Friedrich Srienc, Associate Fellow

Cell Growth Dynamics of Unicellular Organisms Under Changing Environmental Conditions

  Cell properties such as mass, protein content, DNA content, and other cell components, are typically distributed among the cells of a population due to the operation of the cell cycle. Furthermore, cell growth exhibits very different patterns during each stage of the cell cycle. A mathematical model was developed by these researchers to take into account the distributed and staged nature of cell growth. The model consists of a system of population balance equations, each describing a different stage of the cell cycle, and an ordinary integro-differential equation accounting for substrate consumption. Using a time-explicit, finite difference scheme, the problem was solved for the case of a single variable and constant substrate concentration. A similar algorithm was also used to solve the single-variable, single-staged model under conditions of changing substrate concentration.

  This research group worked on the extension of the numerical method in order to achieve the solution of the multi-staged, multi-variable cell population balance models in an environment of changing substrate concentration. Evolution has supplied biological organisms with a highly coupled network of hundreds of enzyme catalyzed reactions. Scientific advances in recent decades like polymerase chain reaction (PCR) have provided means of altering the topography of these reaction networks. For instance, enzymatic activities can be blocked by disrupting certain genes, or new reactions can be introduced through the expression of recombinant genes. Analysis of native and recombinant networks has been simplified by a number of theoretical tools.

  One such method is elementary mode analysis. An elementary mode is the simplest balanced combination of substrates, products, and reactions that enforces a no-accumulation restriction (dc/dt=0) on internal metabolites. The analysis program metatool determines all possible balanced elementary modes for a given reaction network (Pfeiffer et al., 1999). Such results can give information on product formation pathways and can determine basic parameters like maximum theoretical yield. Large, highly branched networks with multiple products and substrates often have numerous elementary modes. Determining all possible modes for these systems can result in significant computational burden.