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Whole-Cell Modeling and Simulation: A Brief Survey

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Abstract

Gaining knowledge and engineering of biological systems require comprehensive models of cellular physiology with 100% predictability. The whole-cell models direct experiments in molecular biological science and empower simulation and computer-aided design in synthetic biology. Whole-cell modeling and simulation help in personalized medical treatment building a biological system through cell interactions. In addition, the whole-cell model can address specific issues such as transcription, regulation, pathways for protein expression and many more. Constructing comprehensive whole-cell models with enough detail and validating them is a massive work. Though considering available parameters and pathways, modeling and simulation of a cell are partially successful, still, there exist a lot of more challenges that need to be tackled. Notwithstanding the immense challenges, whole-cell models are quickly getting to be viable. This paper briefly reviews the cutting edge of existing methods and techniques with their present status and the reason for improvements required in various stages of whole-cell modeling such as (1) collection of data, (2) designing tools and model building, (3) acceleration of simulation speed and (4) visualizing and analyzing.

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References

  1. Karr, J.R., et al.: The principles of whole-cell modeling. Curr. Opin. Microbiol. 27, 18–24 (2015). https://doi.org/10.1016/j.mib.2015.06.004

    Article  Google Scholar 

  2. Dror, R.O., et al.: Biomolecular simulation: a computational microscope for molecular biology. Annu. Rev. Biophys. 41, 429–452 (2012)

    Article  Google Scholar 

  3. Karr, J.R., et al.: A whole-cell computational model predicts phenotype from genotype. Cell 150, 389–401 (2012)

    Article  Google Scholar 

  4. Kazakiewicz, D., et al.: A combined systems and structural modeling approach repositions antibiotics for Mycoplasma genitalium. Comput. Biol. Chem. (2015). https://doi.org/10.1016/j.compbiolchem.2015.07.007

    Article  Google Scholar 

  5. Fraser, C.M.: The minimal gene complement of Mycoplasma genitalium. Science 270(5235), 397–403 (1995). https://doi.org/10.1126/science.270.5235.397

    Article  Google Scholar 

  6. Cokelaer, T., et al.: BioServices: a common Python package to access biological web services programmatically. Bioinformatics 29(24), 3241–3242 (2013)

    Article  Google Scholar 

  7. Kanehisa, M., et al.: KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28(1), 27–30 (2000)

    Article  Google Scholar 

  8. de Matos, P., et al.: ChEBI: a chemistry ontology and database. J. Cheminform. 2, P6 (2010)

    Article  Google Scholar 

  9. Chelliah, V., Juty, N.: BioModels: ten-year anniversary. Nucleic Acids Res. (2015). https://doi.org/10.1093/nar/gku1181

    Article  Google Scholar 

  10. Goldberg, A.P., et al.: A blueprint for human whole-cell modeling. Curr. Opin. Syst. Biol. 7, 8–15 (2017)

    Google Scholar 

  11. Lopez, C.F., Muhlich, J.L., et al.: Programming biological models in Python using PySB. Mol. Syst. Biol. (2013). https://doi.org/10.1038/msb.2013.1

    Article  Google Scholar 

  12. Karr, J.R., et al.: WholeCellKB: model organism databases for comprehensive whole-cell models. Nucleic Acids Res. (2012). https://doi.org/10.1093/nar/gks1108

    Article  Google Scholar 

  13. Purcell, O., et al.: Towards a whole-cell modeling approach for synthetic biology. Chaos 23(2), 025112 (2013)

    Article  Google Scholar 

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

    Article  Google Scholar 

  15. Hoops, S., Sahle, S.: Computational modeling of biochemical networks using COPASI. Methods Mol. Biol. (2009). https://doi.org/10.1007/978-1-59745-525-1_2

    Article  Google Scholar 

  16. Moraru, I.I., Schaff, J.C.: Virtual Cell modelling and simulation software environment. IET Syst. Biol. (2008). https://doi.org/10.1049/iet-syb:20080102

    Article  Google Scholar 

  17. Schaff, J.C., Slepchenko, B.M.: Analysis of nonlinear dynamics on arbitrary geometries with the virtual cell. Chaos 11(1), 115–134 (2001)

    Article  MATH  Google Scholar 

  18. Orth, J.D., Thiele, I., Palsson, B.O.: What is flux balance analysis? Nat. Biotechnol. 28, 245–248 (2010). https://doi.org/10.1038/nbt.1614

    Article  Google Scholar 

  19. Roberts, E., Magis, A.: Noise contributions in an inducible genetic switch: a whole-cell simulation study. PLoS Comput. Biol. 7(3), e1002010 (2011)

    Article  Google Scholar 

  20. Memeti, S., Li, L.: Benchmarking OpenCL, OpenACC, OpenMP, and CUDA: programming productivity, performance, and energy consumption. In: ARMS-CC. ISBN: 978-1-4503-5116-4/17/07. https://doi.org/10.1145/3110355.3110356 (2017)

  21. Carrera, J., Covert, M.W.: Why build whole-cell models? Trends Cell Biol. 25(12), 719–722 (2015). https://doi.org/10.1016/j.tcb.2015.09.004

    Article  Google Scholar 

  22. Goldberg, A.P.: Toward scalable whole-cell modeling of human cells. In: SIGSIM-PADS. ACM, ISBN: 978-1-4503-3742-7/16/05. https://doi.org/10.1145/2901378.2901402 (2016)

  23. Sanghvi, J.C., Regot, S., et al.: Accelerated discovery via a whole-cell model. Nat Methods (2013). https://doi.org/10.1038/nmeth.2724

    Article  Google Scholar 

  24. Karr, J.R., et al.: Toward community standards and software for whole-cell modeling. IEEE Trans. Biomed. Eng. 63(10), 2007–2014 (2016)

    Article  Google Scholar 

  25. Babtie, A.C., Stumpf, M.P.H.: How to deal with parameters for whole-cell modelling. J. R. Soc. Interface 14, 20170237 (2017). https://doi.org/10.1098/rsif.2017.0237

    Article  Google Scholar 

  26. Roberts, E., Stone, J.E., Sepúlveda, L.: Long time-scale simulations of in vivo diffusion using GPU hardware. ISBN: 978-1-4244-3750-4/09/$25.00, IEEE (2009)

  27. Hastings, E., et al.: ArrayExpress update—simplifying data submissions. Nucleic Acids Res. (2014). https://doi.org/10.1093/nar/gku1057

    Article  Google Scholar 

  28. Alonso-López, D., Campos-Laborie, F.J., Gutiérrez, M.A., et al.: APID database: redefining protein–protein interaction experimental evidences and binary interactomes. Database (2019). https://doi.org/10.1093/database/baz005

    Article  Google Scholar 

  29. Sajed, T., et al.: ECMDB 2.0: a richer resource for understanding the biochemistry of E. coli. Nucleic Acids Res. (2016). https://doi.org/10.1093/nar/gkv1060

    Article  Google Scholar 

  30. Grethe, G., et al.: International chemical identifier for reactions (RinChI). J. Cheminform. 5, 45 (2013)

    Article  Google Scholar 

  31. Caspi, R., Altman, T., et al.: The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res. (2012). https://doi.org/10.1093/nar/gkr1014

    Article  Google Scholar 

  32. Schomburg, I., et al.: The BRENDA enzyme information system–From a database to an expert system. J. Biotechnol. (2017). https://doi.org/10.1016/j.jbiotec.2017.04.020

    Article  Google Scholar 

  33. Mi, H., Thomas, P.: PANTHER pathway: an ontology-based pathway database coupled with data analysis tools. Methods Mol. Biol. 563, 123–140 (2009)

    Article  Google Scholar 

  34. Wang, M., Herrmann, C.J., Simonovic, M., Szklarczyk, D., Mering, C.: Version 4.0 of PaxDb: protein abundance data, integrated across model organisms, tissues, and cell-lines. Proteomics 15, 3163–3168 (2015)

    Article  Google Scholar 

  35. Five years of Scientific Data. Sci. Data 6, 72 (2019). https://doi.org/10.1038/s41597-019-0065-y

  36. Shuler, M.L., Leung, S., Dick, C.C.: A mathematical model for the growth of a single cell. Ann. N. Y. Acad. Sci. 326, 35–52 (1979)

    Article  Google Scholar 

  37. Drager, A., Palsson, B.Ø.: Improving collaboration by standardization efforts in systems biology. Front. Bioeng. Biotechnol. 2, 61 (2014)

    Article  Google Scholar 

  38. Sauro, H.M., Bergmann, F.T.: Standards and ontologies in computational systems biology. Essays Biochem. 45, 211–222 (2008)

    Article  Google Scholar 

  39. https://github.com/opencobra/cobrapy. Accessed 24 Feb 2019

  40. Neves, S.R.: Developing models in virtual cell. Sci. Signal. 4(192), tr12 (2011). https://doi.org/10.1126/scisignal.2001970

    Article  Google Scholar 

  41. Funahashi, A., Matsuoka, Y., et al.: Celldesigner: a modeling tool for biochemical networks. In: Proceedings of the 2006 Winter Simulation Conference (2006). https://doi.org/10.1109/wsc.2006.322946

  42. Garny, A., Nickerson, D.P., Cooper, J., dos Santos, R.W., Miller, A.K., McKeever, S., Nielsen, P.M.F., Hunter, P.J.: CellML and associated tools and techniques. Philos. Trans. A Math. Phys. Eng. Sci. 366(1878), 3017–3043 (2008)

    Article  Google Scholar 

  43. Hucka, M., et al.: The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19(4), 524–531 (2003)

    Article  Google Scholar 

  44. Glont, M., et al.: BioModels: expanding horizons to include more modelling approaches and formats. Nucleic Acids Res. (2017). https://doi.org/10.1093/nar/gkx1023

    Article  Google Scholar 

  45. SimTK Team. Simtk. https://simtk.org. Accessed June 2018

  46. Gaster, B.R., Howes, L.: Can GPGPU Programming be liberated from the data-parallel bottleneck? Adv. Micro Dev. 45, 42–52 (2012). https://doi.org/10.1109/MC.2012.257

    Article  Google Scholar 

  47. Macklin, D.N., Ruggero, N.A., Covert, M.W.: The future of whole-cell modeling. Curr. Opin. Biotechnol. 28, 111–115 (2014). https://doi.org/10.1016/j.copbio.2014.01.012

    Article  Google Scholar 

  48. Zhang, H., Zhang, D.-F., Bi, X.-A.: Comparison and analysis of GPGPU and parallel computing on multi-core CPU. Int. J. Inf. Educ. Technol. 2(2), 185 (2012)

    Google Scholar 

  49. Ghorpade, J.: GPGPU processing in CUDA architecture. Adv. Comput. Int. J. (ACIJ) 3(1), 105–120 (2012)

    Article  Google Scholar 

  50. Nobile, M.S., et al.: cupSODA: A CUDA-powered simulator of mass-action kinetics. In: Malyshkin, V. (ed.) PaCT 2013, LNCS 7979, pp. 344–357. Springer, Berlin (2013)

    Google Scholar 

  51. Ewald, R., et al.: SESSL: a domain-specific language for simulation experiments. ACM Trans. Model. Comput. Simul. (2014). https://doi.org/10.1145/2567895

    Article  MathSciNet  MATH  Google Scholar 

  52. Das, B.: A network-based zoning for parallel whole-cell simulation. Bioinformatics 35(1), 88–94 (2018). https://doi.org/10.1093/bioinformatics/bty530

    Article  Google Scholar 

  53. Junker, B.H., Klukas, C., Schreiber, F.: VANTED: a system for advanced data analysis and visualization in the context of biological networks. BMC Bioinform. 7, 109 (2006)

    Article  Google Scholar 

  54. Karr, J.R., et al.: WholeCellSimDB: a hybrid relational/HDF database for whole-cell model predictions. Database (2014). https://doi.org/10.1093/database/bau095

    Article  Google Scholar 

  55. Le Novère, N., et al.: The systems biology graphical notation. Nat. Biotechnol. 27, 735–741 (2009)

    Article  Google Scholar 

  56. Karr, J., et al.: WholeCellViz: data visualization for whole-cell models. BMC Bioinform. (2013). https://doi.org/10.1186/1471-2105-14-253

    Article  Google Scholar 

  57. Okonechnikov, K., et al.: Unipro UGENE: a unified bioinformatics toolkit. Bioinform. Appl. 28(8), 1166–1167 (2012). https://doi.org/10.1093/bioinformatics/bts091

    Article  Google Scholar 

  58. Missan, S., McDonald, F., et al.: CESE: cell electrophysiology simulation environment. Appl. Bioinform. 4, 155–156 (2005)

    Article  Google Scholar 

  59. Yugi, K., et al.: Review trans-omics: how to reconstruct biochemical networks across multiple ‘Omic’ layers. Trends Biotechnol. (2016). https://doi.org/10.1016/j.tibtech.2015.12.013

    Article  Google Scholar 

  60. Jung, S., et al.: Chado use case: storing genomic, genetic and breeding data of Rosaceae and Gossypium crops in Chado. Database (2016). https://doi.org/10.1093/database/baw010

    Article  Google Scholar 

  61. Yang, K., et al.: Databases and ontologies CMAP: complement map database. Bioinformatics 29, 1832–1833 (2013). https://doi.org/10.1093/bioinformatics/btt269

    Article  Google Scholar 

  62. Wolstencroft, K., et al.: SEEK: a systems biology data and model management platform. BMC Syst. Biol. 9, 33 (2015)

    Article  Google Scholar 

  63. Oberlin, A.T., et al.: Biological Database of Images and Genomes: tools for community annotations linking image and genomic information. Database (2013). https://doi.org/10.1093/database/bat016

    Article  Google Scholar 

  64. Arkin, A.P., et al.: KBase: the United States Department of Energy Systems Biology Knowledgebase. Nat. Biotechnol. 36, 566–569 (2018)

    Article  Google Scholar 

  65. Lopez, C.F., et al.: Programming biological models in Python using PySB. Mol. Syst. Biol. (2013). https://doi.org/10.1038/msb.2013.1

    Article  Google Scholar 

  66. Devoid, S., et al.: Automated genome annotation and metabolic model reconstruction in the SEED and model SEED. In: Alper, H.S. (ed.) Systems Metabolic Engineering: Methods and Protocols. Methods in Molecular Biology, vol. 985. Springer, Berlin (2013). https://doi.org/10.1007/978-1-62703-299-5_2

    Chapter  Google Scholar 

  67. Feist, A.M., et al.: A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information. Mol. Syst. Biol. (2007). https://doi.org/10.1038/msb4100155

    Article  Google Scholar 

  68. Duina, A.A., et al.: Budding yeast for budding geneticists: a primer on the Saccharomyces cerevisiae model system. Genetics 197, 33–48 (2014). https://doi.org/10.1534/genetics.114.163188

    Article  Google Scholar 

  69. Earl, A.M., et al.: Ecology and genomics of Bacillus subtilis. Trends Microbiol. (2008). https://doi.org/10.1016/j.tim.2008.03.004

    Article  Google Scholar 

  70. Kashyap, S., et al.: Mycoplasma pneumonia: clinical features and management. Lung India (2010). https://doi.org/10.4103/0970-2113.63611

    Article  Google Scholar 

  71. Ihekwaba, A.E.C., et al.: Computational modelling and analysis of the molecular network regulating sporulation initiation in Bacillus subtilis. BMC Syst. Biol. 8, 119 (2014)

    Article  Google Scholar 

  72. Bajantri, B., et al.: Mycoplasma pneumoniae: a potentially severe infection. J. Clin. Med. Res. (2018). https://doi.org/10.14740/jocmr3421w

    Article  Google Scholar 

  73. Diaz, M.H., et al.: Comprehensive bioinformatics analysis of Mycoplasma pneumoniae genomes to investigate underlying population structure and type-specific determinants. PLoS ONE 12(4), e0174701 (2017). https://doi.org/10.1371/journal.pone.0174701

    Article  Google Scholar 

  74. Pray, L.A.: Eukaryotic genome complexity. Nat. Educ. 1(1), 96 (2008)

    Google Scholar 

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  1. Centre for Incubation, Innovation, Research, and Consultancy, Jyothy Institute of Technology, Tataguni, Off. Kanakapura Road, Bengaluru, 560082, India

    Nayana G. Bhat & S. Balaji

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Bhat, N.G., Balaji, S. Whole-Cell Modeling and Simulation: A Brief Survey. New Gener. Comput. 38, 259–281 (2020). https://doi.org/10.1007/s00354-019-00066-y

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