Evolution and Genetic Engineering - Bioprocess Engineering: Kinetics, Biosystems, Sustainability and Reactor Design - page 736

In addition to the need for extra efforts in engineering design to ensure genetic stability,
regulatory approval may be more difficult. If a genetically engineered cell is to be used to
treat hazardous wastes, containment of the engineered organism is difficult or impossible.
Pump-and-treat scenarios for leachates allow the possibility of control. In situ use of such
organisms would have to satisfy the constraints for deliberate release.
In addition to these two problems, engineering pathways require good quantitative infor-
mation on the flow of metabolites in a cell. Detailed kinetic models of cells (Chapter 10) can be
used in conjunction with experiments to optimize the design of new pathways. Metabolic
engineering has been a fertile area for engineering contributions. Metabolic engineering
(and gene therapy) require a delicate balance of activities and are a problem of quantitative
optimization rather than simple maximization of expression of a gene.
A goal of the engineering approach has been to develop rational design techniques for
metabolic engineering. Due to the highly nonlinear, dynamic nature of a cell and its metab-
olism, uninformed changes intended to improve production of a specific compound often fail
to give the desired result. The problem of metabolic engineering is to express appropriate
genes in the “exact” amount needed. Too much of a key enzyme may result in little change,
if it is not a rate-influencing step, as there is insufficient reactant to increase the flow of
substrate (or flux ) into the reaction product. If it is rate-influencing, the increased reaction
of the substrate will alter fluxes of precursors in other pathways; sometimes these changes
compromise the cell's ability to grow or to provide co-reactants when needed.
The limitation of analytical and computational methods had been a hindrance in metabolic
engineering and kinetic modeling. Simplifications have been employed to circumvent the
computational and analytic demand. Metabolic control theory and the closely allied activity
of metabolic flux analysis are mathematical tools that can be applied to such problems. A
flux balance equation consists of the product of a stoichiometric matrix and an intracellular
flux vector to yield an overall rate vector. Typically such equations are underdetermined and
require assumptions (e.g. on energy stoichiometry) that may not be justified for a metaboli-
cally engineered cell. However, improved experimental techniques to measure the intracel-
lular flux vector (e.g. mass spectrometry) coupled with genomic/proteomic information
may provide the data to allow complete solution of the flux balance equations. One caution
is that analysis of pathways in isolation can be misleading. An assumption of such analysis is
that the products of the pathway do not influence inputs into that pathway. Given the
complexity of a cell, it is difficult to assure that such an assumption is satisfied.
Metabolic control theory is based on a sensitivity analysis to calculate the response of
a pathway to changes in the individual steps in a pathway. This approach allows calculations
of flux control coefficients, which are defined as the fractional change of flux expected for
a fractional change in the amount of each enzyme. This process involves a linearization, so
that flux coefficients can vary significantly if growth conditions and the cell's physiological
step change. An important result of the application of this theory to many cases is that it is
rare for a single enzyme step to be rate controlling. Typically several enzymatic steps influ-
ence rate. Maximization of flux through a particular pathway would require several enzyme
activities to be altered simultaneously. The potential of such analysis to contribute to rational
design of microbes is clear but not yet routinely applicable.
Real progress has been made toward the industrial use of metabolically engineered cells.
Processes to convert glucose from cornstarch into 1,3-propanediol, an important monomer in