How Cells Grow - Bioprocess Engineering: Kinetics, Biosystems, Sustainability and Reactor Design - page 596

the diauxic phenomenon for sequential use of glucose and lactose in Chapter 10. That exper-
imental observation led us to an understanding of regulation of the lac operon and catabolite
repression. This metabolic regulation was necessary for the transition from one primary
pathway to another. You might infer that the culture had as its objective function the maxi-
mization of its growth rate.
One approach to modeling growth on multiple substrates is cybernetic. Cybernetic means
that a process is goal seeking (e.g. maximization of growth rate when substrate level is high).
While this approach was initially motivated by a desire to predict the response of a microbial
culture to growth on a set of substitutable carbon sources, it has been expanded to provide an
alternative method of identifying the regulatory structure of a complex biochemical reaction
network (such as cellular metabolism) in a simple manner. Typically a single objective, such
as maximum growth rate, is chosen and an objective-oriented mathematical analysis is
employed. This analysis is similar to many economic analyses for resource distribution. For
many practical situations, this approach describes satisfactorily growth of a culture on
a complex medium. However, the potential power of this approach is in metabolic engineering
and in relating information on DNA sequences in an organism to physiologic function.
This approach has limitations, as the objective function for any organism is maximizing its
long-term survival as a species. Maximization of growth rate or of growth yield is really sub-
objectives which can dominate under some environmental conditions for example substrate
level is high and the population is sparse; these conditions are often of great interest to the
bioprocess engineer. Consequently, the cybernetic approach is often a valuable tool. A gener-
ation of this approach leads to our discussion in the next section.
11.14.6. Computational Systems Biology
As a progression of the kinetic models presented in the previous sections, we are moving to
the next level: system-level understanding of cellular cultures. As the need for a complete quan-
titative description (holism) in biology is recognized, the understanding develops that living
systems cannot be understood by studying just individual parts. Computational systems
biology aims at a system-level understanding by analyzing biological data using computational
techniques. The fast progress of genome sequencing inmultiple fronts andmassive amounts of
data generated by high-throughput experiments in DNAmicroarrays, proteomics, and metab-
olomics advances have lead to a leap in biological systemmodeling. The approaches outlined so
far are approximations of cellular systems at various degrees of simplifications. A system-wide
understanding will help in simplifying systems at different input and/or environmental condi-
tions and provide guidance to altering cellular systems for a desired goal.
11.1 5. PERFORMANCE ANALYSIS OF BATCH CULT URE
Cultivation of cells is commonly performed in batch mode. Bioreactions are commonly
applied in the production of drugs, food, and waste treatment. Apart from waste treatment,
the product value is usually high and the prime directive is thus quality control. Bioreactors
in pharmaceuticals and food industries are commonly operated in batch mode, eliminating
the potential for cross-contaminations and loss of productivity due to the loss of desired cells.