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Systems Biology Approach and Modeling for the Design of Microbial Cell Factories
Published in Kazuyuki Shimizu, Metabolic Regulation and Metabolic Engineering for Biofuel and Biochemical Production, 2017
Flux balance analysis (FBA) and its extension to genome-scale has made significant progress as it requires only basic knowledge of metabolic reaction stoichiometry, and has reasonably accurate predictability. Significant efforts have also been made to integrate gene level regulation and metabolic networks to reveal the regulation mechanism (Herrgard et al. 2006, O’Brien et al. 2013). In such approach, however, some appropriate objective functions such as the maximization of the cell growth rate, the specific substrate consumption rate, and/or the metabolite production rate must be introduced due to excess degrees of freedom. It was, however, shown that no single objective function can accurately represent the flux data for the different culture condition (Schuetz et al. 2007). Rather, a vector-valued objective function or multiple objective functions must be considered, resulting in Pareto optimal set to represent the metabolic fluxes (Schuetz et al. 2012), where the influential objective function may be the maximum ATP yield, maximum biomass yield, and minimum sum of absolute fluxes (which corresponds to minimum enzyme investment).
Metabolic modeling of synthetic microbial communities for bioremediation
Published in Critical Reviews in Environmental Science and Technology, 2023
Lvjing Wang, Xiaoyu Wang, Hao Wu, Haixia Wang, Yihan Wang, Zhenmei Lu
The generic process of GEM reconstruction can be summarized as follows (Figure 3) (Bernstein et al., 2021; Thiele & Palsson, 2010). (1) Genome annotation. Genome annotation information can be obtained from public knowledge bases, and sequenced genomes can be annotated by RAST (the Rapid Annotation using Subsystems Technology) (Overbeek et al., 2014) or Prokka (Seemann, 2014). (2) Draft reconstruction. Draft models can be built by automatic GEM reconstruction programs (Table 1). Nutrients are generally defined by medium and culture conditions, and secretions can be detected by mass spectrometry. (3) Biomass composition determination. Biomass objective functions can be generated from the literature or experimental data (Lachance et al., 2019; Simensen et al., 2022). (4) Network gap-filling. Gap-filling requires algorithms to identify dead-end metabolites and choked reactions and is based on universal databases and the available curated models that are phylogenetically closest to the organisms of interest. (5) Model evaluation. The accuracy of the model can be validated by comparing in vitro phenotype data and in silico prediction, and the quality of the model can be evaluated by using alternative model testing toolboxes (Jensen et al., 2020), such as MEMOTE, short for metabolic model testing (Lieven et al., 2020). (6) Flux simulation. When model reconstruction and refinement are finished, flux simulation is carried out (Table 2). For instance, the final model can be used to simulate whole-cell metabolic fluxes in a given environmental condition at steady state using flux balance analysis (FBA) (Orth et al., 2010).