Systems Biotechnology: Combined in Silico and Omics Analyses for the Improvement of Microorganisms for Industrial Applications - Systems Biology: Networks, Models, and Applications - page 203

Once the kinetic model is developed, model parameters p in reaction j ,

v j f ( c , p ), are adjusted by best fitting to in vitro kinetic experiments.

Obtaining such experimental data is, however, still a laborious task.
In many cases, the kinetic information on the reactions of interest can
be collected from the published works and/or the available public
databases [42,43]. However, one may fail to obtain reliable solutions
because the parameters collected from different sources can cause
system inconsistency. Thus, it is highly desirable to establish an effec-
tive method in the form of an easily usable software package for
facilitating the kinetic modeling and parameter estimation. Currently,
a variety of software tools and computational environments for such
dynamic simulation and/or parameter estimation are available; they
include GEPASI [44], DBsolve [45], E-Cell [46], SCAMP [47], Virtual
Cell [48], StochSim [49], STOCKS [50], Dynetica [51], GENESIS [43],
and many others (http://sbml.org/) .

=

Constraints-Based Flux Analysis
Although the kinetic model-based dynamic modeling and simulations
are most appropriate for fully characterizing the metabolic reaction
systems, determining a large number of kinetic parameters is not an
easy task. Moreover, the values of many of these parameters cannot be
trusted since the reaction mechanisms and parameters in these models
are derived from in vitro rather than in vivo measurements [52].
The stationary modeling approach is therefore a good alternative to
the kinetic model for the simulation of a large metabolic reaction
network. Assuming the pseudo-steady state, one can simplify the kinetic
model into static representation. Unlike the dynamic approaches,
the stationary model only considers the network's connectivity and
capacity as time-invariant properties of the metabolic system.
The stationary approaches include stoichiometric analysis, structural
or topological pathway analysis, and constraints-based flux analysis
[53-58]. Of these, constraints-based flux analysis, also known as “meta-
bolic flux analysis” (MFA), and more specifically flux balance analysis
(FBA), is the most widely adopted method since it requires the least
amount of information to quantify the fluxes and analyze the metabolic
system [59]. Basically, under the pseudo-steady-state assumption,
eq. (1) can be simplified as follows:

Sv = b (2)

This matrix form can be rewritten in balance equations for I metabolites
given a series of J candidate metabolic reactions:

Sv = b , ∀∈ i I

∑

(3)

ij j i

jJ ∈