Export file:

Format

  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text

Content

  • Citation Only
  • Citation and Abstract

Modeling cell behavior: moving beyond intuition

Canada Research Chair in Applied Metabolic Engineering, Department of Chemical Engineering, École Polytechnique de Montréal, P.O. box 6079, Centre-ville Station, Montréal, Québec, H3C 3A7, Canada

In the context of the launching of this new journal, we propose a forum to the community of researchers interested and involved in, or even simply questioning the why, what, how, and when of modeling cell or cell culture behavior. To start the discussion, we review some of the usual questions we are routinely asked on the pertinence of modeling cell behavior, and on who might benefit from conducting such work. To draw a global portrait, throughout this text we refer the reader to handbooks introducing the basics of modeling a biosystem, as well as to selected works that can help visualize the broad fields of applications.
  Figure/Table
  Supplementary
  Article Metrics

Keywords Modeling cell behavior; Mathematical model; Cell metabolism

Citation: Mario Jolicoeur. Modeling cell behavior: moving beyond intuition. AIMS Bioengineering, 2014, 1(1): 1-12. doi: 10.3934/bioeng.2014.1.1

References

  • 1. Pavlou AK, Belsey MJ (2008) The therapeutic antibodies market to 2008. Eur J Pharm Biopharm 59: 389-396.
  • 2. Wurm F (2004) Production of recombinant protein therapeutics in cultivated mammalian cells. Nat Biotechnol 22: 1393-1398.    
  • 3. Li F, Vijayasankaran N, Shen A, et al. (2010) Cell culture processes for monoclonal antibody production. mAbs J 2: 466-479.    
  • 4. Pirt SJ (1975) Principles of microbe and cell cultivation. Blackwell Scientific Publications.
  • 5. Bailey JE, Ollis DF (1986) Biochemical Engineering Fundamentals, 2 Eds., New York: McGraw-Hill, 984.
  • 6. Monod J (1949) The Growth of Bacterial Cultures. Ann Rev Microbiol 3: 371.    
  • 7. Lamboursain L, Jolicoeur M (2005) Critical influence of Eschscholtzia californica cells nutritional state on secondary metabolite production. Biotechnol Bioeng 91: 827-837.    
  • 8. Cloutier M, Bouchard-Marchand É, Perrier M, et al. (2007) A predictive nutritional model for plant cells and hairy roots. Biotechnol Bioeng 99: 189-200.
  • 9. Stephanopoulos G, Aristodou, Nielsen J (1998) Metabolic Engineering. Principles and Methodologies, San Diego : Academic Press, 698.
  • 10. Palsson BO (2011) Systems Biology. Simulation of dynamic network states, Cambridge UK: Cambridge University Press, 317.
  • 11. Fell D (1997) Understanding the Control of Metabolism. In Frontiers in metabolism. London UK: Portland Press, 301.
  • 12. Bordbar A, Monk JM, King ZA, et al. (2014) Constraint-based models predict metabolic and associated cellular functions. Nat Rev Gen 15: 107-120.    
  • 13. Chong WPK, Thng SH, Hiu AP, et al. (2012) LC-MS-Based Metabolic Characterization of High Monoclonal Antibody-Producing Chinese Hamster Ovary Cells. Biotechnol Bioeng 109:3103-3111.    
  • 14. Dunn WB, Broadhurst DI, Atherton HJ, et al. (2011) Systems level studies of mammalian metabolomes: the roles of mass spectrometry and nuclear magnetic resonance spectroscopy. Chem Soc Rev 40: 387-426.    
  • 15. Ben-Tchavtchavadze M, Perrier M, Jolicoeur M (2010) A Non-Invasive Technique for the Measurement of the Energetic State of Free-Suspension Mammalian Cells. Biotechnol Prog 26: 532-541.
  • 16. Scheer M, Grote A, Chang A, et al. (2011) BRENDA, the enzyme information system. Nucleic Acids Res 39 (Database issue http://www.brenda-enzymes.org/): 670-676.
  • 17. Nolan RP, Lee K (2011) Dynamic model of CHO cell metabolism. Metab Eng 13: 108-124.    
  • 18. Leduc M, Tikhomiroff C, Cloutier M, et al. (2006) Development of a kinetic metabolic model: application to Catharanthus roseus hairy root. Bioprocess Biosyst Eng 28: 295-313.    
  • 19. Ghorbaniaghdam A, Henry O, Jolicoeur M (2012) A kinetic-metabolic model based on cell energetic state: study of CHO cell behavior under Na-butyrate stimulation, Bioprocess Biosyst Eng 36: 469-487.
  • 20. Ghorbaniaghdam A, Chen J, Henry O, et al. (2014) Analyzing clonal variation of monoclonal antibody producing CHO cell lines using an in silico metabolomic platform. PLoS One 9:90832.    
  • 21. Ghorbaniaghdam A, Chen J, Henry O, et al. (2014) An in silico study of the regulation of CHO cells glycolysis. J Theor Biol In press.
  • 22. Cloutier M, Chen J, Tagte F, et al. (2009) Kinetic metabolic modelling for the control of plant cells cytoplasmic phosphate. J Theor Biol 259: 118-131.    
  • 23. Cloutier M, Chen J, DeDobbeleer C, et al. (2009) A systems approach to plant bioprocess optimization. Plant Biotechnol J 7: 939-951.    
  • 24. Valancin A, Srinivasan B, Rivoal J, et al. (2013) Analyzing the effect of decreasing cytosolic triosephosphate isomerase on Solanum tuberosum hairy root cells using a kinetic-metabolic model. Biotechnol Bioeng 110: 924-935.    
  • 25. Poliquin PO, Chen J, Cloutier M, et al. (2013) Metabolomics and in-silico analysis reveal critical energy deregulations in animal models of Parkinson's disease. PLoS One 8: 69146
  • 26. Michal G, Schomburg D (2012) Biochemical Pathways: An atlas of biochemistry and molecular biology, 2 Eds., Wiley, 398.

 

Reader Comments

your name: *   your email: *  

Copyright Info: 2014, Mario Jolicoeur, licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

Download full text in PDF

Export Citation

Copyright © AIMS Press All Rights Reserved