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Using an Escherichia coli genome-scale model (iJO1366), we generated different strain designs for the anaerobic production of ethanol from glucose, thus demonstrating the tractability and potential utility of these modules in metabolic engineering algorithms.
Recently, model-based analysis of microorganisms has begun, for instance, to reveal functional modules in metabolic and transcriptional networks, to predict cellular behavior from genome-scale physicochemical constraints, and to suggest novel design principles for well-studied bacterial subsystems such as chemotaxis.
Guimerà and Amaral [ 21, 22] identify modules in metabolic networks by maximizing the network's modularity using simulated annealing.
After comparison, we adopted the method from Guimerà and Amaral [ 21, 22] to identify modules in metabolic networks in chloroplasts and photosynthetic bacteria (see detailed description in the "Methods" section).
For example [ 70] focused on the identification of conserved modules in metabolic pathways, and showed that many such modules exist, have a skewed size distribution, and may be hierarchically organized.
Many approaches have been used to detect modules in metabolic network including elementary modes, extreme pathways, flux analysis [ 15- 17], and graph clustering techniques such as Markov Clustering [MCL, 18], Iterative Conductance Cutting [ICC, 19], and Geometric Minimal Spanning Tree Clustering [GMC, 20].
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One example where the present work may be extended to is the case where rather than that one piece of DNA encodes three metabolic enzymes (as in Figure 8), one metabolic enzyme catalyses reactions in three metabolic modules in the metabolic network: a multifunctional enzyme.
We employ the ShReD metric to partition a large-scale reconstruction of human liver metabolism (HepatoNet1) [ 10] into modules enriched in metabolic cycles, and conduct an EFM analysis on each module at varying levels of partition hierarchy.
The notion of local regulation allows identification of functional elementary modules in the metabolic network.
Several studies have shown that the modules in a metabolic network have hierarchy [ 2].
AMBIENT identified 20 candidate up- and down-regulated active modules in the metabolic network, involving a total of 330 enzyme-encoding genes (out of 963 genes included in the model) that showed a co-ordinated response to nitrogen limitation (Table 1).
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