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1.1 Constraint-based Modeling Approaches

Organisms abide by the fundamental of evolution, whereby the fittest organism have more chances to survive
than the less fit organism. In essence, a particular environment has specific characteristics, for instance, scarce
resources, oxygen availability, and substrates presence. To be chosen for the next survival, the organisms must
satisfy these constraints, thus limits the phenotypes. Therefore, an approach known as constraint-based
modelling (CBM) has been developed. CBM is an approach to investigate the optimality of an organism by
predicting and describing the metabolic phenotypes (Klamt et al., 2018). The feasible flux distributions space
is created by constraining the systems, whereby only certain phenotypes are allowed to exist.
The unconstrained steady-state solution space is underdetermined due to the ratio of reactions typically
exceeding the number of metabolites; thus a linear equation provides hyperplane that defines the allowable flux
distributions. As a conclusion, the aim of constraint-based modelling (CBM) is to describe and predict the
desired phenotypes of an organism by describing the metabolic networks of an organism using the
stoichiometric framework and a series of constraints. Despite the imposition of constraints and steady-state
assumption, the solutions generated are not limited to a single solution. Rather, the solutions generated are
limited to the desired phenotypes.
Generally, there are three well-known approaches under constraint-based modelling methods - flux balance
analysis (FBA), minimization of metabolic adjustment (MoMA), and regulatory on/off minimization (ROOM).
Table 1 portrays the characteristics, advantages, and disadvantages of each constraint-based approaches as well
as the applications that have been done.
Table 1. Summary of Constraint-based Approaches

Name Characteristic(s) Advantage(s) Disadvantage(s) Reference(s)
- Measure the optimal - Enable analysis for large - Under certain - (Stalidzans et al.,
flux value of the desired systems. medium/environmental conditions, 2018)
objective function. - Suitable for linear and non- the effects of regulatory constraints - (Budinich et al.,
- Linear programming. linear objective functions. are not accounted. 2017)
FBA - Presence of multiple optima.
- Able to predict lethality of a
gene. - Not able to redesign the metabolic
network.
Compare the steady- - Correctly predict the transient - Only suitable for the new curate - (Maia, Rocha and
state fluxes after genetic metabolic states. model that is not exposed to long- Rocha 2016)
perturbation between term evolutionary pressure.
MoMA mutant and wild type. - The measured optimal flux is not a
- Quadratic growth coupled.
programming.

Minimize the number of - The predicted fluxes are - Complex due to using binary - (Tomar and De,
significant flux changes nearer to the experimental data. variables in the objective function. 2013)
between mutant and - Favor for flux distributions - Able to find alternative shortest
wild type.
ROOM that having high growth rates. pathways, but these pathways are
- MILP - Able to predict lethality of a never being evolutionarily found by
gene. the organism.

2. Comparison of Constraint-based Approaches

In this study, FBA, MoMA and ROOM have been compared with E.coli model for optimizing the production
of succinic acid. Previously, a total of 8 knocked out reactions resulted in higher performance in hybrid

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Artificial Intelligence in the 4th Industrial Revolution