10. Appendix: References

1

Eamonn Keogh and Abdullah Mueen. Curse of dimensionality. In Encyclopedia of Machine Learning, pages 257–258. Springer, 2011.

2

Dagmar Waltemath, Richard Adams, Daniel A Beard, Frank T Bergmann, Upinder S Bhalla, Randall Britten, Vijayalakshmi Chelliah, Michael T Cooling, Jonathan Cooper, Edmund J Crampin, and others. Minimum Information About a Simulation Experiment (MIASE). PLoS Comput Biol, 7(4):e1001122, 2011.

3

Kouichi Takahashi, Kazunari Kaizu, Bin Hu, and Masaru Tomita. A multi-algorithm, multi-timescale method for cell simulation. Bioinformatics, 20(4):538–546, 2004.

4

E L Haseltine and F H Arnold. Synthetic gene circuits: design with directed evolution. Annu Rev Biophys Biomol Struct, 36:1–19, 2007.

5

R E Cobb, T Si, and H Zhao. Directed evolution: an evolving and enabling synthetic biology tool. Curr Opin Chem Biol, 16(3-4):285–291, 2012.

6

Dean C Karnopp, Donald L Margolis, and Ronald C Rosenberg. System dynamics: modeling, simulation, and control of mechatronic systems. John Wiley & Sons, 2012.

7

Joe A Clarke. Energy simulation in building design. Routledge, 2001.

8

Ennio Cascetta. Transportation systems analysis: models and applications. Volume 29. Springer Science & Business Media, 2009.

9

J R Karr, K Takahashi, and A Funahashi. The principles of whole-cell modeling. Curr Opin Microbiol, 27:18–24, 2015.

10

D N Macklin, N A Ruggero, and M W Covert. The future of whole-cell modeling. Curr Opin Biotechnol, 28:111–115, 2014.

11

M Tomita. Whole-cell simulation: a grand challenge of the 21st century. Trends Biotechnol, 19(6):205–210, 2001.

12

Javier Carrera and Markus W Covert. Why build whole-cell models? Trends Cell Biol, 25(12):719–722, 2015.

13

Jeffrey D Orth, Ines Thiele, and Bernhard Ø Palsson. What is flux balance analysis? Nat Biotechnol, 28(3):245–248, 2010.

14

Aarash Bordbar, Jonathan M Monk, Zachary A King, and Bernhard O Palsson. Constraint-based models predict metabolic and associated cellular functions. Nat Rev Genet, 15(2):107, 2014.

15

Adam M Feist and Bernhard Ø Palsson. The growing scope of applications of genome-scale metabolic reconstructions: the case of E. coli. Nat Biotechnol, 26(6):659, 2008.

16

B Szigeti, Y D Roth, J A P Sekar, A P Goldberg, S C Pochiraju, and J R Karr. A blueprint for human whole-cell modeling. Curr Opin Syst Biol, In submission.

17

Masaru Tomita, Kenta Hashimoto, Koichi Takahashi, Thomas Simon Shimizu, Yuri Matsuzaki, Fumihiko Miyoshi, Kanako Saito, Sakura Tanida, Katsuyuki Yugi, J Craig Venter, and others. E-CELL: software environment for whole-cell simulation. Bioinformatics, 15(1):72–84, 1999.

18

Markus W Covert, Eric M Knight, Jennifer L Reed, Markus J Herrgard, and Bernhard O Palsson. Integrating high-throughput and computational data elucidates bacterial networks. Nature, 429(6987):92, 2004.

19

Sriram Chandrasekaran and Nathan D Price. Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis. Proc Natl Acad Sci U S A, 107(41):17845–17850, 2010.

20

Markus W Covert, Nan Xiao, Tiffany J Chen, and Jonathan R Karr. Integrating metabolic, transcriptional regulatory and signal transduction models in Escherichia coli. Bioinformatics, 24(18):2044–2050, 2008.

21

Jong Min Lee, Erwin P Gianchandani, James A Eddy, and Jason A Papin. Dynamic analysis of integrated signaling, metabolic, and regulatory networks. PLoS Comput Biol, 4(5):e1000086, 2008.

22

Javier Carrera, Raissa Estrela, Jing Luo, Navneet Rai, Athanasios Tsoukalas, and Ilias Tagkopoulos. An integrative, multi-scale, genome-wide model reveals the phenotypic landscape of Escherichia coli. Mol Syst Biol, 10(7):735, 2014.

23

Ines Thiele, Neema Jamshidi, Ronan MT Fleming, and Bernhard Ø Palsson. Genome-scale reconstruction of Escherichia coli’s transcriptional and translational machinery: a knowledge base, its mathematical formulation, and its functional characterization. PLoS Comput Biol, 5(3):e1000312, 2009.

24

Emanuel Gonçalves, Joachim Bucher, Anke Ryll, Jens Niklas, Klaus Mauch, Steffen Klamt, Miguel Rocha, and Julio Saez-Rodriguez. Bridging the layers: towards integration of signal transduction, regulation and metabolism into mathematical models. Mol Biosyst, 9(7):1576–1583, 2013.

25

JC Atlas, EV Nikolaev, ST Browning, and ML Shuler. Incorporating genome-wide DNA sequence information into a dynamic whole-cell model of Escherichia coli: application to DNA replication. IET Syst Biol, 2(5):369–382, 2008.

26

Elijah Roberts, John E Stone, Leonardo Sepúlveda, Wen-Mei W Hwu, and Zaida Luthey-Schulten. Long time-scale simulations of in vivo diffusion using GPU hardware. In IEEE Intl Symposium Parallel Distributed Processing, 1–8. IEEE, 2009.

27

Jonathan R Karr, Jayodita C Sanghvi, Derek N Macklin, Miriam V Gutschow, Jared M Jacobs, Benjamin Bolival, Nacyra Assad-Garcia, John I Glass, and Markus W Covert. A whole-cell computational model predicts phenotype from genotype. Cell, 150(2):389–401, 2012.

28

Aarash Bordbar, Douglas McCloskey, Daniel C Zielinski, Nikolaus Sonnenschein, Neema Jamshidi, and Bernhard O Palsson. Personalized whole-cell kinetic models of metabolism for discovery in genomics and pharmacodynamics. Cell Syst, 1(4):283–292, 2015.

29

Daniel G Gibson, John I Glass, Carole Lartigue, Vladimir N Noskov, Ray-Yuan Chuang, Mikkel A Algire, Gwynedd A Benders, Michael G Montague, Li Ma, Monzia M Moodie, and others. Creation of a bacterial cell controlled by a chemically synthesized genome. Science, 329(5987):52–56, 2010.

30

J R Karr, J C Sanghvi, D N Macklin, A Arora, and M W Covert. WholeCellKB: model organism databases for comprehensive whole-cell models. Nucleic Acids Res, 41(Database issue):D787–D792, 2013.

31

Nikolay Kolesnikov, Emma Hastings, Maria Keays, Olga Melnichuk, Y Amy Tang, Eleanor Williams, Miroslaw Dylag, Natalja Kurbatova, Marco Brandizi, Tony Burdett, and others. ArrayExpress update–simplifying data submissions. Nucleic Acids Res, 43(D1):D1113–D1116, 2015.

32

Emily Clough and Tanya Barrett. The Gene Expression Omnibus database. Statistical Genomics: Methods and Protocols, pages 93–110, 2016.

33

Mingcong Wang, Christina J Herrmann, Milan Simonovic, Damian Szklarczyk, and Christian Mering. Version 4.0 of PaxDb: protein abundance data, integrated across model organisms, tissues, and cell-lines. Proteomics, 15(18):3163–3168, 2015.

34

Ulrike Wittig, Renate Kania, Martin Golebiewski, Maja Rey, Lei Shi, Lenneke Jong, Enkhjargal Algaa, Andreas Weidemann, Heidrun Sauer-Danzwith, Saqib Mir, and others. SABIO-RK–database for biochemical reaction kinetics. Nucleic Acids Res, 40(D1):D790–D796, 2012.

35

Iain C Macaulay, Chris P Ponting, and Thierry Voet. Single-cell multiomics: multiple measurements from single cells. Trends Genet, 33(2):155–168, 2017.

36

AF Maarten Altelaar, Javier Munoz, and Albert JR Heck. Next-generation proteomics: towards an integrative view of proteome dynamics. Nat Rev Genet, 14(1):35, 2013.

37

Tobias Fuhrer and Nicola Zamboni. High-throughput discovery metabolomics. Curr Opinion Biotechnol, 31:73–78, 2015.

38

Peter W Laird. Principles and challenges of genome-wide DNA methylation analysis. Nat Rev Genetics, 11(3):191, 2010.

39

Job Dekker, Marc A Marti-Renom, and Leonid A Mirny. Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data. Nat Rev Genet, 14(6):390, 2013.

40

Peter J Park. ChIP-seq: advantages and challenges of a maturing technology. Nat Rev Genet, 10(10):669, 2009.

41

Antoine-Emmanuel Saliba, Alexander J Westermann, Stanislaw A Gorski, and Jörg Vogel. Single-cell RNA-seq: advances and future challenges. Nucleic Acids Res, 42(14):8845–8860, 2014.

42

Aleksandra A Kolodziejczyk, Jong Kyoung Kim, Valentine Svensson, John C Marioni, and Sarah A Teichmann. The technology and biology of single-cell RNA sequencing. Mol Cell, 58(4):610–620, 2015.

43

Je Hyuk Lee, Evan R Daugharthy, Jonathan Scheiman, Reza Kalhor, Joyce L Yang, Thomas C Ferrante, Richard Terry, Sauveur SF Jeanty, Chao Li, Ryoji Amamoto, and others. Highly multiplexed subcellular RNA sequencing in situ. Science, 343(6177):1360–1363, 2014.

44

Katja Dettmer, Pavel A Aronov, and Bruce D Hammock. Mass spectrometry-based metabolomics. Mass Spectrom Rev, 26(1):51–78, 2007.

45

Marcus Bantscheff, Simone Lemeer, Mikhail M Savitski, and Bernhard Kuster. Quantitative mass spectrometry in proteomics: critical review update from 2007 to the present. Anal Bioanal Chem, 404(4):939–965, 2012.

46

Sean C Bendall, Garry P Nolan, Mario Roederer, and Pratip K Chattopadhyay. A deep profiler’s guide to cytometry. Trends Immunol, 33(7):323–332, 2012.

47

Tanvir Sajed, Ana Marcu, Miguel Ramirez, Allison Pon, An Chi Guo, Craig Knox, Michael Wilson, Jason R Grant, Yannick Djoumbou, and David S Wishart. ECMDB 2.0: a richer resource for understanding the biochemistry of E. coli. Nucleic Acids Res, 44(D1):D495–D501, 2016.

48

Miguel Ramirez-Gaona, Ana Marcu, Allison Pon, An Chi Guo, Tanvir Sajed, Noah A Wishart, Naama Karu, Yannick Djoumbou Feunang, David Arndt, and David S Wishart. YMDB 2.0: a significantly expanded version of the yeast metabolome database. Nucleic Acids Res, 45(D1):D440–D445, 2017.

49

Zachary A King, Justin Lu, Andreas Dräger, Philip Miller, Stephen Federowicz, Joshua A Lerman, Ali Ebrahim, Bernhard O Palsson, and Nathan E Lewis. BiGG models: a platform for integrating, standardizing and sharing genome-scale models. Nucleic Acids Res, 44(D1):D515–D522, 2015.

50

figshare LLP. Figshare. https://figshare.com, 2017.

51

SimTK Team. Simtk. https://simtk.org, 2017.

52

CERN. Zenodo. https://zenodo.org, 2017.

53

Damian Smedley, Syed Haider, Steffen Durinck, Luca Pandini, Paolo Provero, James Allen, Olivier Arnaiz, Mohammad Hamza Awedh, Richard Baldock, Giulia Barbiera, and others. The BioMart community portal: an innovative alternative to large, centralized data repositories. Nucleic Acids Res, 43(W1):W589–W598, 2015.

54

Thomas Cokelaer, Dennis Pultz, Lea M Harder, Jordi Serra-Musach, and Julio Saez-Rodriguez. BioServices: a common Python package to access biological web services programmatically. Bioinformatics, 29(24):3241–3242, 2013.

55

Alex Kalderimis, Rachel Lyne, Daniela Butano, Sergio Contrino, Mike Lyne, Joshua Heimbach, Fengyuan Hu, Richard Smith, Radek Štěpán, Julie Sullivan, and others. InterMine: extensive web services for modern biology. Nucleic Acids Res, 42(W1):W468–W472, 2014.

56

Minoru Kanehisa, Miho Furumichi, Mao Tanabe, Yoko Sato, and Kanae Morishima. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res, 45(D1):D353–D361, 2017.

57

Ethan G Cerami, Benjamin E Gross, Emek Demir, Igor Rodchenkov, Özgün Babur, Nadia Anwar, Nikolaus Schultz, Gary D Bader, and Chris Sander. Pathway Commons, a web resource for biological pathway data. Nucleic Acids Res, 39(suppl_1):D685–D690, 2010.

58

UniProt Consortium and others. UniProt: the universal protein knowledgebase. Nucleic Acids Res, 45(D1):D158–D169, 2017.

59

Ron Caspi, Richard Billington, Luciana Ferrer, Hartmut Foerster, Carol A Fulcher, Ingrid M Keseler, Anamika Kothari, Markus Krummenacker, Mario Latendresse, Lukas A Mueller, and others. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res, 44(D1):D471–D480, 2016.

60

Ingrid M Keseler, Amanda Mackie, Alberto Santos-Zavaleta, Richard Billington, César Bonavides-Martínez, Ron Caspi, Carol Fulcher, Socorro Gama-Castro, Anamika Kothari, Markus Krummenacker, and others. The EcoCyc database: reflecting new knowledge about Escherichia coli K-12. Nucleic Acids Res, 45(D1):D543–D550, 2017.

61

Mario Latendresse, Markus Krummenacker, Miles Trupp, and Peter D Karp. Construction and completion of flux balance models from pathway databases. Bioinformatics, 28(3):388–396, 2012.

62

Michael Y Galperin, Xosé M Fernández-Suárez, and Daniel J Rigden. The 24th annual Nucleic Acids Research database issue: a look back and upcoming changes. Nucleic Acids Res, 45(D1):D1–D11, 2017.

63

Heinz Pampel, Paul Vierkant, Frank Scholze, Roland Bertelmann, Maxi Kindling, Jens Klump, Hans-Jürgen Goebelbecker, Jens Gundlach, Peter Schirmbacher, and Uwe Dierolf. Making research data repositories visible: the re3data.org Registry. PloS One, 8(11):e78080, 2013.

64

Paul R Cohen. DARPA’s Big Mechanism program. Phys Biol, 12(4):045008, 2015.

65

Nancy Y Yu, James R Wagner, Matthew R Laird, Gabor Melli, Sébastien Rey, Raymond Lo, Phuong Dao, S Cenk Sahinalp, Martin Ester, Leonard J Foster, and others. PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinformatics, 26(13):1608–1615, 2010.

66

Vikram Agarwal, George W Bell, Jin-Wu Nam, and David P Bartel. Predicting effective microRNA target sites in mammalian mRNAs. eLife, 4:e05005, 2015.

67

Fedor A Kolpakov, Nikita I Tolstykh, Tagir F Valeev, Ilya N Kiselev, Elena O Kutumova, Anna Ryabova, Ivan S Yevshin, and Alexander E Kel. BioUML–open source plug-in based platform for bioinformatics: invitation to collaboration. In Moscow Conference on Computational Molecular Biology, 172–173. Department of Bioengineering and Bioinformatics of MV Lomonosov Moscow State University, 2011.

68

Yukiko Matsuoka, Akira Funahashi, Samik Ghosh, and Hiroaki Kitano. Modeling and simulation using CellDesigner. Transcription Factor Regulatory Networks: Methods and Protocols, pages 121–145, 2014.

69

Frank T Bergmann, Stefan Hoops, Brian Klahn, Ursula Kummer, Pedro Mendes, Jürgen Pahle, and Sven Sahle. COPASI and its applications in biotechnology. J Biotechnol, 261:215–220, 2017.

70

Herbert M Sauro, Michael Hucka, Andrew Finney, Cameron Wellock, Hamid Bolouri, John Doyle, and Hiroaki Kitano. Next generation simulation tools: the Systems Biology Workbench and BioSPICE integration. Omics, 7(4):355–372, 2003.

71

Diana C Resasco, Fei Gao, Frank Morgan, Igor L Novak, James C Schaff, and Boris M Slepchenko. Virtual Cell: computational tools for modeling in cell biology. Wiley Interdiscip Rev Syst Biol Med, 4(2):129–140, 2012.

72

Adam M Smith, Wen Xu, Yao Sun, James R Faeder, and G Elisabeta Marai. RuleBender: integrated modeling, simulation and visualization for rule-based intracellular biochemistry. BMC Bioinformatics, 13(8):S3, 2012.

73

Ali Ebrahim, Joshua A Lerman, Bernhard O Palsson, and Daniel R Hyduke. COBRApy: constraints-based reconstruction and analysis for Python. BMC Syst Biol, 7(1):74, 2013.

74

Joost Boele, Brett G Olivier, and Bas Teusink. FAME, the flux analysis and modeling environment. BMC Syst Biol, 6(1):8, 2012.

75

Rasmus Agren, Liming Liu, Saeed Shoaie, Wanwipa Vongsangnak, Intawat Nookaew, and Jens Nielsen. The RAVEN toolbox and its use for generating a genome-scale metabolic model for Penicillium chrysogenum. PLoS Comput Biol, 9(3):e1002980, 2013.

76

Katherine Wolstencroft, Stuart Owen, Olga Krebs, Quyen Nguyen, Natalie J Stanford, Martin Golebiewski, Andreas Weidemann, Meik Bittkowski, Lihua An, David Shockley, and others. SEEK: a systems biology data and model management platform. BMC Syst Biol, 9(1):33, 2015.

77

T Helikar, B Kowal, and JA Rogers. A cell simulator platform: the Cell Collective. Clin Pharmacol Ther, 93(5):393–395, 2013.

78

Franco du Preez. JWS Online. Encyclopedia of Systems Biology, pages 1063–1066, 2013.

79

Carlos F Lopez, Jeremy L Muhlich, John A Bachman, and Peter K Sorger. Programming biological models in Python using PySB. Mol Syst Biol, 9(1):646, 2013.

80

Falko Krause, Jannis Uhlendorf, Timo Lubitz, Marvin Schulz, Edda Klipp, and Wolfram Liebermeister. Annotation and merging of SBML models with semanticSBML. Bioinformatics, 26(3):421–422, 2009.

81

Maxwell L Neal, Michael T Cooling, Lucian P Smith, Christopher T Thompson, Herbert M Sauro, Brian E Carlson, Daniel L Cook, and John H Gennari. A reappraisal of how to build modular, reusable models of biological systems. PLoS Comput Biol, 10(10):e1003849, 2014.

82

Paul Kirk, Thomas Thorne, and Michael PH Stumpf. Model selection in systems and synthetic biology. Curr Opin Biotechnol, 24(4):767–774, 2013.

83

Juliane Liepe, Paul Kirk, Sarah Filippi, Tina Toni, Chris P Barnes, and Michael PH Stumpf. A framework for parameter estimation and model selection from experimental data in systems biology using approximate bayesian computation. Nat Protoc, 9(2):439, 2014.

84

Tina Toni, David Welch, Natalja Strelkowa, Andreas Ipsen, and Michael PH Stumpf. Approximate bayesian computation scheme for parameter inference and model selection in dynamical systems. J R Soc Interface, 6(31):187–202, 2009.

85

Max Flöttmann, Jörg Schaber, Stephan Hoops, Edda Klipp, and Pedro Mendes. ModelMage: a tool for automatic model generation, selection and management. Genome Inform, 20:52–63, 2008.

86

Rob Johnson, Paul Kirk, and Michael PH Stumpf. SYSBIONS: nested sampling for systems biology. Bioinformatics, 31(4):604–605, 2014.

87

Jeffrey D Orth and Bernhard Ø Palsson. Systematizing the generation of missing metabolic knowledge. Biotechnol Bioeng, 107(3):403–412, 2010.

88

Edik M Blais, Arvind K Chavali, and Jason A Papin. Linking genome-scale metabolic modeling and genome annotation. Methods Mol Biol, pages 61–83, 2013.

89

Vinay Satish Kumar, Madhukar S Dasika, and Costas D Maranas. Optimization based automated curation of metabolic reconstructions. BMC Bioinformatics, 8(1):212, 2007.

90

Markus J Herrgård, Stephen S Fong, and Bernhard Ø Palsson. Identification of genome-scale metabolic network models using experimentally measured flux profiles. PLoS Comput Biol, 2(7):e72, 2006.

91

Jennifer L Reed, Trina R Patel, Keri H Chen, Andrew R Joyce, Margaret K Applebee, Christopher D Herring, Olivia T Bui, Eric M Knight, Stephen S Fong, and Bernhard O Palsson. Systems approach to refining genome annotation. Proc Natl Acad Sci U S A, 103(46):17480–17484, 2006.

92

Mario Latendresse. Efficiently gap-filling reaction networks. BMC Bioinformatics, 15(1):225, 2014.

93

Vinay Satish Kumar and Costas D Maranas. GrowMatch: an automated method for reconciling in silico/in vivo growth predictions. PLoS Comput Biol, 5(3):e1000308, 2009.

94

Peter Kharchenko, Lifeng Chen, Yoav Freund, Dennis Vitkup, and George M Church. Identifying metabolic enzymes with multiple types of association evidence. BMC Bioinformatics, 7(1):177, 2006.

95

Zhaleh Hosseini and Sayed-Amir Marashi. Discovering missing reactions of metabolic networks by using gene co-expression data. Sci Rep, 2017.

96

Matthew N Benedict, Michael B Mundy, Christopher S Henry, Nicholas Chia, and Nathan D Price. Likelihood-based gene annotations for gap filling and quality assessment in genome-scale metabolic models. PLoS Comput Biol, 10(10):e1003882, 2014.

97

Edward Vitkin and Tomer Shlomi. MIRAGE: a functional genomics-based approach for metabolic network model reconstruction and its application to cyanobacteria networks. Genome Biol, 13(11):R111, 2012.

98

Michelle L Green and Peter D Karp. A Bayesian method for identifying missing enzymes in predicted metabolic pathway databases. BMC Bioinformatics, 5(1):76, 2004.

99

Andrei Osterman. A hidden metabolic pathway exposed. Proc Natl Acad Sci U S A, 103(15):5637–5638, 2006.

100

Alan Garny, David P Nickerson, Jonathan Cooper, Rodrigo Weber dos Santos, Andrew K Miller, Steve McKeever, Poul MF Nielsen, and Peter J Hunter. CellML and associated tools and techniques. Philos Trans A Math Phys Eng Sci, 366(1878):3017–3043, 2008.

101

Michael Hucka, Andrew Finney, Herbert M Sauro, Hamid Bolouri, John C Doyle, Hiroaki Kitano, Adam P Arkin, Benjamin J Bornstein, Dennis Bray, Athel Cornish-Bowden, and others. The Systems Biology Markup Language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics, 19(4):524–531, 2003.

102

Leonard A Harris, Justin S Hogg, Jose-Juan Tapia, John AP Sekar, Sanjana Gupta, Ilya Korsunsky, Arshi Arora, Dipak Barua, Robert P Sheehan, and James R Faeder. BioNetGen 2.2: advances in rule-based modeling. Bioinformatics, 32(21):3366–3368, 2016.

103

Vincent Danos and Cosimo Laneve. Formal molecular biology. Theor Comput Sci, 325(1):69–110, 2004.

104

Carsten Maus, Stefan Rybacki, and Adelinde M Uhrmacher. Rule-based multi-level modeling of cell biological systems. BMC Syst Biol, 5(1):166, 2011.

105

Vijayalakshmi Chelliah, Nick Juty, Ishan Ajmera, Raza Ali, Marine Dumousseau, Mihai Glont, Michael Hucka, Gaël Jalowicki, Sarah Keating, Vincent Knight-Schrijver, and others. BioModels: ten-year anniversary. Nucleic Acids Res, 43(D1):D542–D548, 2015.

106

Dagmar Waltemath, Jonathan R Karr, Frank T Bergmann, Vijayalakshmi Chelliah, Michael Hucka, Marcus Krantz, Wolfram Liebermeister, Pedro Mendes, Chris J Myers, Pinar Pir, and others. Toward community standards and software for whole-cell modeling. IEEE Trans Biomed Eng, 63(10):2007–2014, 2016.

107

Michael W Sneddon, James R Faeder, and Thierry Emonet. Efficient modeling, simulation and coarse-graining of biological complexity with NFsim. Nat Methods, 8(2):177–183, 2011.

108

Daniel T Gillespie. Exact stochastic simulation of coupled chemical reactions. J Phys Chem, 81(25):2340–2361, 1977.

109

Vo Hong Thanh, Roberto Zunino, and Corrado Priami. Efficient constant-time complexity algorithm for stochastic simulation of large reaction networks. IEEE/ACM Trans Comput Biol Bioinform, 14(3):657–667, 2017.

110

Dagmar Waltemath, Richard Adams, Frank T Bergmann, Michael Hucka, Fedor Kolpakov, Andrew K Miller, Ion I Moraru, David Nickerson, Sven Sahle, Jacky L Snoep, and others. Reproducible computational biology experiments with SED-ML-the Simulation Experiment Description Markup Language. BMC Syst Biol, 5(1):198, 2011.

111

Roland Ewald and Adelinde M Uhrmacher. SESSL: a domain-specific language for simulation experiments. ACM Trans Modeling Comput Simul, 24(2):11, 2014.

112

Pawan K Dhar, Kouichi Takahashi, Yoichi Nakayama, and Masaru Tomita. E-Cell: computer simulation of the cell. Rev Cell Biol Mol Med, 2006.

113

Chris J Myers, Nathan Barker, Kevin Jones, Hiroyuki Kuwahara, Curtis Madsen, and Nam-Phuong D Nguyen. iBioSim: a tool for the analysis and design of genetic circuits. Bioinformatics, 25(21):2848–2849, 2009.

114

Endre T Somogyi, Jean-Marie Bouteiller, James A Glazier, Matthias König, J Kyle Medley, Maciej H Swat, and Herbert M Sauro. libRoadRunner: a high performance SBML simulation and analysis library. Bioinformatics, 31(20):3315–3321, 2015.

115

Marco S Nobile, Daniela Besozzi, Paolo Cazzaniga, Giancarlo Mauri, and Dario Pescini. cupSODA: a CUDA-powered simulator of mass-action kinetics. In International Conference on Parallel Computing Technologies, 344–357. Springer, 2013.

116

Marco S Nobile, Paolo Cazzaniga, Daniela Besozzi, Dario Pescini, and Giancarlo Mauri. cuTauLeaping: a GPU-powered tau-leaping stochastic simulator for massive parallel analyses of biological systems. PLoS One, 9(3):e91963, 2014.

117

Christopher D Carothers, David Bauer, and Shawn Pearce. ROSS: a high-performance, low-memory, modular Time Warp system. J Parallel Distrib Comput, 62(11):1648–1669, 2002.

118

Julio R Banga and Eva Balsa-Canto. Parameter estimation and optimal experimental design. Essays in biochemistry, 45:195–210, 2008.

119

Alexander IJ Forrester and Andy J Keane. Recent advances in surrogate-based optimization. Progress Aerospace Sci, 45(1):50–79, 2009.

120

Chen Wang, Qingyun Duan, Wei Gong, Aizhong Ye, Zhenhua Di, and Chiyuan Miao. An evaluation of adaptive surrogate modeling based optimization with two benchmark problems. Environmental Modelling Softw, 60:167–179, 2014.

121

Jason P Halloran and Ahmet Erdemir. Adaptive surrogate modeling for expedited estimation of nonlinear tissue properties through inverse finite element analysis. Annal Biomed Eng, 39(9):2388–2397, 2011.

122

Donald R Jones. A taxonomy of global optimization methods based on response surfaces. J Global Optim, 21(4):345–383, 2001.

123

Yew S Ong, Prasanth B Nair, and Andrew J Keane. Evolutionary optimization of computationally expensive problems via surrogate modeling. AIAA journal, 41(4):687–696, 2003.

124

Saman Razavi, Bryan A Tolson, and Donald H Burn. Numerical assessment of metamodelling strategies in computationally intensive optimization. Environmental Modelling & Software, 34:67–86, 2012.

125

Nestor V Queipo, Salvador Pintos, Néstor Rincón, Nemrod Contreras, and Juan Colmenares. Surrogate modeling-based optimization for the integration of static and dynamic data into a reservoir description. Journal of Petroleum Science and Engineering, 35(3):167–181, 2002.

126

Liviu Panait and Sean Luke. Cooperative multi-agent learning: the state of the art. Autonomous Agents Multi-agent Syst, 11(3):387–434, 2005.

127

Daniel P Palomar and Yonina C Eldar. Convex optimization in signal processing and communications. Cambridge university press, 2010.

128

Robin L Raffard, Claire J Tomlin, and Stephen P Boyd. Distributed optimization for cooperative agents: application to formation flight. In Decision and Control, 2004. CDC. 43rd IEEE Conference on, volume 3, 2453–2459. IEEE, 2004.

129

Michael Rabbat and Robert Nowak. Distributed optimization in sensor networks. In Proceedings of the 3rd international symposium on Information processing in sensor networks, 20–27. ACM, 2004.

130

Brian Y Chen, Viacheslav Y Fofanov, Drew H Bryant, Bradley D Dodson, David M Kristensen, Andreas M Lisewski, Marek Kimmel, Olivier Lichtarge, and Lydia E Kavraki. Geometric sieving: automated distributed optimization of 3d motifs for protein function prediction. Lecture Notes in Computer Science, 3909:500–515, 2006.

131

Louis B Rall. Automatic differentiation: Techniques and applications. Springer, 1981.

132

Rohit Ramachandran and Paul I Barton. Effective parameter estimation within a multi-dimensional population balance model framework. Chemical Engineering Science, 65(16):4884–4893, 2010.

133

H Martin Bücker, George Corliss, Paul Hovland, Uwe Naumann, and Boyana Norris. Automatic differentiation: applications, theory, and implementations. Volume 50. Springer Science & Business Media, 2006.

134

Jan Schumann-Bischoff, Stefan Luther, and Ulrich Parlitz. Nonlinear system identification employing automatic differentiation. Communications in Nonlinear Science and Numerical Simulation, 18(10):2733–2742, 2013.

135

Oana-Teodora Chis, Julio R Banga, and Eva Balsa-Canto. Structural identifiability of systems biology models: a critical comparison of methods. PloS One, 6(11):e27755, 2011.

136

Maksat Ashyraliyev, Yves Fomekong-Nanfack, Jaap A Kaandorp, and Joke G Blom. Systems biology: parameter estimation for biochemical models. FEBS J, 276(4):886–902, 2009.

137

I-Chun Chou and Eberhard O Voit. Recent developments in parameter estimation and structure identification of biochemical and genomic systems. Math Biosci, 219(2):57–83, 2009.

138

Jianyong Sun, Jonathan M Garibaldi, and Charlie Hodgman. Parameter estimation using metaheuristics in systems biology: a comprehensive review. IEEE/ACM Trans Comput Biol Bioinfor, 9(1):185–202, 2012.

139

Carmen G Moles, Pedro Mendes, and Julio R Banga. Parameter estimation in biochemical pathways: a comparison of global optimization methods. Genome Res, 13(11):2467–2474, 2003.

140

Giuseppina Bellu, Maria Pia Saccomani, Stefania Audoly, and Leontina D’Angiò. DAISY: a new software tool to test global identifiability of biological and physiological systems. Comput Meth Program Biomed, 88(1):52–61, 2007.

141

David R Penas, Patricia González, Jose A Egea, Ramón Doallo, and Julio R Banga. Parameter estimation in large-scale systems biology models: a parallel and self-adaptive cooperative strategy. BMC Bioinformatics, 18(1):52, 2017.

142

Richard Adams, Allan Clark, Azusa Yamaguchi, Neil Hanlon, Nikos Tsorman, Shakir Ali, Galina Lebedeva, Alexey Goltsov, Anatoly Sorokin, Ozgur E Akman, and others. SBSI: an extensible distributed software infrastructure for parameter estimation in systems biology. Bioinformatics, 29(5):664–665, 2013.

143

Edmund M Clarke, James R Faeder, Christopher J Langmead, Leonard A Harris, Sumit Kumar Jha, and Axel Legay. Statistical model checking in BioLab: applications to the automated analysis of T-cell receptor signaling pathway. In Int Conf Comput Meth Syst Biol, 231–250. Springer, 2008.

144

Marta Kwiatkowska, Gethin Norman, and David Parker. PRISM 4.0: verification of probabilistic real-time systems. In Comput Aided Verification, 585–591. Springer, 2011.

145

Christian Lieven, Moritz Beber, and Nikolaus Sonnenschein. Memote – a testing suite for constraint-based metabolic models. http://easychair.org/smart-program/ICSB2017/2017-08-08.html#talk:51929, 2017.

146

Cyrus Omar, Jonathan Aldrich, and Richard C Gerkin. Collaborative infrastructure for test-driven scientific model validation. In Companion Proceedings of the 36th International Conference on Software Engineering, 524–527. ACM, 2014.

147

Circle Internet Services Inc. Circleci. https://circleci.com, 2017.

148

Kohsuke Kawaguchi. Jenkins. https://jenkins.io, 2017.

149

Software Freedom Conservancy. Git. https://git-scm.com/, 2017.

150

Mike Folk, Gerd Heber, Quincey Koziol, Elena Pourmal, and Dana Robinson. An overview of the HDF5 technology suite and its applications. In Proc EDBT/ICDT 2011 Workshop Array Databases, 36–47. ACM, 2011.

151

Shabana Vohra, Benjamin A Hall, Daniel A Holdbrook, Syma Khalid, and Philip C Biggin. Bookshelf: a simple curation system for the storage of biomolecular simulation data. Database, 2010:baq033, 2010.

152

Giacomo Finocchiaro, Ting Wang, Rene Hoffmann, Aitor Gonzalez, and Rebecca C Wade. DSMM: a database of simulated molecular motions. Nucleic Acids Res, 31(1):456–457, 2003.

153

Marc W van der Kamp, R Dustin Schaeffer, Amanda L Jonsson, Alexander D Scouras, Andrew M Simms, Rudesh D Toofanny, Noah C Benson, Peter C Anderson, Eric D Merkley, Steven Rysavy, and others. Dynameomics: a comprehensive database of protein dynamics. Structure, 18(4):423–435, 2010.

154

Tim Meyer, Marco D’Abramo, Adam Hospital, Manuel Rueda, Carles Ferrer-Costa, Alberto Pérez, Oliver Carrillo, Jordi Camps, Carles Fenollosa, Dmitry Repchevsky, and others. MoDEL (Molecular Dynamics Extended Library): a database of atomistic molecular dynamics trajectories. Structure, 18(11):1399–1409, 2010.

155

Gerard Lemson and others. Halo and galaxy formation histories from the millennium simulation: public release of a vo-oriented and sql-queryable database for studying the evolution of galaxies in the lambdacdm cosmogony. arXiv preprint astro-ph/0608019, 2006.

156

Kristin Riebe, Adrian M Partl, Harrya Enke, Jaime Forero-Romero, Stefan Gottloeber, Anatolyb Klypin, Gerardc Lemson, Franciscod Prada, Joel R Primack, Matthiasa Steinmetz, and others. The MultiDark database: release of the Bolshoi and MultiDark cosmological simulations. Astronomische Nachrichten, 334(7):691–708, 2013.

157

Katy Wolstencroft, Stuart Owen, Franco du Preez, Olga Krebs, Wolfgang Mueller, Carole Goble, and Jacky L Snoep. The SEEK: a platform for sharing data and models in systems biology. Meth Enzymol, 500:629–655, 2011.

158

J R Karr, N C Phillips, and M W Covert. WholeCellSimDB: a hybrid relational/HDF database for whole-cell model predictions. Database, 2014:bau095, 2014.

159

Zachary A King, Andreas Dräger, Ali Ebrahim, Nikolaus Sonnenschein, Nathan E Lewis, and Bernhard O Palsson. Escher: a web application for building, sharing, and embedding data-rich visualizations of biological pathways. PLoS Comput Biol, 11(8):e1004321, 2015.

160

Suzanne M Paley and Peter D Karp. The Pathway Tools cellular overview diagram and Omics Viewer. Nucleic Acids Res, 34(13):3771–3778, 2006.

161

R Lee, J R Karr, and M W Covert. WholeCellViz: data visualization for whole-cell models. BMC Bioinformatics, 14:253, 2013.

162

Jill C Sible and John J Tyson. Mathematical modeling as a tool for investigating cell cycle control networks. Methods, 41(2):238–247, 2007.

163

Albert Goldbeter. Computational approaches to cellular rhythms. Nature, 420(6912):238, 2002.

164

Andreas VM Herz, Tim Gollisch, Christian K Machens, and Dieter Jaeger. Modeling single-neuron dynamics and computations: a balance of detail and abstraction. Science, 314(5796):80–85, 2006.

165

Neil Swainston, Kieran Smallbone, Hooman Hefzi, Paul D Dobson, Judy Brewer, Michael Hanscho, Daniel C Zielinski, Kok Siong Ang, Natalie J Gardiner, Jahir M Gutierrez, and others. Recon 2.2: from reconstruction to model of human metabolism. Metabolomics, 12(7):1–7, 2016.

166

Rasmus Agren, Sergio Bordel, Adil Mardinoglu, Natapol Pornputtapong, Intawat Nookaew, and Jens Nielsen. Reconstruction of genome-scale active metabolic networks for 69 human cell types and 16 cancer types using INIT. PLoS Comput Biol, 8(5):e1002518, 2012.

167

Mathias Uhlen, Cheng Zhang, Sunjae Lee, Evelina Sjöstedt, Linn Fagerberg, Gholamreza Bidkhori, Rui Benfeitas, Muhammad Arif, Zhengtao Liu, Fredrik Edfors, and others. A pathology atlas of the human cancer transcriptome. Science, 357(6352):eaan2507, 2017.

168

Jacob J Hughey, Timothy K Lee, and Markus W Covert. Computational modeling of mammalian signaling networks. Wiley Interdiscip Rev Syst Biol Med, 2(2):194–209, 2010.

169

Mark B Gerstein, Anshul Kundaje, Manoj Hariharan, Stephen G Landt, Koon-Kiu Yan, Chao Cheng, Xinmeng Jasmine Mu, Ekta Khurana, Joel Rozowsky, Roger Alexander, and others. Architecture of the human regulatory network derived from ENCODE data. Nature, 489(7414):91, 2012.

170

Shigeru Kondo and Takashi Miura. Reaction-diffusion model as a framework for understanding biological pattern formation. Science, 329(5999):1616–1620, 2010.

171

Anja Geitmann and Joseph KE Ortega. Mechanics and modeling of plant cell growth. Trend Plant Sci, 14(9):467–478, 2009.

172

Kerwyn Casey Huang, Yigal Meir, and Ned S Wingreen. Dynamic structures in Escherichia coli: spontaneous formation of MinE rings and MinD polar zones. Proc Natl Acad Sci U S A, 100(22):12724–12728, 2003.

173

Harold P Erickson. Modeling the physics of ftsz assembly and force generation. Proc Natl Acad Sci U S A, 106(23):9238–9243, 2009.

174

Guy Karlebach and Ron Shamir. Modelling and analysis of gene regulatory networks. Nat Rev Mol Cell Biol, 9(10):770, 2008.

175

Tommy Yu, Catherine M Lloyd, David P Nickerson, Michael T Cooling, Andrew K Miller, Alan Garny, Jonna R Terkildsen, James Lawson, Randall D Britten, Peter J Hunter, and others. The Physiome Model Repository 2. Bioinformatics, 27(5):743–744, 2011.

176

Stephen Hilgartner. Constituting large-scale biology: building a regime of governance in the early years of the Human Genome Project. BioSocieties, 8(4):397–416, 2013.

177

Francis S Collins, Michael Morgan, and Aristides Patrinos. The Human Genome Project: lessons from large-scale biology. Science, 300(5617):286–290, 2003.

178

Stephen Heller, Alan McNaught, Stephen Stein, Dmitrii Tchekhovskoi, and Igor Pletnev. InChI-the worldwide chemical structure identifier standard. J Cheminform, 5(1):7, 2013.

179

J R Karr, A H Williams, J D Zucker, A Raue, B Steiert, J Timmer, C Kreutz, DREAM8 Parameter Estimation Challenge Consortium, S Wilkinson, B A Allgood, B M Bot, B R Hoff, M R Kellen, M W Covert, G A Stolovitzky, and P Meyer. Summary of the DREAM8 parameter estimation challenge: toward parameter identification for whole-cell models. PLoS Comput Biol, 2015.

180

Arvind Satyanarayan, Dominik Moritz, Kanit Wongsuphasawat, and Jeffrey Heer. Vega-lite: a grammar of interactive graphics. IEEE Trans Vis Comput Graphics, 23(1):341–350, 2017.

181

ML Shuler, S Leung, and CC Dick. A mathematical model for the growth of a single bacterial cell. Annals of the New York Academy of Sciences, 326(1):35–52, 1979.

182

Elijah Roberts. Cellular and molecular structure as a unifying framework for whole-cell modeling. Curr Opin Structural Biol, 25:86–91, 2014.

183

Michael J Hallock, John E Stone, Elijah Roberts, Corey Fry, and Zaida Luthey-Schulten. Simulation of reaction diffusion processes over biologically relevant size and time scales using multi-GPU workstations. Parallel Comput, 40(5):86–99, 2014.

184

John A Cole, Lars Kohler, Jamila Hedhli, and Zaida Luthey-Schulten. Spatially-resolved metabolic cooperativity within dense bacterial colonies. BMC Syst Biol, 9(1):15, 2015.

185

Oliver Purcell, Bonny Jain, Jonathan R Karr, Markus W Covert, and Timothy K Lu. Towards a whole-cell modeling approach for synthetic biology. Chaos, 23(2):025112, 2013.

186

Denis Kazakiewicz, Jonathan R Karr, Karol M Langner, and Dariusz Plewczynski. A combined systems and structural modeling approach repositions antibiotics for Mycoplasma genitalium. Comput Biol Chem, 59:91–97, 2015.

187

Doug Howe, Maria Costanzo, Petra Fey, Takashi Gojobori, Linda Hannick, Winston Hide, David P Hill, Renate Kania, Mary Schaeffer, Susan St Pierre, and others. Big data: the future of biocuration. Nature, 455(7209):47–50, 2008.

188

Jacky L Snoep, Frank Bruggeman, Brett G Olivier, and Hans V Westerhoff. Towards building the silicon cell: a modular approach. Biosystems, 83(2):207–216, 2006.

189

J Kyle Medley, Arthur P Goldberg, and Jonathan R Karr. Guidelines for reproducibly building and simulating systems biology models. IEEE Trans Biomed Eng, 63(10):2015–2020, 2016.

190

Arthur P Goldberg, Yin Hoon Chew, and Jonathan R Karr. Toward scalable whole-cell modeling of human cells. In Proc 2016 Annu ACM Conf SIGSIM Princip Adv Discret Simul, 259–262. ACM, 2016.

191

Ken Martin, Will Schroeder, and Bill Lorensen. Vtk: the visualization toolkit. https://www.vtk.org, 2017.

192

ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature, 489(7414):57–74, 2012.

193

James A Thomson, Joseph Itskovitz-Eldor, Sander S Shapiro, Michelle A Waknitz, Jennifer J Swiergiel, Vivienne S Marshall, and Jeffrey M Jones. Embryonic stem cell lines derived from human blastocysts. Science, 282(5391):1145–1147, 1998.

194

Peter Löser, Jacqueline Schirm, Anke Guhr, Anna M Wobus, and Andreas Kurtz. Human embryonic stem cell lines and their use in international research. Stem Cells, 28(2):240–246, 2010.

195

Dániel Varró, Gábor Bergmann, Ábel Hegedüs, Ákos Horváth, István Ráth, and Zoltán Ujhelyi. Road to a reactive and incremental model transformation platform: three generations of the VIATRA framework. Software Systems Modeling, 15(3):609–629, 2016.

196

Jonathan R Karr, Maria Lluch-Senar, Luis Serrano, and Javier Carrera. The 2016 Whole-Cell Modeling Summer School. 2017. doi:10.5281/zenodo.1004027.

197

Jonathan R Karr, Maria Lluch-Senar, Luis Serrano, and Damjana Kastelic. The 2017 Whole-Cell Modeling Summer School. 2017. doi:10.5281/zenodo.1004135.