scholarly journals Omic data from evolved E. coli are consistent with computed optimal growth from genome‐scale models

2010 ◽  
Vol 6 (1) ◽  
pp. 390 ◽  
Author(s):  
Nathan E Lewis ◽  
Kim K Hixson ◽  
Tom M Conrad ◽  
Joshua A Lerman ◽  
Pep Charusanti ◽  
...  
Keyword(s):  
Optimal Growth ◽  
E Coli ◽  
Scale Models ◽  
Genome Scale ◽  
Omic Data

scholarly journals Computation of condition-dependent proteome allocation reveals variability in the macro and micro nutrient requirements for growth

2020 ◽  
Author(s):  
Colton J. Lloyd ◽  
Jonathan Monk ◽  
Laurence Yang ◽  
Ali Ebrahim ◽  
Bernhard O. Palsson
Keyword(s):  
Amino Acids ◽  
Growth Conditions ◽  
Anaerobic Growth ◽  
Metabolic Phenotype ◽  
E Coli ◽  
Scale Models ◽  
Protein Components ◽  
K 12 ◽  
Genome Scale ◽  
Prosthetic Groups

AbstractSustaining a robust metabolic network requires a balanced and fully functioning proteome. In addition to amino acids, many enzymes require cofactors (coenzymes and engrafted prosthetic groups) to function properly. Extensively validated genome-scale models of metabolism and gene expression (ME-models) have the unique ability to compute an optimal proteome composition underlying a metabolic phenotype, including the provision of all required cofactors. Here we use the ME-model for Escherichia coli K-12 MG1655 to computationally examine how environmental conditions change the proteome and its accompanying cofactor usage. We found that: (1) The cofactor requirements computed by the ME model mostly agree with the standard biomass objective function used in models of metabolism alone (M models); (2) ME-model computations reveal non-intuitive variability in cofactor use under different growth conditions; (3) An analysis of ME-model predicted protein use in aerobic and anaerobic conditions suggests an enrichment in the use of prebiotic amino acids in the proteins used to sustain anaerobic growth (4) The ME-model could describe how limitation in key protein components affect the metabolic state of E. coli. Genome-scale models have thus reached a level of sophistication where they reveal intricate properties of functional proteomes and how they support different E. coli lifestyles.

scholarly journals Quantifying the entropic cost of cellular growth control

2017 ◽  
Author(s):  
Daniele De Martino ◽  
Fabrizio Capuani ◽  
Andrea De Martino
Keyword(s):  
Growth Control ◽  
Growth Dynamics ◽  
Cellular Growth ◽  
Initial Size ◽  
E Coli ◽  
Regulatory Strategies ◽  
Control Growth ◽  
Metabolic States ◽  
Scale Models ◽  
Genome Scale

We quantify the amount of regulation required to control growth in living cells by a Maximum Entropy approach to the space of underlying metabolic states described by genome-scale models. Results obtained forE. coliand human cells are consistent with experiments and point to different regulatory strategies by which growth can be fostered or repressed. Moreover we explicitly connect the ‘inverse temperature’ that controls MaxEnt distributions to the growth dynamics, showing that the initial size of a colony may be crucial in determining how an exponentially growing population organizes the phenotypic space.

scholarly journals Genome scale models of yeast: towards standardized evaluation and consistent omic integration

2015 ◽  
Vol 7 (8) ◽  
pp. 846-858 ◽  
Author(s):  
Benjamín J. Sánchez ◽  
Jens Nielsen
Keyword(s):  
Scale Models ◽  
Genome Scale ◽  
Omic Data

We review genome scale models of yeast, how are they typically evaluated, and how can they be integrated with omic data.

scholarly journals Improving Designer Glycan Production inEscherichia colithrough Model-Guided Metabolic Engineering

2017 ◽  
Author(s):  
Joseph A. Wayman ◽  
Cameron Glasscock ◽  
Thomas J. Mansell ◽  
Matthew P. DeLisa ◽  
Jeffrey D. Varner
Keyword(s):  
Protein Modification ◽  
Gene Knockout ◽  
Optimal Growth ◽  
Fluorescent Labeling ◽  
Protein Glycosylation ◽  
Scale Model ◽  
E Coli ◽  
A Genome ◽  
Glycosylation Pathway ◽  
Genome Scale

AbstractAsparagine-linked (N-linked) glycosylation is the most common protein modification in eukaryotes, affecting over two-thirds of the proteome. Glycosylation is also critical to the pharmacokinetic activity and immunogenicity of many therapeutic proteins currently produced in complex eukaryotic hosts. The discovery of a protein glycosylation pathway in the pathogenCampylobacter jejuniand its subsequent transfer into laboratory strains ofEscherichia colihas spurred great interest in glycoprotein production in prokaryotes. However, prokaryotic glycoprotein production has several drawbacks, including insufficient availability of non-native glycan precursors. To address this limitation, we used a constraint-based model ofE. colimetabolism in combination with heuristic optimization to design gene knockout strains that overproduced glycan precursors. First, we incorporated reactions associated withC. jejuniglycan assembly into a genome-scale model ofE. colimetabolism. We then identified gene knockout strains that coupled optimal growth to glycan synthesis. Simulations suggested that these growth-coupled glycan overproducing strains had metabolic imbalances that rerouted flux toward glycan precursor synthesis. We then validated the model-identified knockout strains experimentally by measuring glycan expression using a flow cytometric-based assay involving fluorescent labeling of cell surface-displayed glycans. Overall, this study demonstrates the promising role that metabolic modeling can play in optimizing the performance of a next-generation microbial glycosylation platform.

scholarly journals Literature mining supports a next-generation modeling approach to predict cellular byproduct secretion

2016 ◽  
Author(s):  
Zachary A. King ◽  
Edward J. O’Brien ◽  
Adam M. Feist ◽  
Bernhard O. Palsson
Keyword(s):  
Cancer Biology ◽  
Metabolic Networks ◽  
Predictive Power ◽  
Literature Mining ◽  
Next Generation ◽  
E Coli ◽  
Wide Range ◽  
Scale Models ◽  
A Cell ◽  
Genome Scale

The metabolic byproducts secreted by growing cells can be easily measured and provide a window into the state of a cell; they have been essential to the development of microbiology1, cancer biology2, and biotechnology3. Progress in computational modeling of cells has made it possible to predict metabolic byproduct secretion with bottom-up reconstructions of metabolic networks. However, owing to a lack of data, it has not been possible to validate these predictions across a wide range of strains and conditions. Through literature mining, we were able to generate a database of Escherichia coli strains and their experimentally measured byproduct secretions. We simulated these strains in six historical genome-scale models of E. coli, and we report that the predictive power of the models has increased as they have expanded in size and scope. Next-generation models of metabolism and gene expression are even more capable than previous models, but parameterization poses new challenges.

scholarly journals COBRAme: A Computational Framework for Genome-Scale Models of Metabolism and Gene Expression

2017 ◽  
Author(s):  
Colton J. Lloyd ◽  
Ali Ebrahim ◽  
Laurence Yang ◽  
Zachary King ◽  
Edward Catoiu ◽  
...  
Keyword(s):  
Marked Improvement ◽  
Thermotoga Maritima ◽  
Cellular Growth ◽  
Software Framework ◽  
Computational Framework ◽  
E Coli ◽  
Growing Cell ◽  
Scale Models ◽  
Model Size ◽  
Genome Scale

AbstractGenome-scale models of metabolism and macromolecular expression (ME-models) explicitly compute the optimal proteome composition of a growing cell. ME-models expand upon the well-established genome-scale models of metabolism (M-models), and they enable new and exciting insights that are fundamental to understanding the basis of cellular growth. ME-models have increased predictive capabilities and accuracy due to their inclusion of the biosynthetic costs for the machinery of life, but they come with a significant increase in model size and complexity. This challenge results in models which are both difficult to compute and challenging to understand conceptually. As a result, ME-models exist for only two organisms (Escherichia coli and Thermotoga maritima) and are still used by relatively few researchers. To address these challenges, we have developed a new software framework called COBRAme for building and simulating ME-models. It is coded in Python and built on COBRApy, a popular platform for using M-models. COBRAme streamlines computation and analysis of ME-models. It provides tools to simplify constructing and editing ME-models to enable ME-model reconstructions for new organisms. We used COBRAme to reconstruct a condensed E. coli ME-model called iJL1678b-ME. This reformulated model gives virtually identical solutions to previous E. coli ME-models while using ¼ the number of free variables and solving in less than 10 minutes, a marked improvement over the 6 hour solve time of previous ME-model formulations. This manuscript outlines the architecture of COBRAme and demonstrates how ME-models can be reconstructed and edited most efficiently using the software.

scholarly journals EXPLORATION OF BROTH CHICKEN GUT AS GROWTH MEDIA OF Escherichia coli BL21 pET-Endo FOR ENDO-Β-1,4-D-XYLANASE PRODUCTION

2017 ◽  
Vol 13 (2) ◽  
pp. 191
Author(s):  
Anak Agung Istri Ratnadewi ◽  
Moch. Yoris Alidion ◽  
Agung Budi Santoso ◽  
Ika Oktavianawatia
Keyword(s):  
Large Scale ◽  
Incubation Temperature ◽  
Specific Activity ◽  
Hydrolytic Enzyme ◽  
Optimal Growth ◽  
Good Alternative ◽  
Growth Media ◽  
Gene Encoding ◽  
Xylanase Production ◽  
E Coli

<p>Endo-β-1,4-D-xylanase is a hydrolytic enzyme that breakdown the 1.4 chain of xylan polysaccharide. We have succes to transform the plasmid pET-Endo gene encoding endo-1,4-β-D-xylanase from Bacillus sp. originally from termites abdominal to E. coli BL21. The clone was ready for large scale of enzyme production. To reduce production cost, we look for subtitute media for the expensive Luria Berthani broth. Chicken guts broth is good alternative while rich of protein and very cheap. The content of N dissolved chicken guts broth reaches 87 % of LB broth. Growth of E. Coli BL21 in Chicken guts broth and LB broth (as control) was observed by Optical Density (OD) using spectrofotometer. Concentration of glucose added in broth and incubation temperature was varied. The result showed that optimal growth was in addition of 1.5 % glucose and incubated at  37 <sup>o</sup>C for 16 h. This optimal condition was used to grow E. coli BL21 pET-Endo for xylanase production. Enzyme purification was done by Ni-NTA affinity chromatography. Highest protein yield was 0.076 mg/mL obtained in 100 mM imidazole elucidation. The activity and specific activity of xylanase were estimated as 0.042 U/mL and 0.556 U/µg, respectively. The purification factor was 3.16 time and the molecular weight of enzyme was ± 30, 000 Dalton</p>

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