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Systems Medicine pp 441–463Cite as

Modeling and Simulation Tools: From Systems Biology to Systems Medicine

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 1386))

Abstract

Modeling is an integral component of modern biology. In this chapter we look into the role of the model, as it pertains to Systems Medicine, and the software that is required to instantiate and run it. We do this by comparing the development, implementation, and characteristics of tools that have been developed to work with two divergent methodologies: Systems Biology and Pharmacometrics. From the Systems Biology perspective we consider the concept of “Software as a Medical Device” and what this may imply for the migration of research-oriented, simulation software into the domain of human health.

In our second perspective, we see how in practice hundreds of computational tools already accompany drug discovery and development at every stage of the process. Standardized exchange formats are required to streamline the model exchange between tools, which would minimize translation errors and reduce the required time. With the emergence, almost 15 years ago, of the SBML standard, a large part of the domain of interest is already covered and models can be shared and passed from software to software without recoding them. Until recently the last stage of the process, the pharmacometric analysis used in clinical studies carried out on subject populations, lacked such an exchange medium. We describe a new emerging exchange format in Pharmacometrics which covers the non-linear mixed effects models, the standard statistical model type used in this area. By interfacing these two formats the entire domain can be covered by complementary standards and subsequently the according tools.

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References

  1. Bruggeman FJ, Westerhoff HV (2007) The nature of systems biology. Trends Microbiol 15:45–50

    Article  CAS  PubMed  Google Scholar 

  2. Garfinkel D, Garfinkel L, Pring M et al (1970) Computer applications to biochemical kinetics. Annu Rev Biochem 39:473–498

    Article  CAS  PubMed  Google Scholar 

  3. Savinell JM, Palsson BO (1992) Network analysis of intermediary metabolism using linear optimization. I Development of mathematical formalism. J Theor Biol 154:421–445

    Article  CAS  PubMed  Google Scholar 

  4. Hardy S, Robillard PN (2004) Modeling and simulation of molecular biology systems using petri nets: modeling goals of various approaches. J Bioinform Comput Biol 2:595–613

    Article  CAS  PubMed  Google Scholar 

  5. Liepe J, Kirk P, Filippi S et al (2014) A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation. Nat Protoc 2:439–456

    Article  Google Scholar 

  6. CASyM Consortium (2014) The CASyM roadmap: implementation of systems medicine, version 1.0. https://www.casym.eu/lw_resource/datapool/_items/item_328/roadmap_1.0.pdf. Accessed 4 Dec 2004

  7. Flores M, Glusman G, Brogaard K et al (2014) P4 medicine: how systems medicine will transform the healthcare sector and society. Per Med 10:565–576

    Article  Google Scholar 

  8. Heinrich R, Rapoport SM, Rapoport TA (1977) Metabolic regulation and mathematical models. Progr Biophys Mol Biol 32:1–82

    Article  CAS  Google Scholar 

  9. Wright BE, Kelly PJ (1981) Kinetic models of metabolism in intact cells, tissues and organisms. Curr Top Cell Regul 19:103–158

    Article  CAS  PubMed  Google Scholar 

  10. Massoud TF, Hademenos GJ, Young WL et al (1998) Principles and philosophy of modeling in biomedical research. FASEB J 12:275–285

    CAS  PubMed  Google Scholar 

  11. Bakker BM et al (2010) Systems biology from micro-organisms to human metabolic diseases: the role of detailed kinetic models. Biochem Soc Trans 38:1294–1301

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  12. Orth JD et al (2010) What is flux balance analysis? Nat Biotechnol 28:245–248

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  13. Thiele I, Swainston N, Fleming RMT et al (2013) A community-driven global reconstruction of human metabolism. Nat Biotechnol 31:419–425

    Article  CAS  PubMed  Google Scholar 

  14. Hoops S, Sahle S, Gauges R et al (2006) COPASI: a COmplex PAthway SImulator. Bioinformatics 22:3067–3074

    Article  CAS  PubMed  Google Scholar 

  15. Sauro HM, Hucka M, Finney A et al (2003) Next generation simulation tools: the Systems Biology Workbench and BioSPICE integration. OMICS 7:355–372

    Article  CAS  PubMed  Google Scholar 

  16. Olivier BG, Rohwer JM, Hofmeyr J-HS (2005) Modelling cellular systems with PySCeS. Bioinformatics 21:560–561

    Article  CAS  PubMed  Google Scholar 

  17. Sauro HM, Fell DA (1991) SCAMP: a metabolic simulator and control analysis program. Mathl Comput Model 15:15–28

    Article  Google Scholar 

  18. Klamt S, Saez-Rodriguez J, Gilles ED (2007) Structural and functional analysis of cellular networks with Cell NetAnalyzer. BMC Syst Biol 1:2

    Article  PubMed Central  PubMed  Google Scholar 

  19. Oberhardt MA, Palsson BØ, Papin JA (2009) Applications of genome-scale metabolic reconstructions. Mol Syst Biol 5:320

    Article  PubMed Central  PubMed  Google Scholar 

  20. Li C, Donizelli M, Rodriguez N et al (2010) BioModels Database: an enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol 4:92

    Article  PubMed Central  PubMed  Google Scholar 

  21. Le Novère N, Bornstein B, Broicher A et al (2006) BioModels Database: a free, centralized database of curated, published, quantitative kinetic models of biochemical and cellular systems. Nucleic Acids Res 34:D689–D691

    Article  PubMed Central  PubMed  Google Scholar 

  22. Benson DA, Karsch-Mizrachi I, Lipman DJ et al (2011) GenBank. Nucleic Acids Res 39:D32–D37

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  23. Wimalaratne SM, Grenon P, Hermjakob H et al (2014) BioModels linked dataset. BMC Syst Biol 8:91

    Article  PubMed Central  PubMed  Google Scholar 

  24. Olivier BG, Rohwer JM, Hofmeyr J-HS (2002) Modelling cellular processes with Python and SciPy. Mol Biol Rep 29:249–254

    Article  CAS  PubMed  Google Scholar 

  25. Shapiro BE, Hucka M, Finney A et al (2004) MathSBML: a package for manipulating SBML-based biological models. Bioinformatics 20:2829–2831

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  26. Schellenberger J, Que R, Fleming RMT et al (2011) Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat Protoc 6:1290–1307

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  27. Olivier BG, Snoep JL (2004) Web-based kinetic modelling using JWS Online. Bioinformatics 20:2143–2144

    Article  CAS  PubMed  Google Scholar 

  28. IBM Corporation (2014) IBM ILOG CPLEX optimizer. http://www-01.ibm.com/software/commerce/optimization/cplex-optimizer. Accessed 29 Nov 2014

  29. Gurobi Optimization, Inc. (2014) Gurobi optimizer reference manual. http://www.gurobi.com. Accessed 27 Nov 2014

  30. Hucka M, Finney A (2005) Escalating model sizes and complexities call for standardized forms of representation. Mol Syst Biol 1:2005.0011

    Article  PubMed Central  PubMed  Google Scholar 

  31. Keating SM, Le Novère N (2013) Supporting SBML as a model exchange format in software applications. Methods Mol Biol 1021:201–225

    Article  PubMed  Google Scholar 

  32. Waltemath D, Bergmann FT, Chaouiya C et al (2014) Meeting report from the fourth meeting of the Computational Modeling in Biology Network (COMBINE). Stand Genomic Sci 9:3

    Article  Google Scholar 

  33. Miller AK, Marsh J, Reeve A et al (2010) An overview of the CellML API and its implementation. BMC Bioinformatics 11:178

    Article  PubMed Central  PubMed  Google Scholar 

  34. Galdzicki M, Clancy KP, Oberortner E et al (2014) The Synthetic Biology Open Language (SBOL) provides a community standard for communicating designs in synthetic biology. Nat Biotechnol 32:545–550

    Article  CAS  PubMed  Google Scholar 

  35. Le Novère N, Finney A, Hucka M et al (2005) Minimum information requested in the annotation of biochemical models (MIRIAM). Nat Biotechnol 23:1509–1515

    Article  PubMed  Google Scholar 

  36. Waltemath D, Adams R, Beard DA et al (2011) Minimum Information About a Simulation Experiment (MIASE). PLoS Comput Biol 7:e1001122

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  37. Hucka M, Finney A, Sauro HM et al (2003) The Systems Biology Markup Language (SBML): A medium for representation and exchange of biochemical network models. Bioinformatics 9:524–531

    Article  Google Scholar 

  38. Laibe C, Le Novère N (2007) MIRIAM Resources: tools to generate and resolve robust cross-references in systems biology. BMC Syst Biol 1:58

    Article  PubMed Central  PubMed  Google Scholar 

  39. Juty N, Le Novère N, Laibe C (2012) Identifiers.org and MIRIAM Registry: community resources to provide persistent identification. Nucleic Acids Res 40:D580–D586

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  40. Olivier, BG, Bergmann, FT (2015) The Systems Biology Markup Language (SBML) Level 3 Package: Flux Balance Constraints. Journal of Integrative Bioinformatics, 12:269

    Google Scholar 

  41. Chaouiya C, Berenguier D, Keating SM et al (2013) SBML Qualitative Models: a model representation format and infrastructure to foster interactions between qualitative modelling formalisms and tools. BMC Syst Biol 7:135

    Article  PubMed Central  PubMed  Google Scholar 

  42. Bornstein BJ, Keating SM, Jouraku A et al (2008) LibSBML: an API library for SBML. Bioinformatics 24:880–881

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  43. Dräger A, Rodriguez N, Dumousseau M et al (2011) JSBML: a flexible Java library for working with SBML. Bioinformatics 27:2167–2168

    Article  PubMed Central  PubMed  Google Scholar 

  44. Waltemath D, Adams R, Bergmann FT et al (2011) Reproducible computational biology experiments with SED-ML – The Simulation Experiment Description Markup Language. BMC Syst Biol 5:198

    Article  PubMed Central  PubMed  Google Scholar 

  45. IMDRF SaMD Working Group (2013) Software as a Medical Device (SaMD): key definitions. http://www.imdrf.org/documents/documents.asp. Accessed 25 Nov 2014

  46. Iec I (2006) 62304: 2006 Medical device software – software life cycle processes. International Electrotechnical Commission, Geneva

    Google Scholar 

  47. Buntz B (2011) Simplifying IEC 62304 compliance for developers. http://www.emdt.co.uk/article/iec-62304-compliance?utm_source=emdt&utm_medium=articlebottom&utm_campaign=camilla. Accessed 25 Nov 2014

  48. Regan G, McCaffery F, McDaid K et al (2013) Medical device standards’ requirements for traceability during the software development lifecycle and implementation of a traceability assessment model. Comput Stand Int 36:3–9

    Article  Google Scholar 

  49. Gotel OCZ, Finkelstein CW (1994) An analysis of the requirements traceability problem. Proceedings of the First International Conference on Requirements Engineering. pp 94–101

    Google Scholar 

  50. Lakshmanan M, Koh G, Chung BKS et al (2014) Software applications for flux balance analysis. Brief Bioinform 15:108–122

    Article  PubMed  Google Scholar 

  51. Kent E, Hoops S, Mendes P (2012) Condor-COPASI: high-throughput computing for biochemical networks. BMC Syst Biol 6:91

    Article  PubMed Central  PubMed  Google Scholar 

  52. Ebrahim A, Lerman JA, Palsson BO et al (2013) COBRApy: Constraints-Based Reconstruction and Analysis for Python. BMC Syst Biol 7:74

    Article  PubMed Central  PubMed  Google Scholar 

  53. Bergmann FT, Vallabhajosyula RR, Sauro HM (2006) Computational tools for modeling protein networks. Curr Proteomics 3:181–197

    Article  CAS  Google Scholar 

  54. Hucka M, Finney A, Sauro HM et al. (2002) The ERATO Systems Biology Workbench: enabling interaction and exchange between software tools for computational biology. Pac Symp Biocomput 450–461

    Google Scholar 

  55. Smith LP, Bergmann FT, Chandran D et al (2009) Antimony: a modular model definition language. Bioinformatics 25:2452–2454

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  56. Boele J, Olivier BG, Teusink B (2012) FAME, the Flux Analysis and Modeling Environment. BMC Syst Biol 6:8

    Article  PubMed Central  PubMed  Google Scholar 

  57. Kanehisa M, Goto S (2000) KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res 28:27–30

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  58. Byon W, Smith MK, Chan P et al (2013) Establishing best practices and guidance in population modeling: an experience with an internal population pharmacokinetic analysis guidance. CPT Pharmacometrics Syst Pharmacol 2:e51

    Article  CAS  PubMed  Google Scholar 

  59. Agoram BM, Demin O (2011) Integration not isolation: arguing the case for quantitative and systems pharmacology in drug discovery and development. Drug Discovery Today 16(23–24)

    Google Scholar 

  60. Mager D, Jusko W (2008) Development of translational pharmacokinetic- pharmacodynamic models. Clin Pharmacol Ther 83(6):909–912

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  61. Rostami-Hodjegan A, Tucker GT (2007) Simulation and prediction of in vivo drug metabolism in human populations from in vitro data. Nat Rev Drug Discov 6:140–148

    Article  CAS  PubMed  Google Scholar 

  62. Jones HM, Rowland-Yeo K (2013) Basic concepts in physiologically based pharmacokinetic modeling in drug discovery and development. CPT Pharmacometrics Syst Pharmacol 2:e63

    Article  PubMed Central  PubMed  Google Scholar 

  63. Bonate P (2011) Pharmacokinetic-pharmacodynamic modeling and simulation, 2nd edn. Springer, New York

    Book  Google Scholar 

  64. Lavielle M (2014) Mixed effects models for the population approach models, tasks, methods & tools, CRC biostatistics series. Chapman & Hall, Boca Raton, FL

    Google Scholar 

  65. Leil TA, Bertz R (2014) Quantitative systems pharmacology can reduce attrition and improve productivity in pharmaceutical research and development. Front Pharmacol 5:247

    Article  PubMed Central  PubMed  Google Scholar 

  66. Kola I, Landis J (2004) Can the pharmaceutical industry reduce attrition rates? Nat Rev Drug Discov 3:711–716

    Article  CAS  PubMed  Google Scholar 

  67. Bazzoli C, Retout S, Mentré F (2010) Design evaluation and optimization in multiple response nonlinear mixed effect models: PFIM 3.0. Comput Methods Prog Biomed 98:55–65

    Article  Google Scholar 

  68. Draeger A, Palsson BO (2014) Improving collaboration by standardization efforts in systems biology. Front Bioeng Biotechnol. doi:10.3389/fbioe.2014.00061

    Google Scholar 

  69. Swat MJ (2015) Pharmacometrics Markup Language (PharmML): opening new perspectives for model exchange in drug development. CPT Pharmacometrics Syst Pharmacol 4(6):316–319. doi:10.1002/psp4.57

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  70. Marciniak J (2009) The Simcyp population-based ADME simulator. Expert Opin Drug Metab Toxicol 5:211–223

    Article  PubMed  Google Scholar 

  71. Eissing T, Kuepfer L, Becker C et al (2011) A computational systems biology software platform for multiscale modeling and simulation: integrating whole-body physiology, disease biology, and molecular reaction networks. Front Physiol 2:4

    Article  PubMed Central  PubMed  Google Scholar 

  72. Aarons L (1999) Software for population pharmacokinetics and pharmacodynamics. Clin Pharmacokinet 36:255–264

    Article  CAS  PubMed  Google Scholar 

  73. D’Argenio DZ, Schumitzky A, Wang X (2009) Adapt 5 user's guide: Pharmacokinetic/pharmacodynamic systems analysis software. Tech. Rep., Biomedical Simulations Resource, Los Angeles

    Google Scholar 

  74. Lixoft. Monolix 4.3. http://lixoft.com

  75. Beal SL, Sheiner LB, Boeckmann AJ et al. (2009) NONMEM User’s guides. Technical report. Icon Development Solutions, Ellicott City, MD, USA

    Google Scholar 

  76. Lunn DJ, Best N, Thomas A et al (2002) Bayesian analysis of population pk/pd models: general concepts and software. J Pharmacokinet Pharmacodyn 29:271–307

    Article  CAS  PubMed  Google Scholar 

  77. Lindbom L, Ribbing J, Jonsson EN (2004) Perl–speaks–NONMEM (PsN) – a Perl module for NONMEM related programming. Comput Methods Programs Biomed 75(2):85–94

    Article  PubMed  Google Scholar 

  78. Keizer R, Karlsson M, Hooker A (2013) Modeling and Simulation Workbench for NONMEM: tutorial on Pirana, PsN, and Xpose. CPT Pharmacometrics Syst Pharmacol 2:e50

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  79. Kuhn E, Lavielle M (2005) Maximum likelihood estimation in nonlinear mixed effects models. Comput Stat Data Anal 49:1020–1038

    Article  Google Scholar 

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Acknowledgments

Brett Olivier is supported by a BE-Basic Foundation grant F08.005.001. Maciej Swat has received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement n° 115156, resources of which are composed of financial contributions from the European Union’s Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’ in kind contribution. The DDMoRe project is also supported by financial contribution from academic and SME partners.

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Authors and Affiliations

  1. Systems Bioinformatics, VU University Amsterdam, Amsterdam, The Netherlands

    Brett G. Olivier

  2. EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, UK

    Maciej J. Swat

  3. Molecular Cell Physiology, VU University Amsterdam, Amsterdam, The Netherlands

    Martijn J. Moné

  4. Systems and Synthetic Biology, Wageningen University, Wageningen, The Netherlands

    Martijn J. Moné

Authors
  1. Brett G. Olivier
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  2. Maciej J. Swat
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  3. Martijn J. Moné
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Corresponding author

Correspondence to Brett G. Olivier .

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Editors and Affiliations

  1. Dept of Systems Biology & Bioinformatics, University of Rostock, Rostock, Germany

    Ulf Schmitz

  2. Dept of Systems Biology & Bioinformatics, University of Rostock, Rostock, Germany

    Olaf Wolkenhauer

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Olivier, B.G., Swat, M.J., Moné, M.J. (2016). Modeling and Simulation Tools: From Systems Biology to Systems Medicine. In: Schmitz, U., Wolkenhauer, O. (eds) Systems Medicine. Methods in Molecular Biology, vol 1386. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-3283-2_19

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  • DOI: https://doi.org/10.1007/978-1-4939-3283-2_19

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-3282-5

  • Online ISBN: 978-1-4939-3283-2

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