Biomedical Engineering Reference
In-Depth Information
defined topology and has strong mathematical foundations in graph theory and
convex set theory. In stoichiometric network analysis (SNA), the enzyme kinet-
ics, rate constants, activators, or inhibitors are not important. The only input in
the matrix is the number of substances participating in a given reaction and the
number of reactions in the system.
Advantages
: SNA does not depend on the
nature of the reaction, that is, can be deterministic or stochastic, discrete or con-
tinuous, has direct applications in metabolic flux analysis, allows ease in genera-
tion of a matrix, and the balance equations for substance concentrations can be
written precisely.
Limitations
: cannot represent the evolutionary growth of a
network or its regulatory properties, leading to limited predictive power. Energy
metabolites must be balanced and bidirectional steps are sometimes hard to re-
solve. Furthermore, distinguishing parallel pathways from the stoichiometric
matrix is very challenging.
2.
Metabolic flux analysis
(MFA) is one of the most important tools in
metabolic engineering. MFA makes use of experimental data to study flux dis-
tribution in a system. A complete dynamic description of metabolic pathways
requires detailed kinetic and regulatory information, which is rarely available
from experimental data. An alternative approach is to abstract the dynamic na-
ture of the pathways as a (more easily manageable) steady-state system, i.e.,
instead of guessing kinetic parameters. Systemic properties are determined
mathematically under a quasi-steady state assumption. A very useful concept in
MFA is the presence of elementary modes. Elementary modes are the simplest
flux distribution reaction steps that cannot be further decomposed (28,29). They
represent idealized situations and do not incorporate regulatory feedback loops.
The number of elementary modes may indicate the number of alternative routes
available to accomplish a certain biochemical task. Whether some or all are ac-
tually used by the cells can only be determined under stringent experimental
conditions. In addition, with an increase in the number of reaction steps, the
number of elementary modes also increases, leading to a combinatorial explo-
sion. This problem can be partly overcome by identifying and grouping isoen-
zymes. Despite this limitation, elementary flux analysis provides a strong
mathematical tool to satisfy both stoichiometry and thermodynamic require-
ments. MFA concepts were recently used to predict gene knockout phenotypes
in
E. coli
(40). MPA (metabolic pathway analysis), an offshoot of MFA, investi-
gates entire flux distributions, whereas MFA focuses on unitary flux distribu-
tions.
3.
Metabolic control analysis
(MCA) is a theoretical approach used to ana-
lyze the relative control of fluxes and intermediate concentrations of metabo-
lites. In contrast to the traditional single rate-limiting step concept, the current
belief centers around distributed control of enzymes driving the reactions. MCA
has a strong theoretical basis and is easy to understand. However, it is quite
challenging to implement and test MCA concepts experimentally. The possibil-
ity of modifying flux using MCA principles through specific biochemical path-
