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Flux Balance Analysis (FBA) is a constraint-based method that is commonly used to guide metabolites through restricting pathways that often involve conditions such as anaplerotic cycles like Calvin, reversible or irreversible reactions, and nodes where metabolic pathways branch. The method can identify the best conditions for one course but fails when dealing with the pathways of multiple metabolites of interest. Recent studies on metabolism consider it more natural to optimize several metabolites simultaneously rather than just one; moreover, they point out the use of metaheuristics as an attractive alternative that extends FBA to tackle multiple objectives. However, the literature also warns that the use of such techniques must not be wild. Instead, it must be subject to careful fine-tuning and selection processes to achieve the desired results. This work analyses the impact on the quality of the pathways built using the NSGAII and MOEA/D algorithms and several novel optimization models; it conducts a study on two case studies, the pigment biosynthesis and the node in glutamate metabolism of the microalgae Chlorella vulgaris, under three culture conditions (autotrophic, heterotrophic, and mixotrophic) while optimizing for three metabolic intermediaries as independent objective functions simultaneously. The results show varying performances between NSGAII and MOEA/D, demonstrating that the selection of an optimization model can greatly affect predicted phenotypes. 

Members of the genusMethylacidiphilumare thermoacidophile methanotrophs with optimal growth temperatures between 50°C and 60°C, and pH between 1.0 and 3.0. These microorganisms, as well as other extremophile bacteria, offer an attractive platform for environmental and industrial biotechnology because of their robust operating conditions and capacity to grow using low-cost substrates. In this study, we isolatedMethylacidiphilum fumariolicumstr. Pic from a crater lake located in the state of Chiapas, Mexico. We sequenced the genome and built a genome-scale metabolic model. The manually curated model contains 667 metabolites, 729 reactions, and 473 genes. Predicted flux distributions using flux balance analysis identified changes in redox trade-offs under methanotrophic and autotrophic conditions (H₂+CO₂). This was also predicted under heterotrophic conditions (acetone, isopropanol, and propane). Model validation was performed by testing the capacity of the strains to grow using four substrates: CH₄, acetone, isopropanol, and LP-Gas. The results suggest that the metabolism ofM. fumariolicumstr. Pic is limited by the regeneration of redox equivalents such as NAD(P)H and reduced cytochromes. 

Constraint-based metabolic modeling approaches have enhanced our knowledge and understanding of the metabolism of prokaryotes and eukaryotes. This approach highly depends on the reconstruction process of genome-scale metabolic models (Mmodels). M-models can guide effective experimental design and yield new insights into the function and control of biological systems. Despite the recent advances in the automated generation of draft metabolic network reconstructions, the manual curation of these networks remains a labor-intensive and challenging task. Thus, these ten quick tips for the manual curation process are essential for optimizing high-quality metabolic model generation in less time. This collection of tips describes in great detail the resources and methods to ensure successful reconstruction. Furthermore, it increases the scope of other protocols of metabolic modeling by including resources to reconstruct eukaryotic organisms. Thus, all tips are applicable to a wide range of eukaryotic organisms. We believe this manuscript will interest a broad audience and researchers from different disciplines, spanning from microbiology and systems biology to biotechnology.