ouran, Rami, Anton, Bernat, Arodz, Tomasz, Sichani, Omid Askari, Bagheri, Neda, Berlow, Noah, Bohler, Anwesha, Bonet, Jaume, Carlin, Daniel E, Bonneau, Richard, Budak, Gungor, Bunescu, Razvan, Caglar, Mehmet, Cai, Binghuang, Cai, Chunhui, Carlon, Azzurra, Chen, Lujia, Ciaccio, Mark F, Cooper, Gregory, Coort, Susan, Creighton, Chad J, Daneshmand, Seyed-Mohammad-Hadi, de la Fuente, Alberto, Di Camillo, Barbara, Dutta-Moscato, Joyeeta, Emmett, Kevin, Evelo, Chris, Fassia, Mohammad-Kasim H, Finkle, Justin D, Finotello, Francesca, Gao, Xi, Gao, Jean, Garcia-Garcia, Javier, Ghosh, Samik, Giaretta, Alberto, Großeholz, Ruth, Guinney, Justin, Hafemeister, Christoph, Hahn, Oliver, Haider, Saad, Hase, Takeshi, Hodgson, Jay, Hoff, Bruce, Hsu, Chih Hao, Hu, Ying, Huang, Xun, Jalili, Mahdi

Publikováno v: Nature methods 13(4), 310-318 (2016). doi:10.1038/nmeth.3773
Recercat. Dipósit de la Recerca de Catalunya
instname
Nature methods, 13(4): 310-318
Nature methods
Nature Methods, 13(4), 310-318. Nature Publishing Group

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal infl

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5a25514e80f8faf329feff49e3987fc7
http://hdl.handle.net/11577/3190821