Cell population dynamics

John C. Berg, Andreas Acrivos, and Michel Boudart, Evaporation Convection H. M. Tsuchiya, A. G, Fredrickson, and R. Aiis, Dynamics of Microbial Cell Populations Samuel Sideman, Direct Contact Heat Transfer between Immiscible Liquids Howard Brenner, Hydrodynamic Resistance of Particles at Small Reynolds Numbers... 

John C. Berg, Andreas Acrivos. and Michel Boudart Dynamics of Microbial Cell Populations... 

Dynamics of Microbial Cell Populations H. M. Tsuchiya, A. G. Fredrickson, and R. Arts... 

A. G. Ewing, P. S. Eriksson, L. Brodin, and O. Orwar. Measurement of the Dynamics of Exocytosis and Vesicle Retrieval at Cell Populations Using a Quartz Crystal Microbalance, Anal. Chem. 2001, 73, 5805 X. Su, R. Robelek, Y. Wu,... 

Tsuchiya HM, Fredrickson AG, Aris R (1966), Dynamics of microbial cell population, Adv. Chem. Eng., 6 125-206. 

The bio-activity of synthesized samples was estimated from mobility parameters of unicellular microorganisms measured by Dynamic Light Scattering (DLS). The following characteristics of cell population were measured cell concentration (units/ml) % of mobile cells mean translation velocity (pm/s), cell rotational frequency of (Hz) flagella beat frequency (Hz), distribution of beat frequencies, and kinetic energy of cell motion in a viscous environment (arbitrary units). Details of experimental equipment and measurement procedures may be found elsewhere.7,8 All parameters were measured with an accuracy of about 2%.9... 

Dynamics of development of biological populations in general case and cells populations in particular may be formally described by various empirical equations. 

There are understandably some difficulties in describing reaction kinetics during microbiological conversions. Unlike chemical reactions, transport of components into and out of cells is involved. The cell population itself is in a dynamic state, and processes occurring within the cells may be accompanied by chemical reactions in the surrounding medium. 

A new deterministic population balance model with distributed cell populations has been presented. The model is based on the unstructured approach of Mohler et al. [4]. Concerning outer dynamics the present model is equivalent to the unstructured model which proved to be sufficient to predict virus yields for different initial conditions [4]. The characteristics of the inner dynamics can be simulated except of the decrease of fluorescence intensity at later time points. The biological reasons for this effect are unclear. Presumably there are more states that have to be considered during virus replication like intercellular communication, extent of apoptosis or specific stage in cell cycle. Future computational and experimental research will aim in this directions and concentrate on structured descriptions of the virus replication in mammahan cell culture. 

Alternative models of cell growth dynamics may be substituted for Eq. (23.4) and tested using standard model fitting criteria. For example, in vitro and in vivo cell populations rarely continue to grow exponentially as a result of spatial, nutritive, and other factors that may place an upper limit on cell density (Rss). The logistic growth model is one function that limits exponential growth and is defined as (7)... 

Single-cell experiments allow key parameters that define the dynamic properties of cellular signaling networks that are not available from cell population experiments to be extracted (e.g., Western blotting). Such parameters include delay time constants between signaling steps or bistability in the activation process. 

Fredrickson, A. G., Ramkrishna, D., and Tsuchiya, H. M., Statistics and Dynamics of Procaryotic Cell Populations, Mathematical Biosciences 1 (1967) 327-374. 

---