Growing cells

Synthesis of industrial chemicals by microbial cells may be by fermentation (free, living cells), immobilised growing cells, immobilised resting cells or immobilised dead cells. 

The simplest theory involved in mass transfer across an interface is film theory, as shown in Figure 3.10. In this model, the gas (CO) is transferred from the gas phase into the liquid phase and it must reach the surface of the growing cells. The rate equation for this case is similar to the slurry reactor as mentioned in Levenspiel.20... 

The rate at which a new equilibrium turgor is achieved in response to an altered external water potential depends on the water flow. The half-time (ti/2) of the response for a non-growing cell is given by ... 

We were able show that temperature is a convenient and promising parameter to achieve this goal. Growing cells at temperatures lower than 37 °C will slow down growth rate but on the other hand have a beneficial effect on shear resistance [501 (Fig- 3 ). 

Escherichia coli and Klebsiella pneumoniae subsp, aerogenes produce acid from lactose on this medium, altering the colour of the indicator, and also adsorb some of the indicator which may be precipitated around the growing cells. The organisms causing typhoid and paratyphoid fever and bacillary dysentery do not ferment lactose, and colonies of these organisms appear transparent. 

As illustrated in Fig. 3, in both poplar stems and mung bean hypocotyls, basic isoforms became prevalent in mature, resting cells whereas in young, growing cells, neutral isoforms were predominant. 

Simkins S, R Mukherjee, M Alexander (1986) Two approaches to modeling kinetics of biodegradation by growing cells and application of a two-compartment model for mineralization kinetics in sewage. Appl Environ Microbiol 51 1153-1160. 

Hryant I am a little concerned about going straight from mitotic index to proliferation rate. Is that a valid assumption to make for these cells Really, the mitotic index only tells you what fraction of the cells are in mitosis, and it could be the same for a fast-growing cell population as a slow one. 

Scientists need to classify and organize complex data, such as that yielded by medical tests or analysis via GC-MS (gas chromatography-mass spectrometry). The data may be multifaceted and difficult to interpret, as different tests may conflict or yield inconclusive results. Growing cell structures may be used to assess medical data for example, such as that obtained from patient biopsies, and determine whether the test results are consistent with a diagnosis of breast cancer.1... 

Walker, A.K., Cross, S.S., and Harrison, R.F., Visualisation of biomedical datasets by use of growing cell structure networks A novel diagnostic classification technique, Lancet, 354, 1518,1999. 

A method exists that largely overcomes the problems of computational expense and uncertainty in the size of the map. This is the growing cell structure algorithm, which we explore in this chapter. 

Methods in which the geometry of the map adjusts as the algorithm runs are known as growing cell algorithms. Several growing cell methods exist they differ in the constraints imposed on the geometry of the map and the mechanism that is used to evolve it. 

Similar aims underlie the growing cell structure (GCS) approach, which relies on the use of lines, triangles, or, in more general terms, "dimensional hypertetrahedrons," (which, as we shall see, are far easier to use than the name suggests). 

The growing cell structure algorithm is a variant of a Kohonen network, so the GCS displays several similarities with the SOM. The most distinctive feature of the GCS is that the topology is self-adaptive, adjusting as the algorithm learns about classes in the data. So, unlike the SOM, in which the layout of nodes is regular and predefined, the GCS is not constrained in advance to a particular size of network or a certain lattice geometry. 

The building blocks of one-, two-, and three-dimensional growing cell structures. 

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