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In summary, we provide a new integrative method for mapping condition-dependent regulation in cells through the integrated analysis of multi-omics datasets and genome-scale metabolic models given previous knowledge (e.g., known network topology or results from the analysis of other omics datasets such as transcription factor interactions).
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The resulting metabolic model is given by: (5) where we have added to each equation an out-flow term which is the product of the dilution rate with the concentration of the corresponding metabolite because we have a constant out-flow of both extracellular and intracellular (as a result of cell out-flow) products through the chemostat.
Given a completely annotated genome, the challenges in metabolic modeling have been application of appropriate constraints that represent cellular physiology and, thereafter, solving the resulting set of equations.
Using the DLW method, the one- and two-pool models gave metabolic rates of 16.61±2.13 (n = 4) and 16.16±2.10 kJ h−1 (n = 4), respectively, during the 24-h measurement period (Table 1).
We relied on the CLP model given that it closely mimics many aspects of the inflammatory, hemodynamic and metabolic responses observed in human sepsis originating from the abdominal compartment [1,21].
Based on a given metabolic model different computational tools for the simulation, data evaluation, systems analysis, prediction, design and optimization of metabolic systems have been developed.
The root of this tree is the Project datatype that keeps all objects related to a given metabolic model and the analysis performed with it.
Given these experimental hurdles, we argue that metabolic models derived from bioinformatic analyses offer an especially attractive starting point for unraveling the interesting physiology of A. ferrooxidans.
Specifically, this gives rise to the applicability of the PBN framework for metabolic models.
Genome-scale metabolic models enable global analysis of microbial metabolism by considering all metabolic reactions simultaneously.
Given this information, it seems straightforward to derive an algorithm for generating viable metabolic models.
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