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Related Experiment Videos

Transition probability cell cycle model with product formation.

S J Cain1, P C Chau

  • 1Department of AMES (Chemical Engineering), University of California at San Diego, La Jolla, California 92093-0411, USA.

Biotechnology and Bioengineering
|April 1, 1999
PubMed
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This study extends a cell cycle model to analyze product formation, revealing a negative association between monoclonal antibody productivity and dilution rates in chemostats, especially with age-dependent processes.

Area of Science:

  • Biotechnology
  • Cell Biology
  • Biochemical Engineering

Background:

  • The Smith and Martin (1973) cell cycle model provides a foundation for understanding cell population dynamics.
  • Previous models often simplified product synthesis and export mechanisms within the cell cycle.
  • Understanding cell cycle-dependent product formation is crucial for optimizing bioprocesses.

Purpose of the Study:

  • To extend the cell cycle population model to incorporate product synthesis and export.
  • To investigate two distinct product formation mechanisms: direct protein production and mRNA-mediated production.
  • To analyze the relationship between cell cycle parameters, productivity, and culture conditions.

Main Methods:

  • Developed an extended cell cycle population model based on the transition probability model.

Related Experiment Videos

  • Incorporated mechanisms for direct product (protein) synthesis and indirect (mRNA-mediated) synthesis followed by translation and export.
  • Modeled cell density distribution across primary product and maturity age within the cell cycle.
  • Analyzed substrate-dependent batch and continuous cultures, including chemostat conditions.

    • Main Results:

    • The extended model successfully describes various culture conditions, including substrate-dependent batch and continuous cultures.
    • A negative growth association was observed between specific productivity (e.g., monoclonal antibodies) and dilution rates in chemostats.
    • Maturity age-dependent transcription and translation can amplify the negative association between specific productivity and specific growth rate.

    Conclusions:

    • The developed computational model provides a rigorous framework for analyzing cell cycle-dependent product formation.
    • The findings offer insights into monoclonal antibody productivity in chemostat cultures.
    • The model highlights the impact of cell cycle progression and age-dependent processes on overall product yield.