2.1 An overview of CBM main concepts
Biological systems such as cellular metabolism are constrained by
physiochemical laws, genetics, and the extracellular environment
[34]. The most fundamental constraints of metabolism are the mass
balance equations for each intracellular metabolite generated from
biochemical reaction stoichiometry. Genome-scale network reconstructions
are created from all known metabolic reactions within the system of
interest. Furthermore, they are improved by additional information, such
as gene-protein-reaction (GPR) associations [35]. A valuable manual
protocol describes how to generate a high-quality genome-scale metabolic
reconstruction from genome sequencing data and how to curate the model
with empirical information [12]. A genome-scale network
reconstruction can be transformed into a mathematical format. The
mathematical representation of such reconstructed networks and
implementing further details such as GPR associations is called the
genome-scale metabolic model (GEM). This enables the quantitative and
qualitative analysis of the GEMs via computational approaches such as
constraint-based modeling [36].
Metabolic flux analysis (MFA) and Flux balance analysis (FBA) are two
main CBM methods that aim to determine the reaction fluxes (fluxomics)
within the metabolic network (Figure 1 ). These methods use a
stoichiometric matrix (S) with the size of m * n to calculate the
metabolic flux distribution. In the S matrix, each row represents a
metabolite (m), and each column represents a metabolic reaction (n).
Therefore, under the steady-state condition, the mass balance equation
will be as follows: S. v=0. The v vector contains metabolic fluxes, some
of which are known and some unknown. MFA is a data-driven method that
determines reaction fluxes through experimental measurements. While MFA
is useful in small-scale networks, FBA is a beneficial tool for
analyzing large-scale networks such as the genome-scale metabolic
network [37]. FBA is an optimization method that searches a solution
space and maximizes one or more objective functions such as maximum
growth rate and metabolite production via a linear programming approach
[11]. FBA calculates the single optimal flux distribution or
multiple optimal flux distributions in the GEM, which represents the
‘state’ of the metabolic network that relates to the physiological
function generated from the network [38]. However, mass balance
constraints alone cannot constitute a unique solution space. Therefore,
multiple optimal solutions (i.e., flux vectors) to the problem are
obtained. So, additional constraints such as flux capacity,
thermodynamic feasibility, gene expression, etc., are imposed to shrink
the solution space [39]. Moreover, MFA can combine with FBA to
determine internal metabolic fluxes to increase the prediction power
[40]. Besides, other forms of FBA and MFA, such as dynamic FBA and
MFA, can be used based on the aim of the research [41, 42]. In
addition to FBA and MFA, other CBM approaches can be used to rational
strain designs and increase product yield. These FBA-based methods aim
to determine gene deletion/addition targets, up/down regulations, data
integration, and suggest appropriate strategies to increase productivity
[43]. These computational methods also can be used in fermentation
optimization. For example, up- and down-regulation targets have been
used to identify enzyme activators and inhibitors for enhancing the
production bound in a regulatory-defined medium (RDM) [44]. COBRA
toolbox in MATLAB and COBRApy in python are two platforms for
implementing FBA and other related algorithms to GEMs [45, 46].
Another efficient approach to increase the predictive power of the CBM
models is the integration of omics data with GEMs. Omics data can be
used both to narrow the solution space in the FBA and as a tool to
evaluate and validate the model prediction [6]. As a result of
integrating omics data with GEMs, context-specific models are created
that provide the basis for studying metabolism under different
conditions [47, 48]. Assuming that the system is steady-state,
substrate concentrations, time, and various kinetic parameters are not
taken into account to calculate the metabolic fluxes. Therefore, the
predictive accuracy of the CBMs is less than the calculated fluxes
resulting from solving ODEs in the kinetic models. As the solution space
becomes tighter, the FBA solutions approach the kinetic model solution.
Thus, the integration of omics data can overcome the limitations of the
CBM over the kinetic models. However, data integration remains a major
challenge, and existing methods do not perform at the expected level
[49].