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

Author

Sarah M Keating, Dagmar Waltemath, Matthias König, Fengkai Zhang, Andreas Dräger, Claudine Chaouiya, Frank T Bergmann, Andrew Finney, Colin S Gillespie, Tomáš Helikar, Stefan Hoops, Rahuman S Malik‐Sheriff, Stuart L Moodie, Ion I Moraru, Chris J Myers, Aurélien Naldi, Brett G Olivier, Sven Sahle, James C Schaff, Lucian P Smith, Maciej J Swat, Denis Thieffry, Leandro Watanabe, Darren J Wilkinson, Michael L Blinov, Kimberly Begley, James R Faeder, Harold F Gómez, Thomas M Hamm, Yuichiro Inagaki, Wolfram Liebermeister, Allyson L Lister, Daniel Lucio, Eric Mjolsness, Carole J Proctor, Karthik Raman, Nicolas Rodriguez, Clifford A Shaffer, Bruce E Shapiro, Joerg Stelling, Neil Swainston, Naoki Tanimura, John Wagner, Martin Meier‐Schellersheim, Herbert M Sauro, Bernhard Palsson, Hamid Bolouri, Hiroaki Kitano, Akira Funahashi, Henning Hermjakob, John C Doyle, Michael Hucka, SBML Level 3 Community members, Richard R Adams, Nicholas A Allen, Bastian R Angermann, Marco Antoniotti, Gary D Bader, Jan Červený, Mélanie Courtot, Chris D Cox, Piero Dalle Pezze, Emek Demir, William S Denney, Harish Dharuri, Julien Dorier, Dirk Drasdo, Ali Ebrahim, Johannes Eichner, Johan Elf, Lukas Endler, Chris T Evelo, Christoph Flamm, Ronan MT Fleming, Martina Fröhlich, Mihai Glont, Emanuel Gonçalves, Martin Golebiewski, Hovakim Grabski, Alex Gutteridge, Damon Hachmeister, Leonard A Harris, Benjamin D Heavner, Ron Henkel, William S Hlavacek, Bin Hu, Daniel R Hyduke, Hidde de Jong, Nick Juty, Peter D Karp, Jonathan R Karr, Douglas B Kell, Roland Keller, Ilya Kiselev, Steffen Klamt, Edda Klipp, Christian Knüpfer, Fedor Kolpakov, Falko Krause, Martina Kutmon, Camille Laibe, Conor Lawless, Lu Li, Leslie M Loew, Rainer Machne, Yukiko Matsuoka, Pedro Mendes, Huaiyu Mi, Florian Mittag, Pedro T Monteiro, Kedar Nath Natarajan, Poul MF Nielsen, Tramy Nguyen, Alida Palmisano, Jean‐Baptiste Pettit, Thomas Pfau, Robert D Phair, Tomas Radivoyevitch, Johann M Rohwer, Oliver A Ruebenacker, Julio Saez‐Rodriguez, Martin Scharm, Henning Schmidt, Falk Schreiber, Michael Schubert, Roman Schulte, Stuart C Sealfon, Kieran Smallbone, Sylvain Soliman, Melanie I Stefan, Devin P Sullivan, Koichi Takahashi, Bas Teusink, David Tolnay, Ibrahim Vazirabad, Axel von Kamp, Ulrike Wittig, Clemens Wrzodek, Finja Wrzodek, Ioannis Xenarios, Anna Zhukova, and Jeremy Zucker

Subjects
computational modeling, file format, interoperability, reproducibility, systems biology, Biology (General), QH301-705.5, Medicine (General), R5-920
Abstract

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
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2. Whole genome sequence analyses of eGFR in 23,732 people representing multiple ancestries in the NHLBI trans-omics for precision medicine (TOPMed) consortium

Author

Bridget M Lin, Kelsey E Grinde, Jennifer A Brody, Charles E Breeze, Laura M Raffield, Josyf C Mychaleckyj, Timothy A Thornton, James A Perry, Leslie J Baier, Lisa de las Fuentes, Xiuqing Guo, Benjamin D Heavner, Robert L Hanson, Yi-Jen Hung, Huijun Qian, Chao A Hsiung, Shih-Jen Hwang, Margaret R Irvin, Deepti Jain, Tanika N Kelly, Sayuko Kobes, Leslie Lange, James P Lash, Yun Li, Xiaoming Liu, Xuenan Mi, Solomon K Musani, George J Papanicolaou, Afshin Parsa, Alex P Reiner, Shabnam Salimi, Wayne H-H Sheu, Alan R Shuldiner, Kent D Taylor, Albert V Smith, Jennifer A Smith, Adrienne Tin, Dhananjay Vaidya, Robert B Wallace, Kenichi Yamamoto, Saori Sakaue, Koichi Matsuda, Yoichiro Kamatani, Yukihide Momozawa, Lisa R Yanek, Betsi A Young, Wei Zhao, Yukinori Okada, Gonzalo Abecasis, Bruce M Psaty, Donna K Arnett, Eric Boerwinkle, Jianwen Cai, Ida Yii-Der Chen, Adolfo Correa, L Adrienne Cupples, Jiang He, Sharon LR Kardia, Charles Kooperberg, Rasika A Mathias, Braxton D Mitchell, Deborah A Nickerson, Steve T Turner, Vasan S Ramachandran, Jerome I Rotter, Daniel Levy, Holly J Kramer, Anna Köttgen, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Kidney Working Group, Stephen S Rich, Dan-Yu Lin, Sharon R Browning, and Nora Franceschini

Subjects
Whole genome sequencing, Kidney traits, Rare variants, Ancestry-specific variants, Medicine, Medicine (General), R5-920
Abstract

Background: Genetic factors that influence kidney traits have been understudied for low frequency and ancestry-specific variants. Methods: We combined whole genome sequencing (WGS) data from 23,732 participants from 10 NHLBI Trans-Omics for Precision Medicine (TOPMed) Program multi-ethnic studies to identify novel loci for estimated glomerular filtration rate (eGFR). Participants included European, African, East Asian, and Hispanic ancestries. We applied linear mixed models using a genetic relationship matrix estimated from the WGS data and adjusted for age, sex, study, and ethnicity. Findings: When testing single variants, we identified three novel loci driven by low frequency variants more commonly observed in non-European ancestry (PRKAA2, rs180996919, minor allele frequency [MAF] 0.04%, P = 6.1 × 10−11; METTL8, rs116951054, MAF 0.09%, P = 4.5 × 10−9; and MATK, rs539182790, MAF 0.05%, P = 3.4 × 10−9). We also replicated two known loci for common variants (rs2461702, MAF=0.49, P = 1.2 × 10−9, nearest gene GATM, and rs71147340, MAF=0.34, P = 3.3 × 10−9, CDK12). Testing aggregated variants within a gene identified the MAF gene. A statistical approach based on local ancestry helped to identify replication samples for ancestry-specific variants. Interpretation: This study highlights challenges in studying variants influencing kidney traits that are low frequency in populations and more common in non-European ancestry.

Published
2021
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3. Identification of novel and rare variants associated with handgrip strength using whole genome sequence data from the NHLBI Trans-Omics in Precision Medicine (TOPMed) Program.

Author

Chloé Sarnowski, Han Chen, Mary L Biggs, Sylvia Wassertheil-Smoller, Jan Bressler, Marguerite R Irvin, Kathleen A Ryan, David Karasik, Donna K Arnett, L Adrienne Cupples, David W Fardo, Stephanie M Gogarten, Benjamin D Heavner, Deepti Jain, Hyun Min Kang, Charles Kooperberg, Arch G Mainous, Braxton D Mitchell, Alanna C Morrison, Jeffrey R O'Connell, Bruce M Psaty, Kenneth Rice, Albert V Smith, Ramachandran S Vasan, B Gwen Windham, Douglas P Kiel, Joanne M Murabito, Kathryn L Lunetta, TOPMed Longevity and Healthy Aging Working Group, and from the NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium

Subjects
Medicine, Science
Abstract

Handgrip strength is a widely used measure of muscle strength and a predictor of a range of morbidities including cardiovascular diseases and all-cause mortality. Previous genome-wide association studies of handgrip strength have focused on common variants primarily in persons of European descent. We aimed to identify rare and ancestry-specific genetic variants associated with handgrip strength by conducting whole-genome sequence association analyses using 13,552 participants from six studies representing diverse population groups from the Trans-Omics in Precision Medicine (TOPMed) Program. By leveraging multiple handgrip strength measures performed in study participants over time, we increased our effective sample size by 7-12%. Single-variant analyses identified ten handgrip strength loci among African-Americans: four rare variants, five low-frequency variants, and one common variant. One significant and four suggestive genes were identified associated with handgrip strength when aggregating rare and functional variants; all associations were ancestry-specific. We additionally leveraged the different ancestries available in the UK Biobank to further explore the ancestry-specific association signals from the single-variant association analyses. In conclusion, our study identified 11 new loci associated with handgrip strength with rare and/or ancestry-specific genetic variations, highlighting the added value of whole-genome sequencing in diverse samples. Several of the associations identified using single-variant or aggregate analyses lie in genes with a function relevant to the brain or muscle or were reported to be associated with muscle or age-related traits. Further studies in samples with sequence data and diverse ancestries are needed to confirm these findings.

Published
2021
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4. Combining inferred regulatory and reconstructed metabolic networks enhances phenotype prediction in yeast.

Author

Zhuo Wang, Samuel A Danziger, Benjamin D Heavner, Shuyi Ma, Jennifer J Smith, Song Li, Thurston Herricks, Evangelos Simeonidis, Nitin S Baliga, John D Aitchison, and Nathan D Price

Subjects
Biology (General), QH301-705.5
Abstract

Gene regulatory and metabolic network models have been used successfully in many organisms, but inherent differences between them make networks difficult to integrate. Probabilistic Regulation Of Metabolism (PROM) provides a partial solution, but it does not incorporate network inference and underperforms in eukaryotes. We present an Integrated Deduced And Metabolism (IDREAM) method that combines statistically inferred Environment and Gene Regulatory Influence Network (EGRIN) models with the PROM framework to create enhanced metabolic-regulatory network models. We used IDREAM to predict phenotypes and genetic interactions between transcription factors and genes encoding metabolic activities in the eukaryote, Saccharomyces cerevisiae. IDREAM models contain many fewer interactions than PROM and yet produce significantly more accurate growth predictions. IDREAM consistently outperformed PROM using any of three popular yeast metabolic models and across three experimental growth conditions. Importantly, IDREAM's enhanced accuracy makes it possible to identify subtle synthetic growth defects. With experimental validation, these novel genetic interactions involving the pyruvate dehydrogenase complex suggested a new role for fatty acid-responsive factor Oaf1 in regulating acetyl-CoA production in glucose grown cells.

Published
2017
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5. Comparative Analysis of Yeast Metabolic Network Models Highlights Progress, Opportunities for Metabolic Reconstruction.

Author

Benjamin D Heavner and Nathan D Price

Subjects
Biology (General), QH301-705.5
Abstract

We have compared 12 genome-scale models of the Saccharomyces cerevisiae metabolic network published since 2003 to evaluate progress in reconstruction of the yeast metabolic network. We compared the genomic coverage, overlap of annotated metabolites, predictive ability for single gene essentiality with a selection of model parameters, and biomass production predictions in simulated nutrient-limited conditions. We have also compared pairwise gene knockout essentiality predictions for 10 of these models. We found that varying approaches to model scope and annotation reflected the involvement of multiple research groups in model development; that single-gene essentiality predictions were affected by simulated medium, objective function, and the reference list of essential genes; and that predictive ability for single-gene essentiality did not correlate well with predictive ability for our reference list of synthetic lethal gene interactions (R = 0.159). We conclude that the reconstruction of the yeast metabolic network is indeed gradually improving through the iterative process of model development, and there remains great opportunity for advancing our understanding of biology through continued efforts to reconstruct the full biochemical reaction network that constitutes yeast metabolism. Additionally, we suggest that there is opportunity for refining the process of deriving a metabolic model from a metabolic network reconstruction to facilitate mechanistic investigation and discovery. This comparative study lays the groundwork for developing improved tools and formalized methods to quantitatively assess metabolic network reconstructions independently of any particular model application, which will facilitate ongoing efforts to advance our understanding of the relationship between genotype and cellular phenotype.

Published
2015
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6. MediaDB: a database of microbial growth conditions in defined media.

Author

Matthew A Richards, Victor Cassen, Benjamin D Heavner, Nassim E Ajami, Andrea Herrmann, Evangelos Simeonidis, and Nathan D Price

Subjects
Medicine, Science
Abstract

Isolating pure microbial cultures and cultivating them in the laboratory on defined media is used to more fully characterize the metabolism and physiology of organisms. However, identifying an appropriate growth medium for a novel isolate remains a challenging task. Even organisms with sequenced and annotated genomes can be difficult to grow, despite our ability to build genome-scale metabolic networks that connect genomic data with metabolic function. The scientific literature is scattered with information about defined growth media used successfully for cultivating a wide variety of organisms, but to date there exists no centralized repository to inform efforts to cultivate less characterized organisms by bridging the gap between genomic data and compound composition for growth media. Here we present MediaDB, a manually curated database of defined media that have been used for cultivating organisms with sequenced genomes, with an emphasis on organisms with metabolic network models. The database is accessible online, can be queried by keyword searches or downloaded in its entirety, and can generate exportable individual media formulation files. The data assembled in MediaDB facilitate comparative studies of organism growth media, serve as a starting point for formulating novel growth media, and contribute to formulating media for in silico investigation of metabolic networks. MediaDB is freely available for public use at https://mediadb.systemsbiology.net.

Published
2014
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7. Building a collaborative cloud platform to accelerate heart, lung, blood, and sleep research.

Author

Stan Ahalt, Paul Avillach, Rebecca R. Boyles, Kira Bradford, Steven Cox 0001, Brandi Davis-Dusenbery, Robert L. Grossman, Ashok K. Krishnamurthy 0001, Alisa Manning, Benedict Paten, Anthony Philippakis, Ingrid Borecki, Shu Hui Chen, Jon Kaltman, Sweta Ladwa, Chip Schwartz, Alastair Thomson, Sarah Davis, Alison Leaf, Jessica Lyons, Elizabeth Sheets, Joshua C. Bis, Matthew P. Conomos, Alessandro Culotti, Thomas N. Desain, Jack DiGiovanna, Milan Domazet, Stephanie M. Gogarten, Alba Gutiérrez-Sacristán, Tim Harris 0003, Benjamin D. Heavner, Deepti Jain, Brian O'Connor, Kevin Osborn, Danielle Pillion, Jacob Pleiness, Ken Rice, Garrett Rupp, Arnaud Serret-Larmande, Albert Smith, Jason P. Stedman, Adrienne Stilp, Teresa Barsanti, John B. Cheadle, Christopher Erdmann, Brandy Farlow, Allie Gartland-Gray, Julie Hayes, Hannah Hiles, Paul Kerr, Chris Lenhardt 0001, Tom Madden, Joanna O. Mieczkowska, Amanda Miller, Patrick Patton, Marcie Rathbun, Stephanie Suber, and Joe Asare

Published
2023
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9. Insights From a Large-Scale Whole-Genome Sequencing Study of Systolic Blood Pressure, Diastolic Blood Pressure, and Hypertension

Author

Tanika N, Kelly, Xiao, Sun, Karen Y, He, Michael R, Brown, Sarah A Gagliano, Taliun, Jacklyn N, Hellwege, Marguerite R, Irvin, Xuenan, Mi, Jennifer A, Brody, Nora, Franceschini, Xiuqing, Guo, Shih-Jen, Hwang, Paul S, de Vries, Yan, Gao, Arden, Moscati, Girish N, Nadkarni, Lisa R, Yanek, Tali, Elfassy, Jennifer A, Smith, Ren-Hua, Chung, Amber L, Beitelshees, Amit, Patki, Stella, Aslibekyan, Brandon M, Blobner, Juan M, Peralta, Themistocles L, Assimes, Walter R, Palmas, Chunyu, Liu, Adam P, Bress, Zhijie, Huang, Lewis C, Becker, Chii-Min, Hwa, Jeffrey R, O'Connell, Jenna C, Carlson, Helen R, Warren, Sayantan, Das, Ayush, Giri, Lisa W, Martin, W, Craig Johnson, Ervin R, Fox, Erwin P, Bottinger, Alexander C, Razavi, Dhananjay, Vaidya, Lee-Ming, Chuang, Yen-Pei C, Chang, Take, Naseri, Deepti, Jain, Hyun Min, Kang, Adriana M, Hung, Vinodh, Srinivasasainagendra, Beverly M, Snively, Dongfeng, Gu, May E, Montasser, Muagututi'a Sefuiva, Reupena, Benjamin D, Heavner, Jonathon, LeFaive, James E, Hixson, Kenneth M, Rice, Fei Fei, Wang, Jonas B, Nielsen, Jianfeng, Huang, Alyna T, Khan, Wei, Zhou, Jovia L, Nierenberg, Cathy C, Laurie, Nicole D, Armstrong, Mengyao, Shi, Yang, Pan, Adrienne M, Stilp, Leslie, Emery, Quenna, Wong, Nicola L, Hawley, Ryan L, Minster, Joanne E, Curran, Patricia B, Munroe, Daniel E, Weeks, Kari E, North, Russell P, Tracy, Eimear E, Kenny, Daichi, Shimbo, Aravinda, Chakravarti, Stephen S, Rich, Alex P, Reiner, John, Blangero, Susan, Redline, Braxton D, Mitchell, Dabeeru C, Rao, Yii-Der, Ida Chen, Sharon L R, Kardia, Robert C, Kaplan, Rasika A, Mathias, Jiang, He, Bruce M, Psaty, Myriam, Fornage, Ruth J F, Loos, Adolfo, Correa, Eric, Boerwinkle, Jerome I, Rotter, Charles, Kooperberg, Todd L, Edwards, Gonçalo R, Abecasis, Xiaofeng, Zhu, Daniel, Levy, Donna K, Arnett, and Alanna C, Morrison

Subjects
Hypertension, Internal Medicine, Humans, Blood Pressure, Genomics, Precision Medicine, Polymorphism, Single Nucleotide, Genome-Wide Association Study
Abstract

Background: The availability of whole-genome sequencing data in large studies has enabled the assessment of coding and noncoding variants across the allele frequency spectrum for their associations with blood pressure. Methods: We conducted a multiancestry whole-genome sequencing analysis of blood pressure among 51 456 Trans-Omics for Precision Medicine and Centers for Common Disease Genomics program participants (stage-1). Stage-2 analyses leveraged array data from UK Biobank (N=383 145), Million Veteran Program (N=318 891), and Reasons for Geographic and Racial Differences in Stroke (N=10 643) participants, along with whole-exome sequencing data from UK Biobank (N=199 631) participants. Results: Two blood pressure signals achieved genome-wide significance in meta-analyses of stage-1 and stage-2 single variant findings ( P -8 ). Among them, a rare intergenic variant at novel locus, LOC100506274 , was associated with lower systolic blood pressure in stage-1 (beta [SE]=−32.6 [6.0]; P =4.99×10 -8 ) but not stage-2 analysis ( P =0.11). Furthermore, a novel common variant at the known INSR locus was suggestively associated with diastolic blood pressure in stage-1 (beta [SE]=−0.36 [0.07]; P =4.18×10 -7 ) and attained genome-wide significance in stage-2 (beta [SE]=−0.29 [0.03]; P =7.28×10 -23 ). Nineteen additional signals suggestively associated with blood pressure in meta-analysis of single and aggregate rare variant findings ( P -6 and P -4 , respectively). Discussion: We report one promising but unconfirmed rare variant for blood pressure and, more importantly, contribute insights for future blood pressure sequencing studies. Our findings suggest promise of aggregate analyses to complement single variant analysis strategies and the need for larger, diverse samples, and family studies to enable robust rare variant identification.

Published
2023

11. Whole-exome sequence analysis of anthropometric traits illustrates challenges in identifying effects of rare genetic variants

Author

Kristin L. Young, Virginia Fisher, Xuan Deng, Jennifer A. Brody, Misa Graff, Elise Lim, Bridget M. Lin, Hanfei Xu, Najaf Amin, Ping An, Stella Aslibekyan, Alison E. Fohner, Bertha Hidalgo, Petra Lenzini, Robert Kraaij, Carolina Medina-Gomez, Ivana Prokić, Fernando Rivadeneira, Colleen Sitlani, Ran Tao, Jeroen van Rooij, Di Zhang, Jai G. Broome, Erin J. Buth, Benjamin D. Heavner, Deepti Jain, Albert V. Smith, Kathleen Barnes, Meher Preethi Boorgula, Sameer Chavan, Dawood Darbar, Mariza De Andrade, Xiuqing Guo, Jeffrey Haessler, Marguerite R. Irvin, Rita R. Kalyani, Sharon L.R. Kardia, Charles Kooperberg, Wonji Kim, Rasika A. Mathias, Merry-Lynn McDonald, Braxton D. Mitchell, Patricia A. Peyser, Elizabeth A. Regan, Susan Redline, Alexander P. Reiner, Stephen S. Rich, Jerome I. Rotter, Jennifer A. Smith, Scott Weiss, Kerri L. Wiggins, Lisa R. Yanek, Donna Arnett, Nancy L. Heard-Costa, Suzanne Leal, Danyu Lin, Barbara McKnight, Michael Province, Cornelia M. van Duijn, Kari E. North, L. Adrienne Cupples, Ching-Ti Liu, Epidemiology, and Internal Medicine

Subjects
Anthropometry, central obesity, Quantitative Trait Loci, Human Genome, Cell Cycle Proteins, body mass index, Good Health and Well Being, Clinical Research, Genetics, Molecular Medicine, Humans, Intercellular Signaling Peptides and Proteins, 2.1 Biological and endogenous factors, Exome, Generic health relevance, Aetiology, exome sequencing, Genetics (clinical), Genome-Wide Association Study, height, Biotechnology
Abstract

Anthropometric traits, measuring body size and shape, are highly heritable and significant clinical risk factors for cardiometabolic disorders. These traits have been extensively studied in genome-wide association studies (GWASs), with hundreds of genome-wide significant loci identified. We performed a whole-exome sequence analysis of the genetics of height, body mass index (BMI) and waist/hip ratio (WHR). We meta-analyzed single-variant and gene-based associations of whole-exome sequence variation with height, BMI, and WHR in up to 22,004 individuals, and we assessed replication of our findings in up to 16,418 individuals from 10 independent cohorts from Trans-Omics for Precision Medicine (TOPMed). We identified four trait associations with single-nucleotide variants (SNVs; two for height and two for BMI) and replicated the LECT2 gene association with height. Our expression quantitative trait locus (eQTL) analysis within previously reported GWAS loci implicated CEP63 and RFT1 as potential functional genes for known height loci. We further assessed enrichment of SNVs, which were monogenic or syndromic variants within loci associated with our three traits. This led to the significant enrichment results for height, whereas we observed no Bonferroni-corrected significance for all SNVs. With a sample size of ∼20,000 whole-exome sequences in our discovery dataset, our findings demonstrate the importance of genomic sequencing in genetic association studies, yet they also illustrate the challenges in identifying effects of rare genetic variants.

Published
2023
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14. Identification of novel and rare variants associated with handgrip strength using whole genome sequence data from the NHLBI Trans-Omics in Precision Medicine (TOPMed) Program

Author

Han Chen, Braxton D. Mitchell, Charles Kooperberg, TOPMed Longevity, Kenneth Rice, Kathleen A. Ryan, David W. Fardo, Hyun Min Kang, Alanna C. Morrison, Arch G. Mainous, Jeffrey R. O'Connell, Albert V. Smith, Stephanie M. Gogarten, Ramachandran S. Vasan, Jan Bressler, Donna K. Arnett, Sylvia Wassertheil-Smoller, Joanne M. Murabito, Deepti Jain, B. Gwen Windham, Kathryn L. Lunetta, Benjamin D. Heavner, L. Adrienne Cupples, Mary L. Biggs, David Karasik, Bruce M. Psaty, Marguerite R. Irvin, Chloé Sarnowski, and Douglas P. Kiel

Subjects
0301 basic medicine, Male, Muscle Physiology, Muscle Functions, Physiology, Genome-wide association study, Cohort Studies, 0302 clinical medicine, Cell Signaling, Biomechanics, Precision Medicine, Aged, 80 and over, Multidisciplinary, Hand Strength, Genomics, Middle Aged, Biobank, Medicine, Female, Genomic Signal Processing, Research Article, Signal Transduction, Adult, Science, Computational biology, Biology, Polymorphism, Single Nucleotide, 03 medical and health sciences, Hand strength, Genetic variation, Genome-Wide Association Studies, Genetics, Humans, Genetic association, Aged, Whole genome sequencing, Whole Genome Sequencing, Racial Groups, Biology and Life Sciences, Computational Biology, Human Genetics, Cell Biology, Omics, Genome Analysis, 030104 developmental biology, Genetic Loci, human activities, 030217 neurology & neurosurgery
Abstract

Handgrip strength is a widely used measure of muscle strength and a predictor of a range of morbidities including cardiovascular diseases and all-cause mortality. Previous genome-wide association studies of handgrip strength have focused on common variants primarily in persons of European descent. We aimed to identify rare and ancestry-specific genetic variants associated with handgrip strength by conducting whole-genome sequence association analyses using 13,552 participants from six studies representing diverse population groups from the Trans-Omics in Precision Medicine (TOPMed) Program. By leveraging multiple handgrip strength measures performed in study participants over time, we increased our effective sample size by 7–12%. Single-variant analyses identified ten handgrip strength loci among African-Americans: four rare variants, five low-frequency variants, and one common variant. One significant and four suggestive genes were identified associated with handgrip strength when aggregating rare and functional variants; all associations were ancestry-specific. We additionally leveraged the different ancestries available in the UK Biobank to further explore the ancestry-specific association signals from the single-variant association analyses. In conclusion, our study identified 11 new loci associated with handgrip strength with rare and/or ancestry-specific genetic variations, highlighting the added value of whole-genome sequencing in diverse samples. Several of the associations identified using single-variant or aggregate analyses lie in genes with a function relevant to the brain or muscle or were reported to be associated with muscle or age-related traits. Further studies in samples with sequence data and diverse ancestries are needed to confirm these findings.

Published
2021

15. 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
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16. SBML Level 3: an extensible format for the exchange and reuse of biological models

Author

Sarah M, Keating, Dagmar, Waltemath, Matthias, König, Fengkai, Zhang, Andreas, Dräger, Claudine, Chaouiya, Frank T, Bergmann, Andrew, Finney, Colin S, Gillespie, Tomáš, Helikar, Stefan, Hoops, Rahuman S, Malik‐Sheriff, Stuart L, Moodie, Ion I, Moraru, Chris J, Myers, Aurélien, Naldi, Brett G, Olivier, Sven, Sahle, James C, Schaff, Lucian P, Smith, Maciej J, Swat, Denis, Thieffry, Leandro, Watanabe, Darren J, Wilkinson, Michael L, Blinov, Kimberly, Begley, James R, Faeder, Harold F, Gómez, Thomas M, Hamm, Yuichiro, Inagaki, Wolfram, Liebermeister, Allyson L, Lister, Daniel, Lucio, Eric, Mjolsness, Carole J, Proctor, Karthik, Raman, Nicolas, Rodriguez, Clifford A, Shaffer, Bruce E, Shapiro, Joerg, Stelling, Neil, Swainston, Naoki, Tanimura, John, Wagner, Martin, Meier‐Schellersheim, Herbert M, Sauro, Bernhard, Palsson, Hamid, Bolouri, Hiroaki, Kitano, Akira, Funahashi, Henning, Hermjakob, John C, Doyle, Michael, Hucka, Richard R, Adams, Nicholas A, Allen, Bastian R, Angermann, Marco, Antoniotti, Gary D, Bader, Jan, Červený, Mélanie, Courtot, Chris D, Cox, Piero, Dalle Pezze, Emek, Demir, William S, Denney, Harish, Dharuri, Julien, Dorier, Dirk, Drasdo, Ali, Ebrahim, Johannes, Eichner, Johan, Elf, Lukas, Endler, Chris T, Evelo, Christoph, Flamm, Ronan MT, Fleming, Martina, Fröhlich, Mihai, Glont, Emanuel, Gonçalves, Martin, Golebiewski, Hovakim, Grabski, Alex, Gutteridge, Damon, Hachmeister, Leonard A, Harris, Benjamin D, Heavner, Ron, Henkel, William S, Hlavacek, Bin, Hu, Daniel R, Hyduke, Hidde, Jong, Nick, Juty, Peter D, Karp, Jonathan R, Karr, Douglas B, Kell, Roland, Keller, Ilya, Kiselev, Steffen, Klamt, Edda, Klipp, Christian, Knüpfer, Fedor, Kolpakov, Falko, Krause, Martina, Kutmon, Camille, Laibe, Conor, Lawless, Lu, Li, Leslie M, Loew, Rainer, Machne, Yukiko, Matsuoka, Pedro, Mendes, Huaiyu, Mi, Florian, Mittag, Pedro T, Monteiro, Kedar Nath, Natarajan, Poul MF, Nielsen, Tramy, Nguyen, Alida, Palmisano, Jean‐Baptiste, Pettit, Thomas, Pfau, Robert D, Phair, Tomas, Radivoyevitch, Johann M, Rohwer, Oliver A, Ruebenacker, Julio, Saez‐Rodriguez, Martin, Scharm, Henning, Schmidt, Falk, Schreiber, Michael, Schubert, Roman, Schulte, Stuart C, Sealfon, Kieran, Smallbone, Sylvain, Soliman, Melanie I, Stefan, Devin P, Sullivan, Koichi, Takahashi, Bas, Teusink, David, Tolnay, Ibrahim, Vazirabad, Axel, Kamp, Ulrike, Wittig, Clemens, Wrzodek, Finja, Wrzodek, Ioannis, Xenarios, Takahiro G, Yamada, Anna, Zhukova, Jeremy, Zucker, Sarah M, Keating, Dagmar, Waltemath, Matthias, König, Fengkai, Zhang, Andreas, Dräger, Claudine, Chaouiya, Frank T, Bergmann, Andrew, Finney, Colin S, Gillespie, Tomáš, Helikar, Stefan, Hoops, Rahuman S, Malik‐Sheriff, Stuart L, Moodie, Ion I, Moraru, Chris J, Myers, Aurélien, Naldi, Brett G, Olivier, Sven, Sahle, James C, Schaff, Lucian P, Smith, Maciej J, Swat, Denis, Thieffry, Leandro, Watanabe, Darren J, Wilkinson, Michael L, Blinov, Kimberly, Begley, James R, Faeder, Harold F, Gómez, Thomas M, Hamm, Yuichiro, Inagaki, Wolfram, Liebermeister, Allyson L, Lister, Daniel, Lucio, Eric, Mjolsness, Carole J, Proctor, Karthik, Raman, Nicolas, Rodriguez, Clifford A, Shaffer, Bruce E, Shapiro, Joerg, Stelling, Neil, Swainston, Naoki, Tanimura, John, Wagner, Martin, Meier‐Schellersheim, Herbert M, Sauro, Bernhard, Palsson, Hamid, Bolouri, Hiroaki, Kitano, Akira, Funahashi, Henning, Hermjakob, John C, Doyle, Michael, Hucka, Richard R, Adams, Nicholas A, Allen, Bastian R, Angermann, Marco, Antoniotti, Gary D, Bader, Jan, Červený, Mélanie, Courtot, Chris D, Cox, Piero, Dalle Pezze, Emek, Demir, William S, Denney, Harish, Dharuri, Julien, Dorier, Dirk, Drasdo, Ali, Ebrahim, Johannes, Eichner, Johan, Elf, Lukas, Endler, Chris T, Evelo, Christoph, Flamm, Ronan MT, Fleming, Martina, Fröhlich, Mihai, Glont, Emanuel, Gonçalves, Martin, Golebiewski, Hovakim, Grabski, Alex, Gutteridge, Damon, Hachmeister, Leonard A, Harris, Benjamin D, Heavner, Ron, Henkel, William S, Hlavacek, Bin, Hu, Daniel R, Hyduke, Hidde, Jong, Nick, Juty, Peter D, Karp, Jonathan R, Karr, Douglas B, Kell, Roland, Keller, Ilya, Kiselev, Steffen, Klamt, Edda, Klipp, Christian, Knüpfer, Fedor, Kolpakov, Falko, Krause, Martina, Kutmon, Camille, Laibe, Conor, Lawless, Lu, Li, Leslie M, Loew, Rainer, Machne, Yukiko, Matsuoka, Pedro, Mendes, Huaiyu, Mi, Florian, Mittag, Pedro T, Monteiro, Kedar Nath, Natarajan, Poul MF, Nielsen, Tramy, Nguyen, Alida, Palmisano, Jean‐Baptiste, Pettit, Thomas, Pfau, Robert D, Phair, Tomas, Radivoyevitch, Johann M, Rohwer, Oliver A, Ruebenacker, Julio, Saez‐Rodriguez, Martin, Scharm, Henning, Schmidt, Falk, Schreiber, Michael, Schubert, Roman, Schulte, Stuart C, Sealfon, Kieran, Smallbone, Sylvain, Soliman, Melanie I, Stefan, Devin P, Sullivan, Koichi, Takahashi, Bas, Teusink, David, Tolnay, Ibrahim, Vazirabad, Axel, Kamp, Ulrike, Wittig, Clemens, Wrzodek, Finja, Wrzodek, Ioannis, Xenarios, Takahiro G, Yamada, Anna, Zhukova, and Jeremy, Zucker

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., source:https://www.embopress.org/doi/full/10.15252/msb.20199110

Published
2020

18. BDQC: a general-purpose analytics tool for domain-blind validation of Big Data

Author

Arthur W. Toga, Kyle Chard, Li Na Zhao, Benjamin D. Heavner, Joseph Ames, Ivo D. Dinov, Nathan D. Price, Judy Pa, Mi H, Kristi A. Clark, Mike D'Arcy, Muruganujan A, Eric W. Deutsch, Max Robinson, Carl Kesselman, Roger Kramer, Donovan R, Ravi Madduri, Gustavo Glusman, Farshid Sepehrband, Van Horn J, Lee Hood, Bauman A, David S. Campbell, and Ian Foster

Subjects
0303 health sciences, Correctness, Computer science, business.industry, Heuristic (computer science), Big data, computer.software_genre, Pipeline (software), 03 medical and health sciences, 0302 clinical medicine, Software, Analytics, Outlier, Data mining, business, computer, 030217 neurology & neurosurgery, 030304 developmental biology
Abstract

Translational biomedical research is generating exponentially more data: thousands of whole-genome sequences (WGS) are now available; brain data are doubling every two years. Analyses of Big Data, including imaging, genomic, phenotypic, and clinical data, present qualitatively new challenges as well as opportunities. Among the challenges is a proliferation in ways analyses can fail, due largely to the increasing length and complexity of processing pipelines. Anomalies in input data, runtime resource exhaustion or node failure in a distributed computation can all cause pipeline hiccups that are not necessarily obvious in the output. Flaws that can taint results may persist undetected in complex pipelines, a danger amplified by the fact that research is often concurrent with the development of the software on which it depends. On the positive side, the huge sample sizes increase statistical power, which in turn can shed new insight and motivate innovative analytic approaches. We have developed a framework for Big Data Quality Control (BDQC) including an extensible set of heuristic and statistical analyses that identify deviations in data without regard to its meaning (domain-blind analyses). BDQC takes advantage of large sample sizes to classify the samples, estimate distributions and identify outliers. Such outliers may be symptoms of technology failure (e.g., truncated output of one step of a pipeline for a single genome) or may reveal unsuspected “ signal” in the data (e.g., evidence of aneuploidy in a genome). We have applied the framework to validate real-world WGS analysis pipelines. BDQC successfully identified data outliers representing various failure classes, including genome analyses missing a whole chromosome or part thereof, hidden among thousands of intermediary output files. These failures could then be resolved by reanalyzing the affected samples. BDQC both identified hidden flaws as well as yielded new insights into the data. BDQC is designed to complement quality software development practices. There are multiple benefits from the application of BDQC at all pipeline stages. By verifying input correctness, it can help avoid expensive computations on flawed data. Analysis of intermediary and final results facilitates recovery from aberrant termination of processes. All these computationally inexpensive verifications reduce cryptic analytical artifacts that could seriously preclude clinical-grade genome interpretation. BDQC is available at https://github.com/ini-bdds/bdqc.

Published
2018
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19. Combining Inferred Regulatory and Reconstructed Metabolic Networks Enhances Phenotype Prediction in Yeast

Author

Thurston Herricks, Samuel A. Danziger, Nitin S. Baliga, Song Li, Nathan D. Price, Zhuo Wang, Evangelos Simeonidis, Jennifer J. Smith, Shuyi Ma, John D. Aitchison, and Benjamin D. Heavner

Subjects
0301 basic medicine, Enzyme Metabolism, Gene regulatory network, Gene Expression, Metabolic network, Yeast and Fungal Models, Biochemistry, 0302 clinical medicine, Gene Regulatory Networks, Biology (General), Enzyme Chemistry, Genetics, Regulation of gene expression, 0303 health sciences, Ecology, Systems Biology, Phenotype, Experimental Organism Systems, Computational Theory and Mathematics, Modeling and Simulation, Eukaryote, Metabolic Networks and Pathways, Network Analysis, Research Article, Computer and Information Sciences, QH301-705.5, Systems biology, Saccharomyces cerevisiae, Computational biology, Biology, Research and Analysis Methods, Models, Biological, Enzyme Regulation, Metabolic Networks, Saccharomyces, 03 medical and health sciences, Cellular and Molecular Neuroscience, Model Organisms, Gene Regulation, Molecular Biology, Gene, Transcription factor, Ecology, Evolution, Behavior and Systematics, 030304 developmental biology, Organisms, Fungi, Biology and Life Sciences, Computational Biology, biology.organism_classification, Yeast, 030104 developmental biology, Enzymology, 030217 neurology & neurosurgery
Abstract

Gene regulatory and metabolic network models have been used successfully in many organisms, but inherent differences between them make networks difficult to integrate. Probabilistic Regulation Of Metabolism (PROM) provides a partial solution, but it does not incorporate network inference and underperforms in eukaryotes. We present an Integrated Deduced And Metabolism (IDREAM) method that combines statistically inferred Environment and Gene Regulatory Influence Network (EGRIN) models with the PROM framework to create enhanced metabolic-regulatory network models. We used IDREAM to predict phenotypes and genetic interactions between transcription factors and genes encoding metabolic activities in the eukaryote, Saccharomyces cerevisiae. IDREAM models contain many fewer interactions than PROM and yet produce significantly more accurate growth predictions. IDREAM consistently outperformed PROM using any of three popular yeast metabolic models and across three experimental growth conditions. Importantly, IDREAM’s enhanced accuracy makes it possible to identify subtle synthetic growth defects. With experimental validation, these novel genetic interactions involving the pyruvate dehydrogenase complex suggested a new role for fatty acid-responsive factor Oaf1 in regulating acetyl-CoA production in glucose grown cells., Author summary The integration of gene regulatory and metabolic network models is an important goal in computational biology, in order to develop methods that can identify the underlying mechanistic links in biological networks and advance metabolic engineering techniques. In this paper, we develop a framework called Integrated Deduced REgulation And Metabolism (IDREAM) that can improve our ability to predict phenotypes of microorganisms, and particularly it can address the challenges in evaluating phenotypic consequence of perturbing transcriptional regulation of metabolism in a eukaryotic cell. We compare the predictive performance of an IDREAM S. cerevisiae model with a PROM model using a TRN available from the YEASTRACT database. IDREAM outperforms PROM using any of three popular yeast metabolic models and across three experimental growth conditions, making it possible to identify subtle synthetic growth defects, and a new role for Oaf1 in the regulation of acetyl-CoA biosynthesis.

Published
2016
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20. MediaDB: a database of microbial growth conditions in defined media

Author

Evangelos Simeonidis, Matthew A. Richards, Victor Cassen, Nassim E. Ajami, Nathan D. Price, Benjamin D. Heavner, Andrea Herrmann, and Luxembourg Centre for Systems Biomedicine (LCSB): Experimental Neurobiology (Balling Group) [research center]

Subjects
Metabolic Processes, Computer and Information Sciences, Databases, Factual, Genomic data, In silico, Molecular Sequence Data, Metabolic network, lcsh:Medicine, Genomics, Multidisciplinary, general & others [F99] [Life sciences], Genetic Networks, Biology, computer.software_genre, Research and Analysis Methods, Genome, Biochemistry, Multidisciplinaire, généralités & autres [F99] [Sciences du vivant], Metabolic Networks, lcsh:Science, Organism growth, Multidisciplinary, Metabolic function, Database, Bacteria, Base Sequence, lcsh:R, Biology and Life Sciences, Computational Biology, Genome Analysis, Archaea, Culture Media, Chemically defined medium, Metabolism, Eukaryotic Cells, lcsh:Q, Metabolic Pathways, Biological Cultures, computer, Network Analysis, Research Article, Cell Culturing Techniques
Abstract

Isolating pure microbial cultures and cultivating them in the laboratory on defined media is used to more fully characterize the metabolism and physiology of organisms. However, identifying an appropriate growth medium for a novel isolate remains a challenging task. Even organisms with sequenced and annotated genomes can be difficult to grow, despite our ability to build genome-scale metabolic networks that connect genomic data with metabolic function. The scientific literature is scattered with information about defined growth media used successfully for cultivating a wide variety of organisms, but to date there exists no centralized repository to inform efforts to cultivate less characterized organisms by bridging the gap between genomic data and compound composition for growth media. Here we present MediaDB, a manually curated database of defined media that have been used for cultivating organisms with sequenced genomes, with an emphasis on organisms with metabolic network models. The database is accessible online, can be queried by keyword searches or downloaded in its entirety, and can generate exportable individual media formulation files. The data assembled in MediaDB facilitate comparative studies of organism growth media, serve as a starting point for formulating novel growth media, and contribute to formulating media for in silico investigation of metabolic networks. MediaDB is freely available for public use at https://mediadb.systemsbiology.net.

Published
2014

21. Yeast 5 – an expanded reconstruction of the Saccharomyces cerevisiae metabolic network

Author

Brandon Barker, Kieran Smallbone, Larry P. Walker, Benjamin D. Heavner, and Pedro Mendes

Subjects
Flux balance analysis, Systems biology, Saccharomyces cerevisiae, Genes, Fungal, Metabolic network, Computational biology, Models, Biological, GENRE, 03 medical and health sciences, Structured document, Structural Biology, Modelling and Simulation, SBML, Cooperative Behavior, Molecular Biology, lcsh:QH301-705.5, 030304 developmental biology, 2. Zero hunger, Genetics, 0303 health sciences, GEM, biology, Applied Mathematics, 030302 biochemistry & molecular biology, Fungal genetics, Epistasis, Genetic, biology.organism_classification, Yeast, Computer Science Applications, lcsh:Biology (General), Modeling and Simulation, Metabolic, Reconstruction, Metabolic Networks and Pathways, Software, Research Article, Model
Abstract

Background Efforts to improve the computational reconstruction of the Saccharomyces cerevisiae biochemical reaction network and to refine the stoichiometrically constrained metabolic models that can be derived from such a reconstruction have continued since the first stoichiometrically constrained yeast genome scale metabolic model was published in 2003. Continuing this ongoing process, we have constructed an update to the Yeast Consensus Reconstruction, Yeast 5. The Yeast Consensus Reconstruction is a product of efforts to forge a community-based reconstruction emphasizing standards compliance and biochemical accuracy via evidence-based selection of reactions. It draws upon models published by a variety of independent research groups as well as information obtained from biochemical databases and primary literature. Results Yeast 5 refines the biochemical reactions included in the reconstruction, particularly reactions involved in sphingolipid metabolism; updates gene-reaction annotations; and emphasizes the distinction between reconstruction and stoichiometrically constrained model. Although it was not a primary goal, this update also improves the accuracy of model prediction of viability and auxotrophy phenotypes and increases the number of epistatic interactions. This update maintains an emphasis on standards compliance, unambiguous metabolite naming, and computer-readable annotations available through a structured document format. Additionally, we have developed MATLAB scripts to evaluate the model’s predictive accuracy and to demonstrate basic model applications such as simulating aerobic and anaerobic growth. These scripts, which provide an independent tool for evaluating the performance of various stoichiometrically constrained yeast metabolic models using flux balance analysis, are included as Additional files 1, 2 and 3. Additional file 1 Function testYeastModel.m.m. Click here for file Additional file 2 Function modelToReconstruction.m. Click here for file Additional file 3 Function fluxDistribution.m. Click here for file Conclusions Yeast 5 expands and refines the computational reconstruction of yeast metabolism and improves the predictive accuracy of a stoichiometrically constrained yeast metabolic model. It differs from previous reconstructions and models by emphasizing the distinction between the yeast metabolic reconstruction and the stoichiometrically constrained model, and makes both available as Additional file 4 and Additional file 5 and at http://yeast.sf.net/ as separate systems biology markup language (SBML) files. Through this separation, we intend to make the modeling process more accessible, explicit, transparent, and reproducible. Additional file 4 Yeast metabolic network reconstruction. Click here for file Additional file 5 Yeast metabolic network model. Click here for file

Published
2012

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