Numerical solution of a mass structured cell population balance model in an environment of changing substrate concentration

Cell population balance models are deterministic formulations which describe the dynamics of cell growth and take into account the biological fact that cell properties are distributed among the cells of a population, due to the operation of the cell cycle. Such models, typically consist of a partial integro-differential equation, describing cell growth, and an ordinary integro-differential equation, accounting for substrate consumption. A numerical solution of the mass structured cell population balance in an environment of changing substrate concentration has been developed. The presented method is general. It can be applied for any type of single-cell growth rate expression, equal or unequal cell partitioning at cell division, and constant or changing substrate concentration. It consists of a time-explicit, one-step, finite difference scheme which is characterized by limited requirements in memory and computational time. Simulations were made and conclusions were drawn by applying this numerical method to several different single-cell growth rate expressions. A periodic behavior was observed in the case of linear growth rate, equal partitioning and constant substrate concentration. The periodicity was equal to the average doubling time of the population. In all other cases examined, a state of balanced growth was reached. Unequal partitioning resulted in broader balanced growth distributions which are reached faster. For the specific types of growth rate dependence on the substrate concentration considered, the changing substrate concentration did not affect the balanced growth-normalized distributions, except for the case of linear growth rate and equal partitioning, where the depletion of the substrate destroyed the periodic behavior observed for constant substrate concentration, and forced the system to reach a steady state.

[1]  M. Daniels Lipid synthesis in relation to the cell cycle of Bacillus megaterium KM and Escherichia coli. , 1969, The Biochemical journal.

[2]  Friedrich Srienc,et al.  Cell‐cycle‐dependent protein accumulation by producer and nonproducer murine hybridoma cell lines: A population analysis , 1991, Biotechnology and bioengineering.

[3]  H. M. Tsuchiya,et al.  Statistics and dynamics of microbial cell populations. , 1966 .

[4]  Jens Nielsen,et al.  A conceptual model of autonomous oscillations in microbial cultures , 1994 .

[5]  Doraiswami Ramkrishna,et al.  Statistical models of cell populations , 1979 .

[6]  D. Glaser,et al.  Correlation between rate of cell growth and rate of DNA synthesis in Escherichia coli B-r. , 1971, Proceedings of the National Academy of Sciences of the United States of America.

[7]  A. G. Fredrickson Growth processes in bioreactors with external sources of biomass: Application of structured, continuum models , 1992 .

[8]  James E. Bailey,et al.  Transient responses of budding yeast populations , 1983 .

[9]  T. V. Kostova Numerical solutions of a hyperbolic differential-integral equation , 1988 .

[10]  Odo Diekmann,et al.  On the stability of the cell size distribution , 1986 .

[11]  E. Fiolitakis Ein altersstrukturiertes Populationsmodell zur Beschreibung instationärer mikrobieller Prozesse, Teil II: Modell‐Verifikation am Beispiel der Glucose‐Fermentation mit Zymomonas mobilis , 1987 .

[12]  S. Cooper,et al.  What is the bacterial growth law during the division cycle? , 1988, Journal of bacteriology.

[13]  R. Pepperkok,et al.  System for quantitation of gene expression in single cells by computerized microimaging: application to c-fos expression after microinjection of anti-casein kinase II antibody. , 1993, Experimental cell research.

[14]  E. Fiolitakis Ein altersstrukturiertes Populationsmodell zur Beschreibung instationärer mikrobieller Prozesse, Teil I: Theorie , 1987 .

[15]  H. E. Kubitschek,et al.  Cell volume increase in Escherichia coli after shifts to richer media , 1990, Journal of bacteriology.

[16]  H. M. Tsuchiya,et al.  Dynamics of Microbial Cell Populations , 1966 .

[17]  R. E. Ecker,et al.  Synthesis of Protein, Ribonucleic Acid, and Ribosomes by Individual Bacterial Cells in Balanced Growth , 1969, Journal of bacteriology.

[18]  J. Nielsen,et al.  Population balance models of autonomous microbial oscillations. , 1995, Journal of biotechnology.

[19]  Friedrich Srienc,et al.  Measurement of Unequal DNA Partitioning in Tetrahymena pyriformis Using Slit‐Scanning Flow Cytometry , 1994 .

[20]  Chichia Chiu A numerical method for nonlinear age dependent population models , 1990 .

[21]  Numerical solutions to equations modelling nonlinearly interacting age-dependent populations , 1990 .

[22]  F. Srienc,et al.  Kinetics of the Cell Cycle of Saccharomyces cerevisiae a , 1992, Annals of the New York Academy of Sciences.

[23]  Doraiswami Ramkrishna,et al.  On the solution of statistical models of cell populations , 1971 .

[24]  Eun-Jae Park,et al.  An upwind scheme for a nonlinear model in age-structured population dynamics , 1995 .

[25]  Mi-Young Kim,et al.  Galerkin methods for a model of population dynamics with nonlinear diffusion , 1996 .

[26]  Friedrich Srienc,et al.  Solutions of population balance models based on a successive generations approach , 1997 .

[27]  A. L. Koch Biomass growth rate during the prokaryote cell cycle. , 1993, Critical reviews in microbiology.

[28]  S. Cooper The Escherichia coli cell cycle. , 1990, Research in microbiology.

[29]  William H. Press,et al.  Book-Review - Numerical Recipes in Pascal - the Art of Scientific Computing , 1989 .

[30]  Eun-Jae Park,et al.  Mixed approximation of a population diffusion equation , 1995 .

[31]  Friedrich Srienc,et al.  Cell-Cycle Analysis in Phagotrophic Microorganisms from Flow Cytometric Histograms , 1997 .

[32]  J. Strikwerda Finite Difference Schemes and Partial Differential Equations , 1989 .

[33]  D E Block,et al.  Slit Scanning of Saccharomyces cerevisiae Cells: Quantification of Asymmetric Cell Division and Cell Cycle Progression in Asynchronous Culture , 1990, Biotechnology progress.