Modelling Cell Cycle Regulation
Claire D. Sherman
Radiation Effects Research Foundation, Hiroshima, Japan
Abstract
All living organisms have built-in mechanisms to regulate their
proliferation. Consider for a moment the possibility of uncontrolled
cellular growth. According to O'Farrell (1992), if one simple bacterium
Eschericia coli (E. coli) were allowed to proliferate at its maximum
rate for a 24 hour period (72 cell doublings), it's mass would
increase from 10^-12 g to 4000 metric tons (an amplification of
4x10^21 fold, i.e. 2^72 = 4.722 x 10^21). In two days, the mass of
E. coli growing in this uncontrolled fashion would exceed (by many
times) the mass of Earth. Although this example clearly demonstrates
the implausibility of unchecked cellular growth, many mathematicians
have not incorporated growth regulation or tissue homeostasis into
thir biological models (examples would include, but not be limited to,
the stochastic pure birth process and the linear birth-death process
where the birth rate is estimated to be larger than the death
rate). Thus, at least in terms of protracted model predictions, it is
not inconceivable to have an E. coli expand in a limitless
fashion. This presentation will describe a method to coordinate the
temporal expression of cyclin proteins (modelled via a
physiologically-based pharmacokinetic model) with cell labelling data
(modelled via a stochastic model) so that the estimated rates of the
various phases of the cell cycle model are "in sync" with their
associated biochemical signals regulating growth.
O'Farrell (1992) Cell cycle control: Many ways to skin a cat. Trends
Cell Biol 2: 159.
