Biomedical subjects

S J Cain

Publications and source records attributed to S J Cain.

4 recordsLinked to original sources

PubMed1998

Transition probability cell cycle model with product formation.

A cell cycle population model based on the transition probability model of Smith and Martin (1973) has been extended to include product synthesis and export. The model handles two probable mechanisms. In the direct production model, the product is the protein. In the transcription model, the product is the specific mRNA. The protein is synthesized by translation of the specific mRNA and subsequently exported. In either case, the cell density is jointly distributed in the primary product and maturity age in the cell cycle. This extended model also is capable of describing a large range of conditions, including substrate dependent batch and continuous cultures. With the use of unity maturity-velocity (but the transition rate a function of limiting substrate), the model is shown to exhibit a negative growth association between the specific productivity of monoclonal antibodies from hybridomas and the dilution rates of a chemostat. Possibilities of maturity age dependent transcription and translation are considered, and the results show that these features can amplify the specific productivity negative association with specific growth rate. While this model may provide a partial elucidation of monoclonal antibody productivity in a chemostat, the present work provides a proper framework with which probable cell cycle dependent product formation can be analyzed rigorously with a comprehensive computational model.

Animals↗

PubMed1997

Transition probability cell cycle model. Part I--Balanced growth.

A cell cycle model based on the concept of a transition probability first proposed by Smith & Martin has been implemented as a differential equation model. The probabilistic A-state is modeled as a lumped parameter while the deterministic B-phase is modeled as a distributed parameter, and analytical solutions for both the population and the fraction of labeled mitosis (FLM) curves are derived under balanced growth conditions. Contributions toward cell cycle variability by single and double random transitions are considered. A double transition model provides a more realistic description of the cell cycle time distribution. For gross cell population behavior, a single transition from the A-state to the B-phase may provide acceptable approximation. In spite of the simplification, the single transition Smith & Martin model is shown to describe the gradual asynchronization of a cell population.

Animals↗

PubMed1997

Transition probability cell cycle model. Part II--Non-balanced growth.

A cell cycle model based on the transition probability model of Smith & Martin has been extended to non-balanced growth conditions in batch cultures. The model considers transition to a quiescent cell fraction, variable maturity-velocity, exogenous maintenance, and cell death. This extended model is capable of describing a large range of cell culture behavior which may not conform to Monod kinetics. The use of a constant quiescent transition allows the population to enter a stationary phase, but with a very large A-state to B-phase cell ratio. A substrate dependent quiescent transition helps to reduce this ratio while maintaining the general features of the population growth curves. A model with substrate dependent variable maturity-velocity qualitatively is similar to the Monod equation, while providing additional information on population distribution. The combination of quiescent transition and a substrate dependent maturity-velocity is also examined, and the resulting model is shown to capture the essence of both features.

Animals↗

PubMed1997

A transition probability cell cycle model simulation of bivariate DNA/bromodeoxyuridine distributions.

The transition probability cell cycle model is extended to describe both cell cycle variability and incorporation of bromodeoxyuridine (BrdUrd). The model can simulate BrdUrd uptake in both pulse-chase and continuous-labeling experiments. With the use of a random transition, variability due to cell cycle progression is distinguished from dispersion due to staining and machine errors in the generation of bivariate DNA/BrdUrd distributions. In a comparative test with a compartmental model developed by Yanagisawa et al. (Cytometry 6:550-562, 1985), the present model is shown to provide realistic simulations with fewer model parameters and with the ability to describe gradual asynchronization of cell cycle cohorts. With model predictions as the basis, a simulated experiment is performed to illustrate the difficulty in analyzing bivariate distributions. The simulated experiment illustrated that it is very easy to overestimate unlabeled cell fractions, and, as a result, matching the periodicity of the cell cycle cohort movements is more reliable in the estimation of model parameters.

Algorithms↗