Biomedical Implications of the Porosity of Microbial Biofilms - Porous Media: Applications in Biological Systems and Biotechnology - page 140

Recognizing the importance of quantifying biofilm structure, ISA was used
to calculate a series of parameters, for example, textural entropy, homogeneity,
energy, areal porosity, average horizontal and vertical run lengths, diffusion
distance, and fractal dimension obtained from digital biofilm images (Yang
et al. 2000). To make the results of the image analysis less dependent on
the operator, an automatic thresholding algorithm was developed and inte-
grated with ISA (Yang et al. 2001). The performance of ISA was tested by
several researchers. Lewandowski and colleagues (Lewandowski et al. 1999)
used ISA to monitor temporal variations in the structure of biofilms com-
posed of P. aeruginosa , P. fluorescens , and K. pneumoniae and concluded
that some of the structural parameters may reach stable values. Lewandowski
(Lewandowski 2000) used ISA to calculate areal porosities of layered biofilms
and developed a method for calculating volumetric biofilm porosity. Beyenal
and Lewandowski (Beyenal and Lewandowski 2001) used ISA to quantify the
areal porosity and fractal dimension of mixed population bacterial biofilms
comprising Desulfovibrio desulfuricans and P. fluorescens and concluded that
the extent of biofilm heterogeneity was directly correlated with the flux of
H 2 S from the cell clusters. Purevdorj and colleagues (Purevdorj et al. 2002)
used ISA to quantify the areal porosity, fractal dimension, average horizontal
run length, average diffusion distance, textural entropy and energy of biofilms
of wild-type P. aeruginosa PAO1, and cell-cell signaling using lasI mutant
PAO1-JP1 under laminar and turbulent flows. They concluded that both cell
signaling and hydrodynamics influenced biofilm structure. Recently, Beyenal
and colleagues (Beyenal et al. 2004a) presented a three-dimensional version
of the ISA software, which calculated three-dimensional parameters charac-
terizing biofilm structure in terms of heterogeneity, size, and morphology of
biomass.
Over the past few years, modeling of heterogeneous biofilms gained much
attention. The biofilm's inner space is referred to as porous media: porosity,
diffusion, and permeability are hence the parameters of choice for attempts
to quantify the extent of biofilm structural heterogeneity. For example, cells
that are located near the biofilm-bulk-fluid interface will be considered to be
provided with both substrate and oxygen. Deeper in the biofilm, there could be
a region in which both oxygen and substrate have been depleted; in this zone,
the cells might become resting cells or dead cells (Stewart and Franklin 2008).
Older biofilm models, like AQUASIM, which accept heterogeneous structure
of biofilms, required porosity as an input parameter (Wanner and Reichert
1996; Horn and Hempel 1997). A newer approach to biofilm modeling, cellular
automaton (Wimpenny and Colasanti 1997; Picioreanu et al. 1998) allows
prediction of biofilm porosity from first principles.
In general, there are two different approaches used in modeling of biofilms.
The more prevalent approach is to consider biofilm as a continuous layer.
Another approach is based on considering it as patchy aggregates that
accumulate in pore throats. For cases with sucient nutrients, continuous
layer assumption matches better with reality. For the low-load cases, it is