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1. Chromosome Xq23 is associated with lower atherogenic lipid concentrations and favorable cardiometabolic indices

Author

Natarajan, P. (Pradeep), Pampana, A. (Akhil), Graham, S. E. (Sarah E.), Ruotsalainen, S. E. (Sanni E.), Perry, J. A. (James A.), de Vries, P. S. (Paul S.), Broome, J. G. (Jai G.), Pirruccello, J. P. (James P.), Honigbere, M. C. (Michael C.), Aragam, K. (Krishna), Wolford, B. (Brooke), Brody, J. A. (Jennifer A.), Antonacci-Fulton, L. (Lucinda), Arden, M. (Moscati), Aslibekyan, S. (Stella), Assimes, T. L. (Themistocles L.), Ballantyne, C. M. (Christie M.), Bielak, L. F. (Lawrence F.), Bisl, J. C. (Joshua C.), Cade, B. E. (Brian E.), Do, R. (Ron), Doddapaneni, H. (Harsha), Emery, L. S. (Leslie S.), Hung, Y.-J. (Yi-Jen), Irvin, M. R. (Marguerite R.), Khan, A. T. (Alyna T.), Lange, L. (Leslie), Lee, J. (Jiwon), Lemaitre, R. N. (Rozenn N.), Martin, L. W. (Lisa W.), Metcalf, G. (Ginger), Montasser, M. E. (May E.), Moon, J.-Y. (Jee-Young), Muzny, D. (Donna), Connell, J. R. (Jeffrey R. O.), Palmer, N. D. (Nicholette D.), Peralta, J. M. (Juan M.), Peyser, P. A. (Patricia A.), Stilp, A. M. (Adrienne M.), Tsai, M. (Michael), Wang, F. F. (Fei Fei), Weeks, D. E. (Daniel E.), Yanek, L. R. (Lisa R.), Wilson, J. G. (James G.), Abecasis, G. (Goncalo), Arnett, D. K. (Donna K.), Becker, L. C. (Lewis C.), Blangercy, J. (John), Boerwinkle, E. (Eric), Bowden, D. W. (Donald W.), Chang, Y.-C. (Yi-Cheng), Chen, Y. I. (Yii-Der, I), Choi, W. J. (Won Jung), Correa, A. (Adolfo), Curran, J. E. (Joanne E.), Daly, M. J. (Mark J.), DutcherE, S. K. (Susan K.), Ellinor, P. T. (Patrick T.), Fornage, M. (Myriam), Freedman, B. I. (Barry, I), Gabriel, S. (Stacey), Germer, S. (Soren), Gibbs, R. A. (Richard A.), He, J. (Jiang), Hveem, K. (Kristian), Jarvik, G. P. (Gail P.), Kaplan, R. C. (Robert C.), Kardia, S. L. (Sharon L. R.), Kennyn, E. (Eimear), Kim, R. W. (Ryan W.), Kooperberg, C. (Charles), Laurie, C. C. (Cathy C.), Lee, S. (Seonwook), Lloyd-Jones, D. M. (Don M.), Loos, R. J. (Ruth J. F.), Lubitz, S. A. (Steven A.), Mathias, R. A. (Rasika A.), Martinez, K. A. (Karine A. Viaud), McGarvey, S. T. (Stephen T.), Mitche, B. D. (Braxton D.), Nickerson, D. A. (Deborah A.), North, K. E. (Kari E.), Palotie, A. (Aarno), Park, C. J. (Cheol Joo), Psat, B. M. (Bruce M. Y.), Rao, D. C. (D. C.), Redline, S. (Susan), Reiner, A. P. (Alexander P.), Seo, D. (Daekwan), Seo, J.-S. (Jeong-Sun), Smith, A. V. (Albert, V), Tracy, R. P. (Russell P.), Kathiresan, S. (Sekar), Cupples, L. A. (L. Adrienne), Rotten, J. I. (Jerome, I), Morrison, A. C. (Alanna C.), Rich, S. S. (Stephen S.), Ripatti, S. (Samuli), Wilier, C. (Cristen), Peloso, G. M. (Gina M.), Vasan, R. S. (Ramachandran S.), Abe, N. (Namiko), Albert, C. (Christine), Almasy, L. (Laura), Alonso, A. (Alvaro), Ament, S. (Seth), Anderson, P. (Peter), Applebaum-Bowden, D. (Deborah), Arking, D. (Dan), Ashley-Koch, A. (Allison), Auer, P. (Paul), Avramopoulos, D. (Dimitrios), Barnard, J. (John), Barnes, K. (Kathleen), Barr, R. G. (R. Graham), Barron-Casella, E. (Emily), Beaty, T. (Terri), Becker, D. (Diane), Beer, R. (Rebecca), Begum, F. (Ferdouse), Beitelshees, A. (Amber), Benjamin, E. (Emelia), Bezerra, M. (Marcos), Bielak, L. (Larry), Blackwel, T. (Thomas), Bowler, R. (Russell), Broecke, U. (Ulrich), Bunting, K. (Karen), Burchard, E. (Esteban), Buth, E. (Erin), Cardwel, J. (Jonathan), Carty, C. (Cara), Casaburi, R. (Richard), Casella, J. (James), Chaffin, M. (Mark), Chang, C. (Christy), Chasman, D. (Daniel), Chavan, S. (Sameer), Chen, B.-J. (Bo-Juen), Chen, W.-M. (Wei-Min), Chol, M. (Michael), Choi, S. H. (Seung Hoan), Chuang, L.-M. (Lee-Ming), Chung, M. (Mina), Conomos, M. P. (Matthew P.), Cornell, E. (Elaine), Crapo, J. (James), Curtis, J. (Jeffrey), Custer, B. (Brian), Damcott, C. (Coleen), Darbar, D. (Dawood), Das, S. (Sayantan), David, S. (Sean), Davis, C. (Colleen), Daya, M. (Michelle), de Andrade, M. (Mariza), DeBaunuo, M. (Michael), Duan, Q. (Qing), Devine, R. D. (Ranjan Deka Dawn DeMeo Scott), Duggirala, Q. R. (Qing Ravi), Durda, J. P. (Jon Peter), Dutcher, S. (Susan), Eaton, C. (Charles), Ekunwe, L. (Lynette), Farber, C. (Charles), Farnaml, L. (Leanna), Fingerlin, T. (Tasha), Flickinger, M. (Matthew), Franceschini, N. (Nora), Fu, M. (Mao), Fullerton, S. M. (Stephanie M.), Fulton, L. (Lucinda), Gan, W. (Weiniu), Gao, Y. (Yan), Gass, M. (Margery), Ge, B. (Bruce), Geng, X. P. (Xiaoqi Priscilla), Gignoux, C. (Chris), Gladwin, M. (Mark), Glahn, D. (David), Gogarten, S. (Stephanie), Gong, D.-W. (Da-Wei), Goring, H. (Harald), Gu, C. C. (C. Charles), Guan, Y. (Yue), Guo, X. (Xiuqing), Haessler, J. (Jeff), Hall, M. (Michael), Harris, D. (Daniel), Hawle, N. Y. (Nicola Y.), Heavner, B. (Ben), Heckbert, S. (Susan), Hernandez, R. (Ryan), Herrington, D. (David), Hersh, C. (Craig), Hidalgo, B. (Bertha), Hixson, J. (James), Hokanson, J. (John), Hong, E. (Elliott), Hoth, K. (Karin), Hsiung, C. A. (Chao Agnes), Huston, H. (Haley), Hwu, C. M. (Chii Min), Jackson, R. (Rebecca), Jain, D. (Deepti), Jaquish, C. (Cashell), Jhun, M. A. (Min A.), Johnsen, J. (Jill), Johnson, A. (Andrew), Johnson, C. (Craig), Johnston, R. (Rich), Jones, K. (Kimberly), Kang, H. M. (Hyun Min), Kaufman, L. (Laura), Kell, S. Y. (Shannon Y.), Kessler, M. (Michael), Kinney, G. (Greg), Konkle, B. (Barbara), Kramer, H. (Holly), Krauter, S. (Stephanie), Lange, C. (Christoph), Lange, E. (Ethan), Laurie, C. (Cecelia), LeBoff, M. (Meryl), Lee, S. S. (Seunggeun Shawn), Lee, W.-J. (Wen-Jane), LeFaive, J. (Jonathon), Levine, D. (David), Levy, D. (Dan), Lewis, J. (Joshua), Li, Y. (Yun), Lin, H. (Honghuang), Lin, K. H. (Keng Han), Lin, X. (Xihong), Liu, S. (Simin), Liu, Y. (Yongmei), Lunetta, K. (Kathryn), Luo, J. (James), Mahaney, M. (Michael), Make, B. (Barry), Manichaikul, A. (Ani), Mansonl, J. (JoAnn), Margolin, L. (Lauren), Mathai, S. (Susan), McArdle, P. (Patrick), Mcdonald, M.-L. (Merry-Lynn), McFarland, S. (Sean), McHugh, C. (Caitlin), Mei, H. (Hao), Meyers, D. A. (Deborah A.), Mikulla, J. (Julie), Min, N. (Nancy), Minear, M. (Mollie), Minster, R. L. (Ryan L.), Musani, S. (Solomon), Mwasongwe, S. (Stanford), Mychaleckyj, J. C. (Josyf C.), Nadkarni, G. (Girish), Naik, R. (Rakhi), Naseri, T. (Take), Nekhai, S. (Sergei), Nelson, S. C. (Sarah C.), Nickerson, D. (Deborah), Connell, J. O. (Jeff O.), Connor, T. O. (Tim O.), Ochs-Balcom, H. (Heather), Pankow, J. (James), Papanicolaou, G. (George), Parkerl, M. (Margaret), Parsa, A. (Afshin), Penchey, S. (Sara), Perez, M. (Marco), Peters, U. (Ulrike), Phillips, L. S. (Lawrence S.), Phillips, S. (Sam), Pollin, T. (Toni), Post, W. (Wendy), Becker, J. P. (Julia Powers), Boorgula, M. P. (Meher Preethi), Preuss, M. (Michael), Prokopenko, D. (Dmitry), Qasba, P. (Pankaj), Qiao, D. (Dandi), Rafaels, N. (Nicholas), Raffield, L. (Laura), Rasmussen-Torvik, L. (Laura), Ratan, A. (Aakrosh), Reed, R. (Robert), Reganl, E. (Elizabeth), Reupena, M. S. (Muagututi Sefuiva), Rice, K. (Ken), Roden, D. (Dan), Roselli, C. (Carolina), Ruczinski, I. (Ingo), Russel, P. (Pamela), Ruuska, S. (Sarah), Ryan, K. (Kathleen), Sabino, E. C. (Ester Cerdeira), Sakornsakolpatl, P. (Phuwanat), Salzberg, S. (Steven), Sandow, K. (Kevin), Sankaran, V. G. (Vijay G.), Scheller, C. (Christopher), Schmidt, E. (Ellen), Schwander, K. (Karen), Schwartz, D. (David), Sciurba, F. (Frank), Seidman, C. (Christine), Seidman, J. (Jonathan), Sheehan, V. (Vivien), Shetty, A. (Amol), Shetty, A. (Aniket), Sheu, W. H. (Wayne Hui-Heng), Shoemaker, M. B. (M. Benjamin), Silver, B. (Brian), Silvermanl, E. (Edwin), Smith, J. (Jennifer), Smith, J. (Josh), Smith, N. (Nicholas), Smith, T. (Tanja), Smoller, S. (Sylvia), Snively, B. (Beverly), Soferlm, T. (Tamar), Streeten, E. (Elizabeth), Su, J. L. (Jessica Lasky), Sung, Y. J. (Yun Ju), Sylvia, J. (Jody), Sztalryd, C. (Carole), Taliun, D. (Daniel), Tang, H. (Hua), Taub, M. (Margaret), Taylor, K. D. (Kent D.), Taylor, S. (Simeon), Telen, M. (Marilyn), Thornton, T. A. (Timothy A.), Tinker, L. (Lesley), Tirschwel, D. (David), Tiwari, H. (Hemant), Vaidya, D. (Dhananjay), VandeHaar, P. (Peter), Vrieze, S. (Scott), Walker, T. (Tarik), Wallace, R. (Robert), Waits, A. (Avram), Wan, E. (Emily), Wang, H. (Heming), Watson, K. (Karol), Weir, B. (Bruce), Weiss, S. (Scott), Weng, L.-C. (Lu-Chen), Williams, K. (Kayleen), Williams, L. K. (L. Keoki), Wilson, C. (Carla), Wong, Q. (Quenna), Xu, H. (Huichun), Yang, I. (Ivana), Yang, R. (Rongze), Zaghlou, N. (Norann), Zekavat, M. (Maryam), Zhang, Y. (Yingze), Zhao, S. X. (Snow Xueyan), Zhao, W. (Wei), Zni, D. (Degui), Zhou, X. (Xiang), Zhu, X. (Xiaofeng), Zody, M. (Michael), Zoellner, S. (Sebastian), Daly, M. (Mark), Jacob, H. (Howard), Matakidou, A. (Athena), Runz, H. (Heiko), John, S. (Sally), Plenge, R. (Robert), McCarthy, M. (Mark), Hunkapiller, J. (Julie), Ehm, M. (Meg), Waterworth, D. (Dawn), Fox, C. (Caroline), Malarstig, A. (Anders), Klinger, K. (Kathy), Call, K. (Kathy), Mkel, T. (Tomi), Kaprio, J. (Jaakko), Virolainen, P. (Petri), Pulkki, K. (Kari), Kilpi, T. (Terhi), Perola, M. (Markus), Partanen, J. (Jukka), Pitkranta, A. (Anne), Kaarteenaho, R. (Riitta), Vainio, S. (Seppo), Savinainen, K. (Kimmo), Kosma, V.-M. (Veli-Matti), Kujala, U. (Urho), Tuovila, O. (Outi), Hendolin, M. (Minna), Pakkanen, R. (Raimo), Waring, J. (Jeff), Riley-Gillis, B. (Bridget), Liu, J. (Jimmy), Biswas, S. (Shameek), Diogo, D. (Dorothee), Marshall, C. (Catherine), Hu, X. (Xinli), Gossel, M. (Matthias), Schleutker, J. (Johanna), Arvas, M. (Mikko), Hinttala, R. (Reetta), Kettunen, J. (Johannes), Laaksonen, R. (Reijo), Mannermaa, A. (Arto), Paloneva, J. (Juha), Soininen, H. (Hilkka), Julkunen, V. (Valtteri), Remes, A. (Anne), Klviinen, R. (Reetta), Hiltunen, M. (Mikko), Peltola, J. (Jukka), Tienari, P. (Pentti), Rinne, J. (Juha), Ziemann, A. (Adam), Waring, J. (Jeffrey), Esmaeeli, S. (Sahar), Smaoui, N. (Nizar), Lehtonen, A. (Anne), Eaton, S. (Susan), Landenper, S. (Sanni), Michon, J. (John), Kerchner, G. (Geoff), Bowers, N. (Natalie), Teng, E. (Edmond), Eicher, J. (John), Mehta, V. (Vinay), Gormle, P. Y. (Padhraig Y.), Linden, K. (Kari), Whelan, C. (Christopher), Xu, F. (Fanli), Pulford, D. (David), Frkkil, M. (Martti), Pikkarainen, S. (Sampsa), Jussila, A. (Airi), Blomster, T. (Timo), Kiviniemi, M. (Mikko), Voutilainen, M. (Markku), Georgantas, B. (Bob), Heap, G. (Graham), Rahimov, F. (Fedik), Usiskin, K. (Keith), Maranville, J. (Joseph), Lu, T. (Tim), Oh, D. (Danny), Kalpala, K. (Kirsi), Miller, M. (Melissa), McCarthy, L. (Linda), Eklund, K. (Kari), Palomki, A. (Antti), Isomki, P. (Pia), Piri, L. (Laura), Kaipiainen-Seppnen, O. (Oili), Lertratanaku, A. (Apinya), Bing, D. C. (David Close Marla Hochfeld Nan), Gordillo, J. E. (Jorge Esparza), Mars, N. (Nina), Laitinen, T. (Tarja), Pelkonen, M. (Margit), Kauppi, P. (Paula), Kankaanranta, H. (Hannu), Harju, T. (Terttu), Greenberg, S. (Steven), Chen, H. (Hubert), Betts, J. (Jo), Ghosh, S. (Soumitra), Salomaa, V. (Veikko), Niiranen, T. (Teemu), Juonala, M. (Markus), Metsrinne, K. (Kaj), Khnen, M. (Mika), Junttila, J. (Juhani), Laakso, M. (Markku), Pihlajamki, J. (Jussi), Sinisalo, J. (Juha), Taskinen, M.-R. (Marja-Riitta), Tuomi, T. (Tiinamaija), Laukkanen, J. (Jari), Challis, B. (Ben), Peterson, A. (Andrew), Chu, A. (Audrey), Parkkinen, J. (Jaakko), Muslin, A. (Anthony), Joensuu, H. (Heikki), Meretoja, T. (Tuomo), Aaltonen, L. (Lauri), Auranen, A. (Annika), Karihtala, P. (Peeter), Kauppila, S. (Saila), Auvinen, P. (Pivi), Elenius, K. (Klaus), Popovic, R. (Relja), Schutzman, J. (Jennifer), Loboda, A. (Andrey), Chhibber, A. (Aparna), Lehtonen, H. (Heli), McDonough, S. (Stefan), Crohns, M. (Marika), Kulkarni, D. (Diptee), Kaarniranta, K. (Kai), Turunen, J. (Joni), Ollila, T. (Terhi), Seitsonen, S. (Sanna), Uusitalo, H. (Hannu), Aaltonen, V. (Vesa), Uusitalo-Jrvinen, H. (Hannele), Luodonp, M. (Marja), Hautala, N. (Nina), Strauss, E. (Erich), Chen, H. (Hao), Podgornaia, A. (Anna), Hoffman, J. (Joshua), Tasanen, K. (Kaisa), Huilaja, L. (Laura), Hannula-Jouppi, K. (Katariina), Salmi, T. (Teea), Peltonen, S. (Sirkku), Koulu, L. (Leena), Harvima, I. (Ilkka), Wu, Y. (Ying), Choy, D. (David), Jalanko, A. (Anu), Kajanne, R. (Risto), Lyhs, U. (Ulrike), Kaunisto, M. (Mari), Davis, J. W. (Justin Wade), Quarless, D. (Danjuma), Petrovski, S. (Slav), Chen, C.-Y. (Chia-Yen), Bronson, P. (Paola), Yang, R. (Robert), Chang, D. (Diana), Bhangale, T. (Tushar), Holzinger, E. (Emily), Wang, X. (Xulong), Chen, X. (Xing), Auro, K. (Kirsi), Wang, C. (Clarence), Xu, E. (Ethan), Auge, F. (Franck), Chatelain, C. (Clement), Kurki, M. (Mitja), Karjalainen, J. (Juha), Havulinna, A. (Aki), Palin, K. (Kimmo), Palta, P. (Priit), Parolo, P. D. (Pietro Della Briotta), Zhou, W. (Wei), Lemmel, S. (Susanna), Rivas, M. (Manuel), Harju, J. (Jarmo), Lehisto, A. (Arto), Ganna, A. (Andrea), Llorens, V. (Vincent), Karlsson, A. (Antti), Kristiansson, K. (Kati), Hyvrinen, K. (Kati), Ritari, J. (Jarmo), Wahlfors, T. (Tiina), Koskinen, M. (Miika), Pylkäs, K. (Katri), Kalaoja, M. (Marita), Karjalainen, M. (Minna), Mantere, T. (Tuomo), Kangasniemi, E. (Eeva), Heikkinen, S. (Sami), Laakkonen, E. (Eija), Kononen, J. (Juha), Loukola, A. (Anu), Laiho, P. (Pivi), Sistonen, T. (Tuuli), Kaiharju, E. (Essi), Laukkanen, M. (Markku), Jrvensivu, E. (Elina), Lhteenmki, S. (Sini), Mnnikk, L. (Lotta), Wong, R. (Regis), Mattsson, H. (Hannele), Hiekkalinna, T. (Tero), Jimnez, M. G. (Manuel Gonzlez), Donner, K. (Kati), Prn, K. (KaIle), Nunez-Fontarnau, J. (Javier), Kilpelinen, E. (Elina), Sipi, T. P. (Timo P.), Brein, G. (Georg), Dada, A. (Alexander), Awaisa, G. (Ghazal), Shcherban, A. (Anastasia), Sipil, T. (Tuomas), Laivuori, H. (Hannele), Kiiskinen, T. (Tuomo), Siirtola, H. (Harri), Tabuenca, J. G. (Javier Gracia), Kallio, L. (Lila), Soini, S. (Sirpa), Pitknen, K. (Kimmo), and Kuopio, T. (Teijo)

Subjects
Cardiovascular genetics, Genome-wide association studies
Abstract

Autosomal genetic analyses of blood lipids have yielded key insights for coronary heart disease (CHD). However, X chromosome genetic variation is understudied for blood lipids in large sample sizes. We now analyze genetic and blood lipid data in a high-coverage whole X chromosome sequencing study of 65,322 multi-ancestry participants and perform replication among 456,893 European participants. Common alleles on chromosome Xq23 are strongly associated with reduced total cholesterol, LDL cholesterol, and triglycerides (min P = 8.5 × 10−72), with similar effects for males and females. Chromosome Xq23 lipid-lowering alleles are associated with reduced odds for CHD among 42,545 cases and 591,247 controls (P = 1.7 × 10−4), and reduced odds for diabetes mellitus type 2 among 54,095 cases and 573,885 controls (P = 1.4 × 10−5). Although we observe an association with increased BMI, waist-to-hip ratio adjusted for BMI is reduced, bioimpedance analyses indicate increased gluteofemoral fat, and abdominal MRI analyses indicate reduced visceral adiposity. Co-localization analyses strongly correlate increased CHRDL1 gene expression, particularly in adipose tissue, with reduced concentrations of blood lipids.

Published
2021

2. SBML Level 3: an extensible format for the exchange and reuse of biological models

Author

Keating, S, Waltemath, D, König, M, Zhang, F, Dräger, A, Chaouiya, C, Bergmann, F, Finney, A, Gillespie, C, Helikar, T, Hoops, S, Malik-Sheriff, R, Moodie, S, Moraru, I, Myers, C, Naldi, A, Olivier, B, Sahle, S, Schaff, J, Smith, L, Swat, M, Thieffry, D, Watanabe, L, Wilkinson, D, Blinov, M, Begley, K, Faeder, J, Gómez, H, Hamm, T, Inagaki, Y, Liebermeister, W, Lister, A, Lucio, D, Mjolsness, E, Proctor, C, Raman, K, Rodriguez, N, Shaffer, C, Shapiro, B, Stelling, J, Swainston, N, Tanimura, N, Wagner, J, Meier-Schellersheim, M, Sauro, H, Palsson, B, Bolouri, H, Kitano, H, Funahashi, A, Hermjakob, H, Doyle, J, Hucka, M, Adams, R, Allen, N, Angermann, B, Antoniotti, M, Bader, G, Červený, J, Courtot, M, Cox, C, Dalle Pezze, P, Demir, E, Denney, W, Dharuri, H, Dorier, J, Drasdo, D, Ebrahim, A, Eichner, J, Elf, J, Endler, L, Evelo, C, Flamm, C, Fleming, R, Fröhlich, M, Glont, M, Gonçalves, E, Golebiewski, M, Grabski, H, Gutteridge, A, Hachmeister, D, Harris, L, Heavner, B, Henkel, R, Hlavacek, W, Hu, B, Hyduke, D, Jong, H, Juty, N, Karp, P, Karr, J, Kell, D, Keller, R, Kiselev, I, Klamt, S, Klipp, E, Knüpfer, C, Kolpakov, F, Krause, F, Kutmon, M, Laibe, C, Lawless, C, Li, L, Loew, L, Machne, R, Matsuoka, Y, Mendes, P, Mi, H, Mittag, F, Monteiro, P, Natarajan, K, Nielsen, P, Nguyen, T, Palmisano, A, Jean-Baptiste, P, Pfau, T, Phair, R, Radivoyevitch, T, Rohwer, J, Ruebenacker, O, Saez-Rodriguez, J, Scharm, M, Schmidt, H, Schreiber, F, Schubert, M, Schulte, R, Sealfon, S, Smallbone, K, Soliman, S, Stefan, M, Sullivan, D, Takahashi, K, Teusink, B, Tolnay, D, Vazirabad, I, Kamp, A, Wittig, U, Wrzodek, C, Wrzodek, F, Xenarios, I, Zhukova, A, Zucker, J, Keating, SM, Bergmann, FT, Gillespie, CS, Malik-Sheriff, RS, Moodie, SL, Moraru, II, Myers, CJ, Olivier, BG, Schaff, JC, Smith, LP, Swat, MJ, Wilkinson, DJ, Blinov, ML, Faeder, JR, Gómez, HF, Hamm, TM, Lister, AL, Proctor, CJ, Shaffer, CA, Shapiro, BE, Sauro, HM, Doyle, JC, Adams, RR, Allen, NA, Angermann, BR, Bader, GD, Cox, CD, Denney, WS, Evelo, CT, Fleming, RM, Harris, LA, Heavner, BD, Hlavacek, WS, Hyduke, DR, Karp, PD, Karr, JR, Kell, DB, Loew, LM, Monteiro, PT, Natarajan, KN, Nielsen, PM, Phair, RD, Rohwer, JM, Ruebenacker, OA, Sealfon, SC, Stefan, MI, Sullivan, DP, Keating, S, Waltemath, D, König, M, Zhang, F, Dräger, A, Chaouiya, C, Bergmann, F, Finney, A, Gillespie, C, Helikar, T, Hoops, S, Malik-Sheriff, R, Moodie, S, Moraru, I, Myers, C, Naldi, A, Olivier, B, Sahle, S, Schaff, J, Smith, L, Swat, M, Thieffry, D, Watanabe, L, Wilkinson, D, Blinov, M, Begley, K, Faeder, J, Gómez, H, Hamm, T, Inagaki, Y, Liebermeister, W, Lister, A, Lucio, D, Mjolsness, E, Proctor, C, Raman, K, Rodriguez, N, Shaffer, C, Shapiro, B, Stelling, J, Swainston, N, Tanimura, N, Wagner, J, Meier-Schellersheim, M, Sauro, H, Palsson, B, Bolouri, H, Kitano, H, Funahashi, A, Hermjakob, H, Doyle, J, Hucka, M, Adams, R, Allen, N, Angermann, B, Antoniotti, M, Bader, G, Červený, J, Courtot, M, Cox, C, Dalle Pezze, P, Demir, E, Denney, W, Dharuri, H, Dorier, J, Drasdo, D, Ebrahim, A, Eichner, J, Elf, J, Endler, L, Evelo, C, Flamm, C, Fleming, R, Fröhlich, M, Glont, M, Gonçalves, E, Golebiewski, M, Grabski, H, Gutteridge, A, Hachmeister, D, Harris, L, Heavner, B, Henkel, R, Hlavacek, W, Hu, B, Hyduke, D, Jong, H, Juty, N, Karp, P, Karr, J, Kell, D, Keller, R, Kiselev, I, Klamt, S, Klipp, E, Knüpfer, C, Kolpakov, F, Krause, F, Kutmon, M, Laibe, C, Lawless, C, Li, L, Loew, L, Machne, R, Matsuoka, Y, Mendes, P, Mi, H, Mittag, F, Monteiro, P, Natarajan, K, Nielsen, P, Nguyen, T, Palmisano, A, Jean-Baptiste, P, Pfau, T, Phair, R, Radivoyevitch, T, Rohwer, J, Ruebenacker, O, Saez-Rodriguez, J, Scharm, M, Schmidt, H, Schreiber, F, Schubert, M, Schulte, R, Sealfon, S, Smallbone, K, Soliman, S, Stefan, M, Sullivan, D, Takahashi, K, Teusink, B, Tolnay, D, Vazirabad, I, Kamp, A, Wittig, U, Wrzodek, C, Wrzodek, F, Xenarios, I, Zhukova, A, Zucker, J, Keating, SM, Bergmann, FT, Gillespie, CS, Malik-Sheriff, RS, Moodie, SL, Moraru, II, Myers, CJ, Olivier, BG, Schaff, JC, Smith, LP, Swat, MJ, Wilkinson, DJ, Blinov, ML, Faeder, JR, Gómez, HF, Hamm, TM, Lister, AL, Proctor, CJ, Shaffer, CA, Shapiro, BE, Sauro, HM, Doyle, JC, Adams, RR, Allen, NA, Angermann, BR, Bader, GD, Cox, CD, Denney, WS, Evelo, CT, Fleming, RM, Harris, LA, Heavner, BD, Hlavacek, WS, Hyduke, DR, Karp, PD, Karr, JR, Kell, DB, Loew, LM, Monteiro, PT, Natarajan, KN, Nielsen, PM, Phair, RD, Rohwer, JM, Ruebenacker, OA, Sealfon, SC, Stefan, MI, and Sullivan, DP

Abstract

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution.

Published
2020

5. Cutting works to size: defending artists' work - issues of copyright and unfair competition when editing.

Author

Heavner, B. Brett and Barnhart, Christianna Lewis

Subjects
Unfair competition (Commerce) -- Laws, regulations and rules, Copyright infringement -- Remedies, Editing -- Laws, regulations and rules, Gilliam v. American Broadcast Co. (538 F.2d 14 (2d Cir. 1976)), Dastar Corp. v. Twentieth Century Fox Film Corp. (539 U.S. 23 (2003)), Government regulation, Lanham Act (15 U.S.C. 43(a))
Published
2008

8. Gray v. Novell, Inc., 2011 WL 69373 (11th Cir. Jan. 7, 2011)

Author

Heavner, B.

Subjects
Novell Inc. -- Cases, GrayTech Software Inc. -- Intellectual property -- Cases, Computer software industry -- Intellectual property -- Cases, Trademark infringement -- Cases, Fraud -- Cases, Company legal issue, Business, international
Abstract

Incontestable Trademark Newsletter - January 2011 ABSTRACT X/Open opposed Gray's application to register the mark iNUX based on X/Open's prior rights to the mark UNIX. In defending the opposition, Gray [...]

Published
2011

9. Visa Int'l Serv. Ass'n v. JSL Corp., 95 USPQ2d 1571 (9th Cir. June 28, 2010)

Author

Heavner, B.

Subjects
Visa Inc. -- Cases, Computer software industry -- Cases, Credit and debit card industry -- Cases, Trademark infringement -- Cases, Company legal issue, Business, international
Abstract

Incontestable Trademark Newsletter - July/August 2010 ABSTRACT The Ninth Circuit Court of Appeals affirmed the district court's grant of summary judgment to plaintiff on its claim that defendant's eVISA mark [...]

Published
2010

11. Judging a Pill By its Cover.

Author

Heavner, B. Brett

Subjects
PHARMACEUTICAL industry, PRODUCT usage, MARKETING, CONSUMER behavior, PHARMACEUTICAL services, HEALTH care industry
Abstract

The article reports on the effective marketing tool used in pharmaceutical industry. It mentions that pharmaceutical trade dress is effective marketing tool for promoting drugs. It also mentions that the drug manufacturing companies have been accorded rights to their pharmaceutical trade dress that help the consumer to identify the brand of the pharmaceutical product.

Published
2012

12. SBML Level 3: an extensible format for the exchange and reuse of biological models

Author

Edda Klipp, Marco Antoniotti, Frank Bergmann, James C. Schaff, Peter D. Karp, Daniel Lucio, Kedar Nath Natarajan, Thomas M. Hamm, Leandro Watanabe, Henning Hermjakob, David Tolnay, John Wagner, Joerg Stelling, Alida Palmisano, Falk Schreiber, Yukiko Matsuoka, Harold F. Gómez, Huaiyu Mi, Carole J. Proctor, Ulrike Wittig, Neil Swainston, Jan Červený, Denis Thieffry, Piero Dalle Pezze, Julio Saez-Rodriguez, Maciej J. Swat, Bin Hu, Martina Kutmon, Thomas Pfau, Bas Teusink, Sarah M. Keating, Fedor A. Kolpakov, Andreas Dräger, Pedro Mendes, Martin Scharm, Emek Demir, Ioannis Xenarios, Christoph Flamm, Axel von Kamp, Darren J. Wilkinson, Nick Juty, Fengkai Zhang, Leonard A. Harris, Michael Schubert, Dagmar Waltemath, Lucian P. Smith, Steffen Klamt, Herbert M. Sauro, Ali Ebrahim, Wolfram Liebermeister, Christian Knüpfer, Nicolas Rodriguez, Tramy Nguyen, Naoki Tanimura, Christopher Cox, Stuart C. Sealfon, Nicholas Alexander Allen, Clemens Wrzodek, Bastian R. Angermann, Martin Meier-Schellersheim, Anna Zhukova, Jean-Baptiste Pettit, Hovakim Grabski, Devin P. Sullivan, Claudine Chaouiya, Michael L. Blinov, John Doyle, Ilya Kiselev, Roman Schulte, Alex Gutteridge, Mélanie Courtot, Eric Mjolsness, Finja Wrzodek, Rahuman S Malik-Sheriff, Ronan M. T. Fleming, Bruce E. Shapiro, Kimberly Begley, Leslie M. Loew, Colin S. Gillespie, Ibrahim Vazirabad, Michael Hucka, Akira Funahashi, Bernhard O. Palsson, Hamid Bolouri, Tomáš Helikar, Camille Laibe, William S. Denney, Chris T. Evelo, Florian Mittag, William S. Hlavacek, Ron Henkel, Harish Dharuri, Julien Dorier, Karthik Raman, Martina Fröhlich, Conor Lawless, Rainer Machné, Falko Krause, Damon Hachmeister, Matthias König, Clifford A. Shaffer, Benjamin D. Heavner, Douglas B. Kell, Jonathan R. Karr, Mihai Glont, Lukas Endler, Melanie I. Stefan, Robert Phair, Lu Li, Henning Schmidt, Dirk Drasdo, Johan Elf, Allyson L. Lister, Hiroaki Kitano, Richard R. Adams, Oliver A. Ruebenacker, Roland Keller, Sven Sahle, Ion I. Moraru, Gary D. Bader, Poul M. F. Nielsen, Johann M. Rohwer, Johannes Eichner, Daniel R. Hyduke, James R. Faeder, Stefan Hoops, Emanuel Gonçalves, Yuichiro Inagaki, Aurélien Naldi, Koichi Takahashi, Sylvain Soliman, Brett G. Olivier, Kieran Smallbone, Stuart L. Moodie, Pedro T. Monteiro, Chris J. Myers, Martin Golebiewski, Tomas Radivoyevitch, Jeremy Zucker, Hidde de Jong, Andrew Finney, Keating, S, Waltemath, D, König, M, Zhang, F, Dräger, A, Chaouiya, C, Bergmann, F, Finney, A, Gillespie, C, Helikar, T, Hoops, S, Malik-Sheriff, R, Moodie, S, Moraru, I, Myers, C, Naldi, A, Olivier, B, Sahle, S, Schaff, J, Smith, L, Swat, M, Thieffry, D, Watanabe, L, Wilkinson, D, Blinov, M, Begley, K, Faeder, J, Gómez, H, Hamm, T, Inagaki, Y, Liebermeister, W, Lister, A, Lucio, D, Mjolsness, E, Proctor, C, Raman, K, Rodriguez, N, Shaffer, C, Shapiro, B, Stelling, J, Swainston, N, Tanimura, N, Wagner, J, Meier-Schellersheim, M, Sauro, H, Palsson, B, Bolouri, H, Kitano, H, Funahashi, A, Hermjakob, H, Doyle, J, Hucka, M, Adams, R, Allen, N, Angermann, B, Antoniotti, M, Bader, G, Červený, J, Courtot, M, Cox, C, Dalle Pezze, P, Demir, E, Denney, W, Dharuri, H, Dorier, J, Drasdo, D, Ebrahim, A, Eichner, J, Elf, J, Endler, L, Evelo, C, Flamm, C, Fleming, R, Fröhlich, M, Glont, M, Gonçalves, E, Golebiewski, M, Grabski, H, Gutteridge, A, Hachmeister, D, Harris, L, Heavner, B, Henkel, R, Hlavacek, W, Hu, B, Hyduke, D, Jong, H, Juty, N, Karp, P, Karr, J, Kell, D, Keller, R, Kiselev, I, Klamt, S, Klipp, E, Knüpfer, C, Kolpakov, F, Krause, F, Kutmon, M, Laibe, C, Lawless, C, Li, L, Loew, L, Machne, R, Matsuoka, Y, Mendes, P, Mi, H, Mittag, F, Monteiro, P, Natarajan, K, Nielsen, P, Nguyen, T, Palmisano, A, Jean-Baptiste, P, Pfau, T, Phair, R, Radivoyevitch, T, Rohwer, J, Ruebenacker, O, Saez-Rodriguez, J, Scharm, M, Schmidt, H, Schreiber, F, Schubert, M, Schulte, R, Sealfon, S, Smallbone, K, Soliman, S, Stefan, M, Sullivan, D, Takahashi, K, Teusink, B, Tolnay, D, Vazirabad, I, Kamp, A, Wittig, U, Wrzodek, C, Wrzodek, F, Xenarios, I, Zhukova, A, Zucker, J, European Bioinformatics Institute [Hinxton] (EMBL-EBI), EMBL Heidelberg, Heidelberg University Hospital [Heidelberg], Swiss Institute of Bioinformatics [Lausanne] (SIB), Université de Lausanne = University of Lausanne (UNIL), European Molecular Biology Laboratory (EMBL), University of Connecticut (UCONN), National Institutes of Health [Bethesda] (NIH), Chercheur indépendant, Amazon Web Services [Seattle] (AWS), Università degli Studi di Milano-Bicocca = University of Milano-Bicocca (UNIMIB), University of Toronto, Masaryk University [Brno] (MUNI), Terry Fox Laboratory, BC Cancer Agency (BCCRC)-British Columbia Cancer Agency Research Centre, The University of Tennessee [Knoxville], The Babraham Institute [Cambridge, UK], Oregon Health and Science University [Portland] (OHSU), Human Predictions LLC, Illumina, Swiss-Prot Group, Swiss Institute of Bioinformatics [Genève] (SIB), Modelling and Analysis for Medical and Biological Applications (MAMBA), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jacques-Louis Lions (LJLL (UMR_7598)), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), University of California [San Diego] (UC San Diego), University of California (UC), Center for Bioinformatics (ZBIT), Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen, Uppsala University, Institut für Populationsgenetik [Vienna], Veterinärmedizinische Universität Wien, Maastricht University [Maastricht], Alpen-Adria-Universität Klagenfurt [Klagenfurt, Austria], Medizinische Universität Wien = Medical University of Vienna, German Cancer Research Center - Deutsches Krebsforschungszentrum [Heidelberg] (DKFZ), Heidelberg Institute for Theoretical Studies (HITS ), Russian-Armenian University (RAU), GlaxoSmithKline [Stevenage, UK] (GSK), GlaxoSmithKline [Headquarters, London, UK] (GSK), Microsoft Technology Licensing (MTL), Microsoft Corporation [Redmond, Wash.], Vanderbilt University School of Medicine [Nashville], University of Washington [Seattle], University of Rostock, Los Alamos National Laboratory (LANL), Lorentz Institute, Universiteit Leiden, Tegmine Therapeutics, Modeling, simulation, measurement, and control of bacterial regulatory networks (IBIS), Laboratoire Adaptation et pathogénie des micro-organismes [Grenoble] (LAPM), Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Jean Roget, SRI International [Menlo Park] (SRI), Icahn School of Medicine at Mount Sinai [New York] (MSSM), University of Liverpool, Universitätsklinikum Tübingen - University Hospital of Tübingen, Institute of Information and Computational Technologies (IICT), Max Planck Institute for Dynamics of Complex Technical Systems, Max-Planck-Gesellschaft, Max-Planck-Institut für Molekulare Genetik (MPIMG), Friedrich-Schiller-Universität = Friedrich Schiller University Jena [Jena, Germany], Humboldt University Of Berlin, Newcastle University [Newcastle], École polytechnique (X), Heinrich Heine Universität Düsseldorf = Heinrich Heine University [Düsseldorf], The Systems Biology Institute [Tokyo] (SBI), Centro de Quimica Estrutural (CQE), Instituto Superior Técnico, Universidade Técnica de Lisboa (IST), University of Southern California (USC), Instituto Gulbenkian de Ciência [Oeiras] (IGC), Fundação Calouste Gulbenkian, University of Southern Denmark (SDU), University of Auckland [Auckland], University of Utah, Virginia Tech [Blacksburg], University of Luxembourg [Luxembourg], Integrative Bioinformatics Inc [Mountain View], Cleveland Clinic, Stellenbosch University, Broad Institute of MIT and Harvard (BROAD INSTITUTE), Harvard Medical School [Boston] (HMS)-Massachusetts Institute of Technology (MIT)-Massachusetts General Hospital [Boston], Universität Heidelberg [Heidelberg] = Heidelberg University, Leibniz Institute of Plant Genetics and Crop Plant Research [Gatersleben] (IPK-Gatersleben), Laboratoire de Biologie du Développement de Villefranche sur mer (LBDV), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de la Mer de Villefranche (IMEV), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Mount Sinai School of Medicine, Department of Psychiatry-Icahn School of Medicine at Mount Sinai [New York] (MSSM), University of Manchester [Manchester], Computational systems biology and optimization (Lifeware), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), California Institute of Technology (CALTECH), Encodia Inc [San Diego], Shinshu University [Nagano], University of Amsterdam [Amsterdam] (UvA), Versiti Blood Center of Wisconsin, Greifswald University Hospital, Bioinformatique évolutive - Evolutionary Bioinformatics, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Pacific Northwest National Laboratory (PNNL), National Institute of Allergy and Infectious Diseases [Bethesda] (NIAID-NIH), Department of Bioengineering, University of California (UC)-University of California (UC), ANSYS, Virginia Polytechnic Institute and State University [Blacksburg], Eight Pillars Ltd, Center for Integrative Genomics - Institute of Bioinformatics, Génopode (CIG), Université de Lausanne = University of Lausanne (UNIL)-Université de Lausanne = University of Lausanne (UNIL), Universität Heidelberg, Bioquant, Applied Biomathematics [New York], SimCYP Ltd, Institut de biologie de l'ENS Paris (IBENS), Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), University of Utah School of Medicine [Salt Lake City], University of Pittsburgh School of Medicine, Pennsylvania Commonwealth System of Higher Education (PCSHE), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Mizuho Information and Research Institute, Mathématiques et Informatique Appliquées du Génome à l'Environnement [Jouy-En-Josas] (MaIAGE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), University of Oxford, Computer Science (North Carolina State University), North Carolina State University [Raleigh] (NC State), University of North Carolina System (UNC)-University of North Carolina System (UNC), University of California [Irvine] (UC Irvine), Indian Institute of Technology Madras (IIT Madras), California State University [Northridge] (CSUN), Biotechnology and Biological Sciences Research Council (BBSRC), IBM Research [Melbourne], Benaroya Research Institute [Seattle] (BRI), Okinawa Institute of Science and Technology Graduate University, Keio University, Department of Computing and Mathematical sciences, members, SBML Level 3 Community, Université de Lausanne (UNIL), Università degli Studi di Milano-Bicocca [Milano] (UNIMIB), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), University of California, Universiteit Leiden [Leiden], Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Inria Grenoble - Rhône-Alpes, Humboldt University of Berlin, Universität Heidelberg [Heidelberg], Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Humboldt-Universität zu Berlin, University of California-University of California, Université de Lausanne (UNIL)-Université de Lausanne (UNIL), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), University of Oxford [Oxford], University of California [Irvine] (UCI), Biotechnology and Biological Sciences Research Council, Computer Science, Institut de biologie de l'ENS Paris (UMR 8197/1024) (IBENS), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris)

Subjects
computational modeling, Medicine (General), Markup language, [SDV.BIO]Life Sciences [q-bio]/Biotechnology, INFORMATION, Interoperability, interoperability, Review, [SDV.BC.BC]Life Sciences [q-bio]/Cellular Biology/Subcellular Processes [q-bio.SC], ANNOTATION, 0302 clinical medicine, Software, file forma, Models, Biology (General), 0303 health sciences, Computational model, Applied Mathematics, Systems Biology, systems biology, File format, 3. Good health, Networking and Information Technology R&D, Networking and Information Technology R&D (NITRD), Computational Theory and Mathematics, SIMULATION, General Agricultural and Biological Sciences, STANDARDS, REPOSITORY, Information Systems, QH301-705.5, Bioinformatics, Systems biology, Software ecosystem, Reviews, Bioengineering, Methods & Resources, Biology, MARKUP LANGUAGE, Models, Biological, SBML Level 3 Community members, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, R5-920, Animals, Humans, SBML, reproducibility, 030304 developmental biology, ENVIRONMENT, General Immunology and Microbiology, file format, business.industry, Computational Biology, Biological, ONTOLOGY, Metabolism, Logistic Models, Biochemistry and Cell Biology, Other Biological Sciences, Software engineering, business, 030217 neurology & neurosurgery
Abstract

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction‐based models and packages that extend the core with features suited to other model types including constraint‐based models, reaction‐diffusion models, logical network models, and rule‐based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single‐cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution., Over the past two decades, scientists from different fields have been developing SBML, a standard format for encoding computational models in biology and medicine. This article summarizes recent progress and gives perspectives on emerging challenges.

Published
2020

13. Correction: Reproducible big data science: A case study in continuous FAIRness.

Author

Madduri R, Chard K, D'Arcy M, Jung SC, Rodriguez A, Sulakhe D, Deutsch E, Funk C, Heavner B, Richards M, Shannon P, Glusman G, Price N, Kesselman C, and Foster I

Abstract

[This corrects the article DOI: 10.1371/journal.pone.0213013.]., (Copyright: © 2023 Madduri et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Published
2023
Full Text
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14. Building a collaborative cloud platform to accelerate heart, lung, blood, and sleep research.

Author

Ahalt S, Avillach P, Boyles R, Bradford K, Cox S, Davis-Dusenbery B, Grossman RL, Krishnamurthy A, Manning A, Paten B, Philippakis A, Borecki I, Chen SH, Kaltman J, Ladwa S, Schwartz C, Thomson A, Davis S, Leaf A, Lyons J, Sheets E, Bis JC, Conomos M, Culotti A, Desain T, Digiovanna J, Domazet M, Gogarten S, Gutierrez-Sacristan A, Harris T, Heavner B, Jain D, O'Connor B, Osborn K, Pillion D, Pleiness J, Rice K, Rupp G, Serret-Larmande A, Smith A, Stedman JP, Stilp A, Barsanti T, Cheadle J, Erdmann C, Farlow B, Gartland-Gray A, Hayes J, Hiles H, Kerr P, Lenhardt C, Madden T, Mieczkowska JO, Miller A, Patton P, Rathbun M, Suber S, and Asare J

Subjects
Humans, Ecosystem, Reproducibility of Results, Lung, Software, Cloud Computing, COVID-19
Abstract

Research increasingly relies on interrogating large-scale data resources. The NIH National Heart, Lung, and Blood Institute developed the NHLBI BioData CatalystⓇ (BDC), a community-driven ecosystem where researchers, including bench and clinical scientists, statisticians, and algorithm developers, find, access, share, store, and compute on large-scale datasets. This ecosystem provides secure, cloud-based workspaces, user authentication and authorization, search, tools and workflows, applications, and new innovative features to address community needs, including exploratory data analysis, genomic and imaging tools, tools for reproducibility, and improved interoperability with other NIH data science platforms. BDC offers straightforward access to large-scale datasets and computational resources that support precision medicine for heart, lung, blood, and sleep conditions, leveraging separately developed and managed platforms to maximize flexibility based on researcher needs, expertise, and backgrounds. Through the NHLBI BioData Catalyst Fellows Program, BDC facilitates scientific discoveries and technological advances. BDC also facilitated accelerated research on the coronavirus disease-2019 (COVID-19) pandemic., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Published
2023
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15. Genetic determinants of telomere length from 109,122 ancestrally diverse whole-genome sequences in TOPMed.

Author

Taub MA, Conomos MP, Keener R, Iyer KR, Weinstock JS, Yanek LR, Lane J, Miller-Fleming TW, Brody JA, Raffield LM, McHugh CP, Jain D, Gogarten SM, Laurie CA, Keramati A, Arvanitis M, Smith AV, Heavner B, Barwick L, Becker LC, Bis JC, Blangero J, Bleecker ER, Burchard EG, Celedón JC, Chang YPC, Custer B, Darbar D, de las Fuentes L, DeMeo DL, Freedman BI, Garrett ME, Gladwin MT, Heckbert SR, Hidalgo BA, Irvin MR, Islam T, Johnson WC, Kaab S, Launer L, Lee J, Liu S, Moscati A, North KE, Peyser PA, Rafaels N, Seidman C, Weeks DE, Wen F, Wheeler MM, Williams LK, Yang IV, Zhao W, Aslibekyan S, Auer PL, Bowden DW, Cade BE, Chen Z, Cho MH, Cupples LA, Curran JE, Daya M, Deka R, Eng C, Fingerlin TE, Guo X, Hou L, Hwang SJ, Johnsen JM, Kenny EE, Levin AM, Liu C, Minster RL, Naseri T, Nouraie M, Reupena MS, Sabino EC, Smith JA, Smith NL, Su JL, Taylor JG, Telen MJ, Tiwari HK, Tracy RP, White MJ, Zhang Y, Wiggins KL, Weiss ST, Vasan RS, Taylor KD, Sinner MF, Silverman EK, Shoemaker MB, Sheu WH, Sciurba F, Schwartz DA, Rotter JI, Roden D, Redline S, Raby BA, Psaty BM, Peralta JM, Palmer ND, Nekhai S, Montgomery CG, Mitchell BD, Meyers DA, McGarvey ST, Mak AC, Loos RJ, Kumar R, Kooperberg C, Konkle BA, Kelly S, Kardia SL, Kaplan R, He J, Gui H, Gilliland FD, Gelb BD, Fornage M, Ellinor PT, de Andrade M, Correa A, Chen YI, Boerwinkle E, Barnes KC, Ashley-Koch AE, Arnett DK, Laurie CC, Abecasis G, Nickerson DA, Wilson JG, Rich SS, Levy D, Ruczinski I, Aviv A, Blackwell TW, Thornton T, O'Connell J, Cox NJ, Perry JA, Armanios M, Battle A, Pankratz N, Reiner AP, and Mathias RA

Abstract

Genetic studies on telomere length are important for understanding age-related diseases. Prior GWAS for leukocyte TL have been limited to European and Asian populations. Here, we report the first sequencing-based association study for TL across ancestrally-diverse individuals (European, African, Asian and Hispanic/Latino) from the NHLBI Trans-Omics for Precision Medicine (TOPMed) program. We used whole genome sequencing (WGS) of whole blood for variant genotype calling and the bioinformatic estimation of telomere length in n=109,122 individuals. We identified 59 sentinel variants (p-value <5×10 -9 ) in 36 loci associated with telomere length, including 20 newly associated loci (13 were replicated in external datasets). There was little evidence of effect size heterogeneity across populations. Fine-mapping at OBFC1 indicated the independent signals colocalized with cell-type specific eQTLs for OBFC1 ( STN1 ). Using a multi-variant gene-based approach, we identified two genes newly implicated in telomere length, DCLRE1B ( SNM1B ) and PARN . In PheWAS, we demonstrated our TL polygenic trait scores (PTS) were associated with increased risk of cancer-related phenotypes., Competing Interests: Declaration of Interests: The authors declare the following competing interests: J.C.C. has received research materials from GlaxoSmithKline and Merck (inhaled steroids) and Pharmavite (vitamin D and placebo capsules) to provide medications free of cost to participants in NIH-funded studies, unrelated to the current work. B.I.F. is a consultant for AstraZeneca Pharmaceuticals and RenalytixAI L.W. is on the advisory board for GlaxoSmithKline and receives grant funding from NIAID, NHLBI, and NIDDK, NIH I.V.Y. is a consultant for ElevenP15 S.A. receives equity and salary from 23andMe, Inc. M.H.C. receives grant support from GlaxoSmithKline S.T.W. receives royalties from UpToDate E.K.S. received grant support from GlaxoSmithKline and Bayer in the past three years. B.M.P. serves on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. F.D.M. is supported by grants from NIH/NHLBI (HL139054,HL091889,HL132523,HL130045,HL098112,HL056177), the NIH/NIEHS (ES006614), the NIH/NIAID (AI126614), and the NIH/ Office of Director (OD023282). Vifor Pharmaceuticals provided medicine and additional funding to support recruitment for HL130045. Dr. Martinez is a council member for the Council for the Developing Child. P.T.E. is supported by a grant from Bayer AG to the Broad Institute focused on the genetics and therapeutics of cardiovascular diseases. Dr. Ellinor has also served on advisory boards or consulted for Bayer AG, Quest Diagnostics, and Novartis. K.C.B. receives royalties from UpToDate G.A. is an employee of Regeneron Pharmaceuticals and owns stock and stock options for Regeneron Pharmaceuticals. A.M. is an employee of Regeneron Pharmaceuticals and owns stock and stock options for Regeneron Pharmaceuticals. A.B. is a consultant for Third Rock Ventures, LLC and holds stock in Google, Inc. D.A.S. is the founder and chief scientific officer of Eleven P15, a company focused on the early diagnosis and treatment of pulmonary fibrosis

Published
2022
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16. A Model of Minor Histocompatibility Antigens in Allogeneic Hematopoietic Cell Transplantation.

Author

Martin PJ, Levine DM, Storer BE, Zheng X, Jain D, Heavner B, Norris BM, Geraghty DE, Spellman SR, Sather CL, Wu F, and Hansen JA

Subjects
Adolescent, Adult, Aged, Alleles, Child, Child, Preschool, Disease Susceptibility immunology, Female, Genetic Predisposition to Disease, Genetic Variation, Graft vs Host Disease epidemiology, Graft vs Host Disease etiology, HLA Antigens genetics, HLA Antigens immunology, Humans, Incidence, Infant, Infant, Newborn, Linkage Disequilibrium, Male, Middle Aged, Minor Histocompatibility Antigens genetics, Peptides genetics, Peptides immunology, Transplantation, Homologous, Young Adult, Hematopoietic Stem Cell Transplantation adverse effects, Hematopoietic Stem Cell Transplantation methods, Minor Histocompatibility Antigens immunology, Transplantation Immunology
Abstract

Minor histocompatibility antigens (mHAg) composed of peptides presented by HLA molecules can cause immune responses involved in graft-versus-host disease (GVHD) and graft-versus-leukemia effects after allogeneic hematopoietic cell transplantation (HCT). The current study was designed to identify individual graft-versus-host genomic mismatches associated with altered risks of acute or chronic GVHD or relapse after HCT between HLA-genotypically identical siblings. Our results demonstrate that in allogeneic HCT between a pair of HLA-identical siblings, a mHAg manifests as a set of peptides originating from annotated proteins and non-annotated open reading frames, which i) are encoded by a group of highly associated recipient genomic mismatches, ii) bind to HLA allotypes in the recipient, and iii) evoke a donor immune response. Attribution of the immune response and consequent clinical outcomes to individual peptide components within this set will likely differ from patient to patient according to their HLA types., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Martin, Levine, Storer, Zheng, Jain, Heavner, Norris, Geraghty, Spellman, Sather, Wu and Hansen.)

Published
2021
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17. BinomiRare: A robust test for association of a rare genetic variant with a binary outcome for mixed models and any case-control proportion.

Author

Sofer T, Lee J, Kurniansyah N, Jain D, Laurie CA, Gogarten SM, Conomos MP, Heavner B, Hu Y, Kooperberg C, Haessler J, Vasan RS, Cupples LA, Coombes BJ, Seyerle A, Gharib SA, Chen H, O'Connell JR, Zhang M, Gottlieb DJ, Psaty BM, Longstreth WT Jr, Rotter JI, Taylor KD, Rich SS, Guo X, Boerwinkle E, Morrison AC, Pankow JS, Johnson AD, Pankratz N, Reiner AP, Redline S, Smith NL, Rice KM, and Schifano ED

Abstract

Whole-genome sequencing (WGS) and whole-exome sequencing studies have become increasingly available and are being used to identify rare genetic variants associated with health and disease outcomes. Investigators routinely use mixed models to account for genetic relatedness or other clustering variables (e.g., family or household) when testing genetic associations. However, no existing tests of the association of a rare variant with a binary outcome in the presence of correlated data control the type 1 error where there are (1) few individuals harboring the rare allele, (2) a small proportion of cases relative to controls, and (3) covariates to adjust for. Here, we address all three issues in developing a framework for testing rare variant association with a binary trait in individuals harboring at least one risk allele. In this framework, we estimate outcome probabilities under the null hypothesis and then use them, within the individuals with at least one risk allele, to test variant associations. We extend the BinomiRare test, which was previously proposed for independent observations, and develop the Conway-Maxwell-Poisson (CMP) test and study their properties in simulations. We show that the BinomiRare test always controls the type 1 error, while the CMP test sometimes does not. We then use the BinomiRare test to test the association of rare genetic variants in target genes with small-vessel disease (SVD) stroke, short sleep, and venous thromboembolism (VTE), in whole-genome sequence data from the Trans-Omics for Precision Medicine (TOPMed) program., Competing Interests: Declaration of interests B.M.P. serves on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. All other authors declare no competing interests.

Published
2021
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18. Atlas of Transcription Factor Binding Sites from ENCODE DNase Hypersensitivity Data across 27 Tissue Types.

Author

Funk CC, Casella AM, Jung S, Richards MA, Rodriguez A, Shannon P, Donovan-Maiye R, Heavner B, Chard K, Xiao Y, Glusman G, Ertekin-Taner N, Golde TE, Toga A, Hood L, Van Horn JD, Kesselman C, Foster I, Madduri R, Price ND, and Ament SA

Subjects
Humans, Binding Sites genetics, Deoxyribonucleases metabolism, Genomics methods, Transcription Factors metabolism
Abstract

Characterizing the tissue-specific binding sites of transcription factors (TFs) is essential to reconstruct gene regulatory networks and predict functions for non-coding genetic variation. DNase-seq footprinting enables the prediction of genome-wide binding sites for hundreds of TFs simultaneously. Despite the public availability of high-quality DNase-seq data from hundreds of samples, a comprehensive, up-to-date resource for the locations of genomic footprints is lacking. Here, we develop a scalable footprinting workflow using two state-of-the-art algorithms: Wellington and HINT. We apply our workflow to detect footprints in 192 ENCODE DNase-seq experiments and predict the genomic occupancy of 1,515 human TFs in 27 human tissues. We validate that these footprints overlap true-positive TF binding sites from ChIP-seq. We demonstrate that the locations, depth, and tissue specificity of footprints predict effects of genetic variants on gene expression and capture a substantial proportion of genetic risk for complex traits., Competing Interests: Declaration of Interests The authors declare no competing interests., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Published
2020
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19. Reproducible big data science: A case study in continuous FAIRness.

Author

Madduri R, Chard K, D'Arcy M, Jung SC, Rodriguez A, Sulakhe D, Deutsch E, Funk C, Heavner B, Richards M, Shannon P, Glusman G, Price N, Kesselman C, and Foster I

Subjects
Algorithms, Humans, Information Dissemination, Longitudinal Studies, Software, Big Data, Data Science statistics & numerical data, Databases, Factual statistics & numerical data
Abstract

Big biomedical data create exciting opportunities for discovery, but make it difficult to capture analyses and outputs in forms that are findable, accessible, interoperable, and reusable (FAIR). In response, we describe tools that make it easy to capture, and assign identifiers to, data and code throughout the data lifecycle. We illustrate the use of these tools via a case study involving a multi-step analysis that creates an atlas of putative transcription factor binding sites from terabytes of ENCODE DNase I hypersensitive sites sequencing data. We show how the tools automate routine but complex tasks, capture analysis algorithms in understandable and reusable forms, and harness fast networks and powerful cloud computers to process data rapidly, all without sacrificing usability or reproducibility-thus ensuring that big data are not hard-to-(re)use data. We evaluate our approach via a user study, and show that 91% of participants were able to replicate a complex analysis involving considerable data volumes., Competing Interests: The authors have declared that no competing interests exist.

Published
2019
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20. Predictive Big Data Analytics: A Study of Parkinson's Disease Using Large, Complex, Heterogeneous, Incongruent, Multi-Source and Incomplete Observations.

Author

Dinov ID, Heavner B, Tang M, Glusman G, Chard K, Darcy M, Madduri R, Pa J, Spino C, Kesselman C, Foster I, Deutsch EW, Price ND, Van Horn JD, Ames J, Clark K, Hood L, Hampstead BM, Dauer W, and Toga AW

Subjects
Aged, Disease Progression, Female, Humans, Logistic Models, Male, Neuroimaging, Parkinson Disease genetics, Parkinson Disease pathology, Support Vector Machine, Databases, Factual, Parkinson Disease diagnosis
Abstract

Background: A unique archive of Big Data on Parkinson's Disease is collected, managed and disseminated by the Parkinson's Progression Markers Initiative (PPMI). The integration of such complex and heterogeneous Big Data from multiple sources offers unparalleled opportunities to study the early stages of prevalent neurodegenerative processes, track their progression and quickly identify the efficacies of alternative treatments. Many previous human and animal studies have examined the relationship of Parkinson's disease (PD) risk to trauma, genetics, environment, co-morbidities, or life style. The defining characteristics of Big Data-large size, incongruency, incompleteness, complexity, multiplicity of scales, and heterogeneity of information-generating sources-all pose challenges to the classical techniques for data management, processing, visualization and interpretation. We propose, implement, test and validate complementary model-based and model-free approaches for PD classification and prediction. To explore PD risk using Big Data methodology, we jointly processed complex PPMI imaging, genetics, clinical and demographic data., Methods and Findings: Collective representation of the multi-source data facilitates the aggregation and harmonization of complex data elements. This enables joint modeling of the complete data, leading to the development of Big Data analytics, predictive synthesis, and statistical validation. Using heterogeneous PPMI data, we developed a comprehensive protocol for end-to-end data characterization, manipulation, processing, cleaning, analysis and validation. Specifically, we (i) introduce methods for rebalancing imbalanced cohorts, (ii) utilize a wide spectrum of classification methods to generate consistent and powerful phenotypic predictions, and (iii) generate reproducible machine-learning based classification that enables the reporting of model parameters and diagnostic forecasting based on new data. We evaluated several complementary model-based predictive approaches, which failed to generate accurate and reliable diagnostic predictions. However, the results of several machine-learning based classification methods indicated significant power to predict Parkinson's disease in the PPMI subjects (consistent accuracy, sensitivity, and specificity exceeding 96%, confirmed using statistical n-fold cross-validation). Clinical (e.g., Unified Parkinson's Disease Rating Scale (UPDRS) scores), demographic (e.g., age), genetics (e.g., rs34637584, chr12), and derived neuroimaging biomarker (e.g., cerebellum shape index) data all contributed to the predictive analytics and diagnostic forecasting., Conclusions: Model-free Big Data machine learning-based classification methods (e.g., adaptive boosting, support vector machines) can outperform model-based techniques in terms of predictive precision and reliability (e.g., forecasting patient diagnosis). We observed that statistical rebalancing of cohort sizes yields better discrimination of group differences, specifically for predictive analytics based on heterogeneous and incomplete PPMI data. UPDRS scores play a critical role in predicting diagnosis, which is expected based on the clinical definition of Parkinson's disease. Even without longitudinal UPDRS data, however, the accuracy of model-free machine learning based classification is over 80%. The methods, software and protocols developed here are openly shared and can be employed to study other neurodegenerative disorders (e.g., Alzheimer's, Huntington's, amyotrophic lateral sclerosis), as well as for other predictive Big Data analytics applications.

Published
2016
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