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Machine Learning in Metabolic Engineering
Published in Shampa Sen, Leonid Datta, Sayak Mitra, Machine Learning and IoT, 2018
13CFLUX-2 (Weitzel et al. 2013) is used to plan and analyze 13C-labeling experiments to determine in vivo metabolic fluxes. It uses a modified XML language, called FluxML. However, 13CFLUX-2 requires that several steps be carried out manually. 13C-labeling-based metabolic flux analysis can be automated and standardized using Flux-P (Ebert et al. 2012), which is based on the Bio-jETI workflow framework. Isotopomer Network Compartmental Analysis (INCA) (Young 2014) is the first software able to perform 13C metabolic flux analysis under both steady-state and nonstationary (transient labeling experiments) conditions, thus expanding the scope of metabolic engineering to photosynthetic organisms and mammalian cultures.
Biothermodynamics
Published in Marc J. Assael, Geoffrey C. Maitland, Thomas Maskow, Urs von Stockar, William A. Wakeham, Stefan Will, Commonly Asked Questions in Thermodynamics, 2022
Marc J. Assael, Geoffrey C. Maitland, Thomas Maskow, Urs von Stockar, William A. Wakeham, Stefan Will
One of the core tools of systems biology is metabolic flux analysis. This consists of the construction of a model of the whole cellular network based on a knowledge of the important enzymes present in the cell from genomics (studying an organism's complete set of DNA) and proteomics (considering the complete set of proteins) as well as of the important metabolites from metabolomics (analyzing the chemical processes of organisms involving small molecules, intermediates and products of metabolism). For each metabolite with the concentration cj, a molar balance is formulated, as shown in the following equation (see also Question 1.4.3.) ν1,1r1+ν1,2r2+ν1,3r3+⋯=dc1dtν2,1r1+ν1,2r2+ν1,3r3+⋯=dc2dtν3,1r1+ν1,2r2+ν1,3r3+⋯=dc3dt⋮⋮⋮+⋯=⋮.
Modeling of bioethanol production through glucose fermentation using Saccharomyces cerevisiae immobilized on sodium alginate beads
Published in Cogent Engineering, 2022
Astrilia Damayanti, Zuhriyan Ash Shiddieqy Bahlawan, Andri Cahyo Kumoro
Metabolic flux analysis (MFA) is a powerful analytical method employing a rigorous optimization procedure to measure the quantity of the intracellular metabolic fluxes resulted from all recognized catalytic and transcriptional interactions. Although the MFA is basically performed based on the stoichiometry of the metabolic reactions and the mass balances of the intracellular metabolites, which are assumed to occur under pseudo-steady state conditions, two methods are generally used to study the metabolic flux presents in a biological system, namely the C-based flux analysis and constraint-based flux analysis (S. Y. Lee et al., 2011). In this work, the MFA was used to determine the main metabolic pathway based on an optimal criterion (maximum growth) using the stoichiometric constraint and the following general assumptions: