Modeling the Microbial Cells for Biotechnological Applications

Computational Aspects In Metabolic Engineering

Synthetic biology and metabolic engineering are interdependent disciplines that collaborate to thrive and attain their distinct objectives. While synthetic biology provides the components and insights about diverse biological processes, metabolic engineering employs this knowledge to optimize the path of biological synthesis for a specific desired compound (García-Granados et al., 2019).

Metabolic engineering aims to maximize cellular processes that belong to a particular organism in order to synthesize the molecule of interest from a substrate that is preferably inexpensive and simple. Genetic engineering tools are used in metabolic engineering studies to remove genes from the organism that hinder synthesis as well as to insert genes of partial or whole metabolic pathways for the production of a certain substance. Additionally, these tools enable the introduction of point mutations that alter the metabolic flux by lowering the activity or expression of native proteins. But to make all of these modifications, specific knowledge about the enzymes involved in the reactions, the metabolic pathways that include these reactions, and details about the organism where the modifications will be made are required; for this reason, organisms' genomes have been sequenced, proteins have been characterized, and metabolic studies have provided a wealth of extremely useful tools and information (Caspi et al., 2013). In recent years, especially with the introduction of the CRISPR/Cas9 techniques to the community, genetic manipulations and interventions are quite simple. Technological advances in sequencing technologies have enabled more information about the genomes of both known and newly discovered organisms. Additionally, the catalog of enzymes and biochemical reactions has grown, and specific biological phenomena (such as viruses or phage infections) have been characterized (Goodwin et al., 2016; Liu et al., 2015). New technologies in computational techniques have been developed rapidly. All these achievements gain new perspectives on metabolic engineering.

Computational techniques in metabolic engineering have a promising key role to improve and design new strains and increase the productivity of organisms. Available information in different databases which are related to the biological materials of the organisms is used to evaluate and characterize the metabolic models of the organisms and then these metabolic models are improved or redesigned to have strains with higher production yields (Cameron et al., 2014; Woolston et al., 2013). These models help to understand the behavior of the system, the prediction of the product yield, and the choice of process conditions easier. Several uses of mathematical models and computational simulations are provided, including, but not limited to, analyzing gene and reaction knockouts, proposing possible genetic interventions for an overproducer of a metabolite, comparing inputs and outputs, iterative improvement, gap-filling strategies, discovering regulatory interactions, quantifying predictable phenotypes from optimality principles, and designing environmental and genetic perturbations (Greenberg et al., 2011). The use of mathematical models can be effective for comprehending biological systems, and computational simulations can provide a clearer understanding of the processes, changes, and predictions. With all this progress and knowledge, scientists will be able to develop microorganisms as little factories that enable the production of chemical compounds at greater yields/rates (Jouhten, 2012).

Among different mathematical models, genome-scale metabolic models have proven to be an effective tool in systems biology during the past few decades with their applications in several fields. Genome-scale metabolic models, for instance, can be used to design drug targets for significant pathogens, predict metabolic interactions in microbial communities, or predict cellular behavior under various biological conditions. They have also been used to study evolutionary processes or provide a justification for laboratory experiments (Scossa et al., 2020).