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2. CD19-CD28: an affinity-optimized CD28 agonist for combination with glofitamab (CD20-TCB) as off-the-shelf immunotherapy

Author

Sam, Johannes, Hofer, Thomas, Kuettel, Christine, Claus, Christina, Thom, Jenny, Herter, Sylvia, Georges, Guy, Korfi, Koorosh, Lechmann, Martin, Eigenmann, Miro Julian, Marbach, Daniel, Jamois, Candice, Lechner, Katharina, Krishnan, Sreenath M., Gaillard, Brenda, Marinho, Joana, Kronenberg, Sven, Kunz, Leo, Wilson, Sabine, Briner, Stefanie, Gebhardt, Samuel, Varol, Ahmet, Appelt, Birte, Nicolini, Valeria, Speziale, Dario, Bez, Miriam, Bommer, Esther, Eckmann, Jan, Hage, Carina, Limani, Florian, Jenni, Silvia, Schoenle, Anne, Le Clech, Marine, Vallier, Jean-Baptiste Pierre, Colombetti, Sara, Bacac, Marina, Gasser, Stephan, Klein, Christian, and Umaña, Pablo

Published
2024
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3. A Community Challenge for Inferring Genetic Predictors of Gene Essentialities through Analysis of a Functional Screen of Cancer Cell Lines

Author

Gönen, Mehmet, Weir, Barbara A, Cowley, Glenn S, Vazquez, Francisca, Guan, Yuanfang, Jaiswal, Alok, Karasuyama, Masayuki, Uzunangelov, Vladislav, Wang, Tao, Tsherniak, Aviad, Howell, Sara, Marbach, Daniel, Hoff, Bruce, Norman, Thea C, Airola, Antti, Bivol, Adrian, Bunte, Kerstin, Carlin, Daniel, Chopra, Sahil, Deran, Alden, Ellrott, Kyle, Gopalacharyulu, Peddinti, Graim, Kiley, Kaski, Samuel, Khan, Suleiman A, Newton, Yulia, Ng, Sam, Pahikkala, Tapio, Paull, Evan, Sokolov, Artem, Tang, Hao, Tang, Jing, Wennerberg, Krister, Xie, Yang, Zhan, Xiaowei, Zhu, Fan, Community, Broad-DREAM, Afsari, Bahman, Aittokallio, Tero, Boehm, Jesse S, Chang, Yu-Chuan, Chen, Tenghui, Chong, Zechen, Elmarakeby, Haitham, Fertig, Elana J, Gonçalves, Emanuel, Gong, Pinghua, Hafemeister, Christoph, Hahn, William C, Heath, Lenwood, Kędziorski, Łukasz, Khemka, Niraj, King, Erh-kan, Lauria, Mario, Liu, Mark, Machado, Daniel, Mamitsuka, Hiroshi, Margolin, Adam A, Mazurkiewicz, Mateusz, Menden, Michael P, Migacz, Szymon, Nie, Zhi, Praveen, Paurush, Priami, Corrado, Rizzetto, Simone, Rocha, Miguel, Root, David E, Rudd, Cameron, Rudnicki, Witold R, Saez-Rodriguez, Julio, Song, Lei, Stolovitzky, Gustavo, Stuart, Joshua M, Sun, Duanchen, and Szalai, Bence

Subjects
Biological Sciences, Genetics, Biotechnology, Prevention, Cancer, Human Genome, Algorithms, Cell Line, Tumor, Gene Expression, Genes, Essential, Genomics, Humans, RNA, Small Interfering, Broad-DREAM Community, cancer genomics, community challenge, crowdsourcing, functional screen, machine learning, oncogene, Biochemistry and Cell Biology, Biochemistry and cell biology
Abstract

We report the results of a DREAM challenge designed to predict relative genetic essentialities based on a novel dataset testing 98,000 shRNAs against 149 molecularly characterized cancer cell lines. We analyzed the results of over 3,000 submissions over a period of 4 months. We found that algorithms combining essentiality data across multiple genes demonstrated increased accuracy; gene expression was the most informative molecular data type; the identity of the gene being predicted was far more important than the modeling strategy; well-predicted genes and selected molecular features showed enrichment in functional categories; and frequently selected expression features correlated with survival in primary tumors. This study establishes benchmarks for gene essentiality prediction, presents a community resource for future comparison with this benchmark, and provides insights into factors influencing the ability to predict gene essentiality from functional genetic screens. This study also demonstrates the value of releasing pre-publication data publicly to engage the community in an open research collaboration.

Published
2017

4. Tissue-specific regulatory circuits reveal variable modular perturbations across complex diseases

Author

Marbach, Daniel, Lamparter, David, Quon, Gerald, Kellis, Manolis, Kutalik, Zoltán, and Bergmann, Sven

Subjects
Biological Sciences, Genetics, Aetiology, 2.1 Biological and endogenous factors, Generic health relevance, Good Health and Well Being, Brain Diseases, Cell Lineage, Computational Biology, Epigenomics, Gene Regulatory Networks, Genetic Variation, Genome, Human, Genome-Wide Association Study, Humans, Organ Specificity, Promoter Regions, Genetic, Protein Interaction Mapping, Transcription Factors, Technology, Medical and Health Sciences, Developmental Biology, Biological sciences
Abstract

Mapping perturbed molecular circuits that underlie complex diseases remains a great challenge. We developed a comprehensive resource of 394 cell type- and tissue-specific gene regulatory networks for human, each specifying the genome-wide connectivity among transcription factors, enhancers, promoters and genes. Integration with 37 genome-wide association studies (GWASs) showed that disease-associated genetic variants--including variants that do not reach genome-wide significance--often perturb regulatory modules that are highly specific to disease-relevant cell types or tissues. Our resource opens the door to systematic analysis of regulatory programs across hundreds of human cell types and tissues (http://regulatorycircuits.org).

Published
2016

6. Supplementary Table S1 from BET Inhibition Enhances TNF-Mediated Antitumor Immunity

Author

Wellinger, Lisa C., primary, Hogg, Simon J., primary, Newman, Dane M., primary, Friess, Thomas, primary, Geiss, Daniela, primary, Michie, Jessica, primary, Ramsbottom, Kelly M., primary, Bacac, Marina, primary, Fauti, Tanja, primary, Marbach, Daniel, primary, Jarassier, Laura, primary, Thienger, Phillip, primary, Paehler, Axel, primary, Cluse, Leonie A., primary, Kearney, Conor J., primary, Vervoort, Stephin J., primary, Trapani, Joseph A., primary, Oliaro, Jane, primary, Shortt, Jake, primary, Ruefli-Brasse, Astrid, primary, Rohle, Daniel, primary, and Johnstone, Ricky W., primary

Published
2023
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7. Video 1 from BET Inhibition Enhances TNF-Mediated Antitumor Immunity

Author

Wellinger, Lisa C., primary, Hogg, Simon J., primary, Newman, Dane M., primary, Friess, Thomas, primary, Geiss, Daniela, primary, Michie, Jessica, primary, Ramsbottom, Kelly M., primary, Bacac, Marina, primary, Fauti, Tanja, primary, Marbach, Daniel, primary, Jarassier, Laura, primary, Thienger, Phillip, primary, Paehler, Axel, primary, Cluse, Leonie A., primary, Kearney, Conor J., primary, Vervoort, Stephin J., primary, Trapani, Joseph A., primary, Oliaro, Jane, primary, Shortt, Jake, primary, Ruefli-Brasse, Astrid, primary, Rohle, Daniel, primary, and Johnstone, Ricky W., primary

Published
2023
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8. Data from BET Inhibition Enhances TNF-Mediated Antitumor Immunity

Author

Wellinger, Lisa C., primary, Hogg, Simon J., primary, Newman, Dane M., primary, Friess, Thomas, primary, Geiss, Daniela, primary, Michie, Jessica, primary, Ramsbottom, Kelly M., primary, Bacac, Marina, primary, Fauti, Tanja, primary, Marbach, Daniel, primary, Jarassier, Laura, primary, Thienger, Phillip, primary, Paehler, Axel, primary, Cluse, Leonie A., primary, Kearney, Conor J., primary, Vervoort, Stephin J., primary, Trapani, Joseph A., primary, Oliaro, Jane, primary, Shortt, Jake, primary, Ruefli-Brasse, Astrid, primary, Rohle, Daniel, primary, and Johnstone, Ricky W., primary

Published
2023
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9. Video 3 from BET Inhibition Enhances TNF-Mediated Antitumor Immunity

Author

Wellinger, Lisa C., primary, Hogg, Simon J., primary, Newman, Dane M., primary, Friess, Thomas, primary, Geiss, Daniela, primary, Michie, Jessica, primary, Ramsbottom, Kelly M., primary, Bacac, Marina, primary, Fauti, Tanja, primary, Marbach, Daniel, primary, Jarassier, Laura, primary, Thienger, Phillip, primary, Paehler, Axel, primary, Cluse, Leonie A., primary, Kearney, Conor J., primary, Vervoort, Stephin J., primary, Trapani, Joseph A., primary, Oliaro, Jane, primary, Shortt, Jake, primary, Ruefli-Brasse, Astrid, primary, Rohle, Daniel, primary, and Johnstone, Ricky W., primary

Published
2023
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10. Video 2 from BET Inhibition Enhances TNF-Mediated Antitumor Immunity

Author

Wellinger, Lisa C., primary, Hogg, Simon J., primary, Newman, Dane M., primary, Friess, Thomas, primary, Geiss, Daniela, primary, Michie, Jessica, primary, Ramsbottom, Kelly M., primary, Bacac, Marina, primary, Fauti, Tanja, primary, Marbach, Daniel, primary, Jarassier, Laura, primary, Thienger, Phillip, primary, Paehler, Axel, primary, Cluse, Leonie A., primary, Kearney, Conor J., primary, Vervoort, Stephin J., primary, Trapani, Joseph A., primary, Oliaro, Jane, primary, Shortt, Jake, primary, Ruefli-Brasse, Astrid, primary, Rohle, Daniel, primary, and Johnstone, Ricky W., primary

Published
2023
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11. Video 4 from BET Inhibition Enhances TNF-Mediated Antitumor Immunity

Author

Wellinger, Lisa C., primary, Hogg, Simon J., primary, Newman, Dane M., primary, Friess, Thomas, primary, Geiss, Daniela, primary, Michie, Jessica, primary, Ramsbottom, Kelly M., primary, Bacac, Marina, primary, Fauti, Tanja, primary, Marbach, Daniel, primary, Jarassier, Laura, primary, Thienger, Phillip, primary, Paehler, Axel, primary, Cluse, Leonie A., primary, Kearney, Conor J., primary, Vervoort, Stephin J., primary, Trapani, Joseph A., primary, Oliaro, Jane, primary, Shortt, Jake, primary, Ruefli-Brasse, Astrid, primary, Rohle, Daniel, primary, and Johnstone, Ricky W., primary

Published
2023
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12. Supplementary Video Legends and Figures from BET Inhibition Enhances TNF-Mediated Antitumor Immunity

Author

Wellinger, Lisa C., primary, Hogg, Simon J., primary, Newman, Dane M., primary, Friess, Thomas, primary, Geiss, Daniela, primary, Michie, Jessica, primary, Ramsbottom, Kelly M., primary, Bacac, Marina, primary, Fauti, Tanja, primary, Marbach, Daniel, primary, Jarassier, Laura, primary, Thienger, Phillip, primary, Paehler, Axel, primary, Cluse, Leonie A., primary, Kearney, Conor J., primary, Vervoort, Stephin J., primary, Trapani, Joseph A., primary, Oliaro, Jane, primary, Shortt, Jake, primary, Ruefli-Brasse, Astrid, primary, Rohle, Daniel, primary, and Johnstone, Ricky W., primary

Published
2023
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13. Deciphering molecular and cellular ex vivo responses to bispecific antibodies PD1-TIM3 and PD1-LAG3 in human tumors

Author

Natoli, Marina, primary, Hatje, Klas, additional, Gulati, Pratiksha, additional, Junker, Fabian, additional, Herzig, Petra, additional, Jiang, Zhiwen, additional, Davydov, Iakov I, additional, Germann, Markus, additional, Trüb, Marta, additional, Marbach, Daniel, additional, Zwick, Adrian, additional, Weber, Patrick, additional, Seeber, Stefan, additional, Wiese, Mark, additional, Lardinois, Didier, additional, Heinzelmann-Schwarz, Viola, additional, Rosenberg, Robert, additional, Tietze, Lothar, additional, Mertz, Kirsten D, additional, Umaña, Pablo, additional, Klein, Christian, additional, Codarri-Deak, Laura, additional, Kao, Henry, additional, and Zippelius, Alfred, additional

Published
2022
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14. Bio-mimetic Evolutionary Reverse Engineering of Genetic Regulatory Networks

Author

Marbach, Daniel, Mattiussi, Claudio, Floreano, Dario, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Rangan, C. Pandu, editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Marchiori, Elena, editor, Moore, Jason H., editor, and Rajapakse, Jagath C., editor

Published
2007
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15. DUSP4 protects BRAF- and NRAS-mutant melanoma from oncogene overdose through modulation of MITF

Author

Gutierrez-Prat, Nuria, primary, Zuberer, Hedwig L, additional, Mangano, Luca, additional, Karimaddini, Zahra, additional, Wolf, Luise, additional, Tyanova, Stefka, additional, Wellinger, Lisa C, additional, Marbach, Daniel, additional, Griesser, Vera, additional, Pettazzoni, Piergiorgio, additional, Bischoff, James R, additional, Rohle, Daniel, additional, Palladino, Chiara, additional, and Vivanco, Igor, additional

Published
2022
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16. BET Inhibition Enhances TNF-Mediated Antitumor Immunity

Author

Wellinger, Lisa C., primary, Hogg, Simon J., additional, Newman, Dane M., additional, Friess, Thomas, additional, Geiss, Daniela, additional, Michie, Jessica, additional, Ramsbottom, Kelly M., additional, Bacac, Marina, additional, Fauti, Tanja, additional, Marbach, Daniel, additional, Jarassier, Laura, additional, Thienger, Phillip, additional, Paehler, Axel, additional, Cluse, Leonie A., additional, Kearney, Conor J., additional, Vervoort, Stephin J., additional, Trapani, Joseph A., additional, Oliaro, Jane, additional, Shortt, Jake, additional, Ruefli-Brasse, Astrid, additional, Rohle, Daniel, additional, and Johnstone, Ricky W., additional

Published
2022
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17. Identification of Functional Elements and Regulatory Circuits by Drosophila modENCODE

Author

The modeENCODE Consortium, Roy, Sushmita, Ernst, Jason, Kharchenko, Peter V., Kheradpour, Pouya, Negre, Nicolas, Eaton, Matthew L., Landolin, Jane M., Bristow, Christopher A., Ma, Lijia, Lin, Michael F., Washietl, Stefan, Arshinoff, Bradley I., Ay, Ferhat, Meyer, Patrick E., Robine, Nicolas, Washington, Nicole L., Di Stefano, Luisa, Berezikov, Eugene, Brown, Christopher D., Candeias, Rogerio, Carlson, Joseph W., Carr, Adrian, Jungreis, Irwin, Marbach, Daniel, Sealfon, Rachel, Tolstorukov, Michael Y., Will, Sebastian, Alekseyenko, Artyom A., Artieri, Carlo, Booth, Benjamin W., Brooks, Angela N., Dai, Qi, Davis, Carrie A., Duff, Michael O., Feng, Xin, Gorchakov, Andrey A., Gu, Tingting, Henikoff, Jorja G., Kapranov, Philipp, Li, Renhua, MacAlpine, Heather K., Malone, John, Minoda, Aki, Nordman, Jared, Okamura, Katsutomo, Perry, Marc, Powell, Sara K., Riddle, Nicole C., Sakai, Akiko, Samsonova, Anastasia, Sandler, Jeremy E., Schwartz, Yuri B., Sher, Noa, Spokony, Rebecca, Sturgill, David, van Baren, Marijke, Wan, Kenneth H., Yang, Li, Yu, Charles, Feingold, Elise, Good, Peter, Guyer, Mark, Lowdon, Rebecca, Ahmad, Kami, Andrews, Justen, Berger, Bonnie, Brenner, Steven E., Brent, Michael R., Cherbas, Lucy, Elgin, Sarah C. R., Gingeras, Thomas R., Grossman, Robert, Hoskins, Roger A., Kaufman, Thomas C., Kent, William, Kuroda, Mitzi I., Orr-Weaver, Terry, Perrimon, Norbert, Pirrotta, Vincenzo, Posakony, James W., Ren, Bing, Russell, Steven, Cherbas, Peter, Graveley, Brenton R., Lewis, Suzanna, Micklem, Gos, Oliver, Brian, Park, Peter J., Celniker, Susan E., Henikoff, Steven, Karpen, Gary H., Lai, Eric C., MacAlpine, David M., Stein, Lincoln D., White, Kevin P., and Kellis, Manolis

Published
2010

19. BET Inhibition Enhances TNF Mediated Anti-Tumor Immunity

Author

Wellinger, Lisa C., primary, Hogg, Simon J., additional, Newman, Dane M., additional, Friess, Thomas, additional, Geiss, Daniela, additional, Michie, Jessica, additional, Ramsbottom, Kelly M., additional, Bacac, Marina, additional, Fauti, Tanja, additional, Marbach, Daniel, additional, Jarassier, Laura, additional, Thienger, Phillip, additional, Paehler, Axel, additional, Cluse, Leonie A., additional, Kearney, Conor J., additional, Vervoort, Stephin J., additional, Oliaro, Jane, additional, Shortt, Jake, additional, Ruefli-Brasse, Astrid, additional, Rohle, Daniel, additional, and Johnstone, Ricky W., additional

Published
2021
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23. Identification of Functional Elements and Regulatory Circuits by Drosophila modENCODE

Author

Roy, Sushmita, Ernst, Jason, Kharchenko, Peter V., Kheradpour, Pouya, Negre, Nicolas, Eaton, Matthew L., Landolin, Jane M., Bristow, Christopher A., Ma, Lijia, Lin, Michael F., Washietl, Stefan, Arshinoff, Bradley I., Ay, Ferhat, Meyer, Patrick E., Robine, Nicolas, Washington, Nicole L., Stefano, Luisa Di, Berezikov, Eugene, Brown, Christopher D., Candeias, Rogerio, Carlson, Joseph W., Carr, Adrian, Jungreis, Irwin, Marbach, Daniel, Sealfon, Rachel, Tolstorukov, Michael Y., Will, Sebastian, Alekseyenko, Artyom A., Artieri, Carlo, Booth, Benjamin W., Brooks, Angela N., Dai, Qi, Davis, Carrie A., Duff, Michael O., Feng, Xin, Gorchakov, Andrey A., Gu, Tingting, Henikoff, Jorja G., Kapranov, Philipp, Li, Renhua, MacAlpine, Heather K., Malone, John, Minoda, Aki, Nordman, Jared, Okamura, Katsutomo, Perry, Marc, Powell, Sara K., Riddle, Nicole C., Sakai, Akiko, Samsonova, Anastasia, Sandler, Jeremy E., Schwartz, Yuri B., Sher, Noa, Spokony, Rebecca, Sturgill, David, van Baren, Marijke, Wan, Kenneth H., Yang, Li, Yu, Charles, Feingold, Elise, Good, Peter, Guyer, Mark, Lowdon, Rebecca, Ahmad, Kami, Andrews, Justen, Berger, Bonnie, Brenner, Steven E., Brent, Michael R., Cherbas, Lucy, Elgin, Sarah C. R., Gingeras, Thomas R., Grossman, Robert, Hoskins, Roger A., Kaufman, Thomas C., Kent, William, Kuroda, Mitzi I., Orr-Weaver, Terry, Perrimon, Norbert, Pirrotta, Vincenzo, Posakony, James W., Ren, Bing, Russell, Steven, Cherbas, Peter, Graveley, Brenton R., Lewis, Suzanna, Micklem, Gos, Oliver, Brian, Park, Peter J., Celniker, Susan E., Henikoff, Steven, Karpen, Gary H., Lai, Eric C., MacAlpine, David M., Stein, Lincoln D., White, Kevin P., and Kellis, Manolis

Published
2010
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26. Assessment of network module identification across complex diseases

Author

Choobdar, Sarvenaz, Ahsen, Mehmet E, Crawford, Jake, Tomasoni, Mattia, Fang, Tao, Lamparter, David, Lin, Junyuan, Hescott, Benjamin, Hu, Xiaozhe, Mercer, Johnathan, Natoli, Ted, Narayan, Rajiv, von Mering, C, Subramanian, Aravind, Zhang, Jitao D, Stolovitzky, Gustavo, Kutalik, Zoltán, Lage, Kasper, Slonim, Donna K, Saez-Rodriguez, Julio, Cowen, Lenore J, Bergmann, Sven, Marbach, Daniel, University of Zurich, and Bergmann, Sven

Subjects
UFSP13-7 Evolution in Action: From Genomes to Ecosystems, 1307 Cell Biology, 1303 Biochemistry, 1305 Biotechnology, 1312 Molecular Biology, 570 Life sciences, biology, 10124 Institute of Molecular Life Sciences
Published
2019

27. The age of analog networks

Author

Mattiussi, Claudio, Marbach, Daniel, Durr, Peter, and Floreano, Dario

Subjects
Neural network, Neural networks -- Evaluation, Analog integrated circuits -- Evaluation
Abstract

Many systems of primary interest in biology and in engineering can be seen as analog networks. An analog network (figure 1) is composed of a collection of nodes representing devices […], A large class of systems of biological and technological relevance can be described as analog networks, that is, collections of dynamic devices interconnected by links of varying strength. Some examples of analog networks are genetic regulatory networks, metabolic networks, neural networks, analog electronic circuits, and control systems. Analog networks are typically complex systems that include nonlinear feedback loops and possess temporal dynamics at different time scales. Both the synthesis and reverse engineering of analog networks are recognized as knowledge-intensive activities, for which few systematic techniques exist. In this paper we will discuss the general relevance of the analog network concept and describe an evolutionary approach to the automatic synthesis and the reverse engineering of analog networks. The proposed approach is called analog genetic encoding (AGE) and realizes an implicit genetic encoding of analog networks. AGE permits the evolution of human-competitive solutions to real-world analog network design and identification problems. This is illustrated by some examples of application to the design of electronic circuits, control systems, learning neural architectures, and the reverse engineering of biological networks.

Published
2008

28. Open Community Challenge Reveals Molecular Network Modules with Key Roles in Diseases

Author

Choobdar, Sarvenaz, Ahsen, Mehmet E., Crawford, Jake, Tomasoni, Mattia, Fang, Tao, Lamparter, David, Lin, Junyuan, Hescott, Benjamin, Hu, Xiaozhe, Mercer, Johnathan, Natoli, Ted, Narayan, Rajiv, Subramanian, Aravind, Zhang, Jitao D., Stolovitzky, Gustavo, Kutalik, Zoltán, Lage, Kasper, Slonim, Donna K., Saez-Rodriguez, Julio, Cowen, Lenore J., Bergmann, Sven, Marbach, Daniel, Aicheler, Fabian, Amoroso, Nicola, Arenas, Alex, Azhagesan, Karthik, Baker, Aaron, Banf, Michael, Batzoglou, Serafim, Baudot, Anaïs, Bellotti, Roberto, Boroevich, Keith A., Brun, Christine, Cai, Stanley, Caldera, Michael, Calderone, Alberto, Cesareni, Gianni, Chen, Weiqi, Chichester, Christine, Cowen, Lenore, Cui, Hongzhu, Dao, Phuong, Domenico, Manlio De, Dhroso, Andi, Didier, Gilles, Divine, Mathew, Sol, Antonio del, Feng, Xuyang, Flores-Canales, Jose C., Fortunato, Santo, Gitter, Anthony, Gorska, Anna, Guan, Yuanfang, Guénoche, Alain, Gómez, Sergio, Hamza, Hatem, Hartmann, András, He, Shan, Heijs, Anton, Heinrich, Julian, Hu, Ying, Huang, Xiaoqing, Hughitt, V. Keith, Jeon, Minji, Jeub, Lucas, Johnson, Nathan, Joo, Keehyoung, Joung, InSuk, Jung, Sascha, Kalko, Susana G., Kamola, Piotr J., Kang, Jaewoo, Kaveelerdpotjana, Benjapun, Kim, Minjun, Kim, Yoo-Ah, Kohlbacher, Oliver, Korkin, Dmitry, Krzysztof, Kiryluk, Kunji, Khalid, Kutalik, Zoltàn, Lang-Brown, Sean, Le, Thuc Duy, Lee, Jooyoung, Lee, Sunwon, Lee, Juyong, Li, Dong, Li, Jiuyong, Liu, Lin, Loizou, Antonis, Luo, Zhenhua, Lysenko, Artem, Ma, Tianle, Mall, Raghvendra, Mattia, Tomasoni, Medvedovic, Mario, Menche, Jörg, Micarelli, Elisa, Monaco, Alfonso, Müller, Felix, Narykov, Oleksandr, Norman, Thea, Park, Sungjoon, Perfetto, Livia, Perrin, Dimitri, Pirrò, Stefano, Przytycka, Teresa M., Qian, Xiaoning, Raman, Karthik, Ramazzotti, Daniele, Ramsahai, Emilie, Ravindran, Balaraman, Rennert, Philip, Schärfe, Charlotta, Sharan, Roded, Shi, Ning, Shin, Wonho, Shu, Hai, Sinha, Himanshu, Spinelli, Lionel, Srinivasan, Suhas, Suver, Christine, Szklarczyk, Damian, Tangaro, Sabina, Thiagarajan, Suresh, Tichit, Laurent, Tiede, Thorsten, Tripathi, Beethika, Tsherniak, Aviad, Tsunoda, Tatsuhiko, Türei, Dénes, Ullah, Ehsan, Vahedi, Golnaz, Valdeolivas, Alberto, Vivek, Jayaswal, Mering, Christian von, Waagmeester, Andra, Wang, Bo, Wang, Yijie, Weir, Barbara A., White, Shana, Winkler, Sebastian, Xu, Ke, Xu, Taosheng, Yan, Chunhua, Yang, Liuqing, Yu, Kaixian, Yu, Xiangtian, Zaffaroni, Gaia, Zaslavskiy, Mikhail, Zeng, Tao, Zhang, Lu, Zhang, Weijia, Zhang, Lixia, Zhang, Xinyu, Zhang, Junpeng, Zhou, Xin, Zhou, Jiarui, Zhu, Hongtu, Zhu, Junjie, and Zuccon, Guido

Subjects
Identification methods, Molecular network, Computer science, Association (object-oriented programming), Key (cryptography), Open community, Genome-wide association study, Identification (biology), Computational biology, Disease
Abstract

Identification of modules in molecular networks is at the core of many current analysis methods in biomedical research. However, how well different approaches identify disease-relevant modules in different types of gene and protein networks remains poorly understood. We launched the “Disease Module Identification DREAM Challenge”, an open competition to comprehensively assess module identification methods across diverse protein-protein interaction, signaling, gene co-expression, homology, and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies (GWAS). Our critical assessment of 75 contributed module identification methods reveals novel top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets and correctly prioritize candidate disease genes. This community challenge establishes benchmarks, tools and guidelines for molecular network analysis to study human disease biology (https://synapse.org/modulechallenge).

Published
2018
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32. Open Community Challenge Reveals Molecular Network Modules with Key Roles in Diseases

Author

Choobdar, Sarvenaz, primary, Ahsen, Mehmet, additional, Crawford, Jake, additional, Tomasoni, Mattia, additional, Lamparter, David, additional, Lin, Junyuan, additional, Hescott, Benjamin, additional, Hu, Xiaozhe, additional, Mercer, Jonathan, additional, Natoli, Ted, additional, Narayan, Rajiv, additional, Consortium, DREAM Module Identification Challen, additional, Subramanian, Aravind, additional, Stolovitzky, Gustavo, additional, Kutalik, Zolttn, additional, Lage, Kasper, additional, Slonim, Donna, additional, Saez-Rodriguez, Julio, additional, Cowen, Lenore J., additional, Bergmann, Sven, additional, and Marbach, Daniel, additional

Published
2018
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34. Predictive Regulatory Models in of Transcriptional Networks

Author

Kahveci, Tamer, Marbach, Daniel, Roy, Sushmita, Ay, Ferhat, Meyer, Patrick E., Candeias, Rogerio, Bristow, Christopher A., Kellis, Manolis, Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Kellis, Manolis, Marbach, Daniel, Roy, Sushmita, Ay, Ferhat, Meyer, Patrick E., Candeias, Rogerio, and Bristow, Christopher A.

Abstract

Gaining insights on gene regulation from large-scale functional data sets is a grand challenge in systems biology. In this article, we develop and apply methods for transcriptional regulatory network inference from diverse functional genomics data sets and demonstrate their value for gene function and gene expression prediction. We formulate the network inference problem in a machine-learning framework and use both supervised and unsupervised methods to predict regulatory edges by integrating transcription factor (TF) binding, evolutionarily conserved sequence motifs, gene expression, and chromatin modification data sets as input features. Applying these methods to Drosophila melanogaster, we predict ∼300,000 regulatory edges in a network of ∼600 TFs and 12,000 target genes. We validate our predictions using known regulatory interactions, gene functional annotations, tissue-specific expression, protein–protein interactions, and three-dimensional maps of chromosome conformation. We use the inferred network to identify putative functions for hundreds of previously uncharacterized genes, including many in nervous system development, which are independently confirmed based on their tissue-specific expression patterns. Last, we use the regulatory network to predict target gene expression levels as a function of TF expression, and find significantly higher predictive power for integrative networks than for motif or ChIP-based networks. Our work reveals the complementarity between physical evidence of regulatory interactions (TF binding, motif conservation) and functional evidence (coordinated expression or chromatin patterns) and demonstrates the power of data integration for network inference and studies of gene regulation at the systems level., National Science Foundation (U.S.). (Computing Research Association for the CI Fellows Project) (Grant number 1136996)

Published
2012

39. Evolutionary reverse engineering of gene networks

Author

Marbach, Daniel and Floreano, Dario

Subjects
reverse engineering, ComputingMethodologies_PATTERNRECOGNITION, Analog Genetic Encoding (AGE), systems biology, evolutionary algorithms, gene regulatory networks, pathway inference, evolutionary robotics
Abstract

The expression of genes is controlled by regulatory networks, which perform fundamental information processing and control mechanisms in a cell. Unraveling and modelling these networks will be indispensable to gain a systems-level understanding of biological organisms and genetically related diseases. In this thesis, we present an evolutionary reverse engineering method, which allows to simultaneously infer both the wirings and nonlinear dynamical models of gene regulatory networks from gene expression data. The proposed method reconstructs gene networks by mimicking the natural evolutionary process that constructed them. This is achieved by modelling both the way in which gene networks are encoded in the biological genome, and the different types of mutations and recombinations that drive their evolution, using an artificial genome called Analog Genetic Encoding (AGE). Since AGE mimics the evolutionary forces and constraints that shape biological gene networks, the reconstruction is naturally guided towards biologically plausible solutions. Consequently, the search space is explored more efficiently, and the networks are recovered more reliably, than with alternative methods. We have confirmed the state-of-the-art performance of AGE both in vivo (on real gene networks) and in silico (on simulated networks). In particular, AGE achieved winning performance in the in vivo gene network inference inference challenge of the 2nd DREAM (Dialogue on Reverse Engineering Assessment and Methods) conference, which consisted in predicting the structure of a synthetic-biology gene network in Saccharomyces cerevisiae from time-series data. In vivo performance assessment of network-inference methods is problematic because it is in general not possible to systematically validate predictions, except for few well-characterized gene networks. Consequently, in silico benchmarks are essential to understand the performance of network-inference methods. We have developed tools to generate biologically plausible in silico gene networks, which allow realistic performance assessment of network-inference methods. In contrast to previous in silico benchmarks, we generate network structures by extracting modules from known gene networks of model organisms, instead of using random graphs. Furthermore, we simulate network dynamics using more realistic kinetic models, which include both mRNA and proteins. We have implemented this framework in an open-source Java tool called GeneNetWeaver (GNW). Using GNW we have generated benchmarks for community-wide challenges of the 3rd and 4th DREAM conference (the DREAM in silico network challenges). Here, we assess the performance of 29 network-inference methods, which have been applied independently by participating teams of the DREAM3 challenge. Performance profiling on individual network motifs reveals that current inference methods are affected, to various degrees, by three types of systematic prediction errors. We find that these errors are induced by inaccurate prior assumptions of prevalent gene-network models. The evolutionary reverse engineering approach, which would have ranked 3rd in this challenge, can be used with a wide range of nonlinear models. It could thus provide the necessary framework for the development of models that better approximate different types of gene regulation, thereby enabling ever more accurate reconstruction of gene networks.

Published
2009
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41. sbv IMPROVER Diagnostic Signature Challenge

Author

Norel, Raquel, primary, Bilal, Erhan, additional, Conrad-Chemineau, Nathalie, additional, Bonneau, Richard, additional, de la Fuente, Alberto, additional, Jurisica, Igor, additional, Marbach, Daniel, additional, Meyer, Pablo, additional, Rice, J Jeremy, additional, Tuller, Tamir, additional, and Stolovitzky, Gustavo, additional

Published
2013
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44. Verification of systems biology research in the age of collaborative competition

Author

Meyer, Pablo, primary, Alexopoulos, Leonidas G, additional, Bonk, Thomas, additional, Califano, Andrea, additional, Cho, Carolyn R, additional, de la Fuente, Alberto, additional, de Graaf, David, additional, Hartemink, Alexander J, additional, Hoeng, Julia, additional, Ivanov, Nikolai V, additional, Koeppl, Heinz, additional, Linding, Rune, additional, Marbach, Daniel, additional, Norel, Raquel, additional, Peitsch, Manuel C, additional, Rice, J Jeremy, additional, Royyuru, Ajay, additional, Schacherer, Frank, additional, Sprengel, Joerg, additional, Stolle, Katrin, additional, Vitkup, Dennis, additional, and Stolovitzky, Gustavo, additional

Published
2011
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50. Bio-mimetic Evolutionary Reverse Engineering of Genetic Regulatory Networks.

Author

Hutchison, David, Kanade, Takeo, Kittler, Josef, Kleinberg, Jon M., Mattern, Friedemann, Mitchell, John C., Naor, Moni, Nierstrasz, Oscar, Rangan, C. Pandu, Steffen, Bernhard, Sudan, Madhu, Terzopoulos, Demetri, Tygar, Doug, Vardi, Moshe Y., Weikum, Gerhard, Marchiori, Elena, Moore, Jason H., Rajapakse, Jagath C., Marbach, Daniel, and Mattiussi, Claudio

Abstract

The effective reverse engineering of biochemical networks is one of the great challenges of systems biology. The contribution of this paper is two-fold: 1) We introduce a new method for reverse engineering genetic regulatory networks from gene expression data; 2) We demonstrate how nonlinear gene networks can be inferred from steady-state data alone. The reverse engineering method is based on an evolutionary algorithm that employs a novel representation called Analog Genetic Encoding (AGE), which is inspired from the natural encoding of genetic regulatory networks. AGE can be used with biologically plausible, nonlinear gene models where analytical approaches or local gradient based optimisation methods often fail. Recently there has been increasing interest in reverse engineering linear gene networks from steady-state data. Here we demonstrate how more accurate nonlinear dynamical models can also be inferred from steady-state data alone. [ABSTRACT FROM AUTHOR]

Published
2007
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